ISA Transactions

This paper presents a novel contribution of a low complexity control scheme for voltage control of a dynamic voltage restorer (DVR). The scheme proposed utilizes an error-driven proportional-integral-derivative (PID) controller to guarantee better power quality performance in terms of voltage enhancement and stabilization of the buses, energy efficient utilization, and harmonic distortion reduction in a distribution network. This method maintains the load voltage close to or equal to the nominal value in terms of various voltage disturbances such as balanced and unbalanced sag/swell, voltage imbalance, notching, different fault conditions as well as power system harmonic distortion. A grasshopper optimization algorithm (GOA) is used to tune the gain values of the PID controller. In order to validate the effectiveness of the proposed DVR controller, first, a fractional order PID controller was presented and compared with the proposed one. Further, a comparative performance evaluation of four optimization techniques, namely Cuckoo search (CSA), GOA, Flower pollination (FBA), and Grey wolf optimizer (GWO), is presented to compare between the PID and FOPID performance in terms of fault conditions in order to achieve a global minimum error and fast dynamic response of the proposed controller. Second, a comparative analysis of simulation results obtained using the proposed controller and those obtained using an active disturbance rejection controller (ADRC) is presented, and it was found that the performance of the optimal PID is better than the performance of the conventional ADRC. Finally, the effectiveness of the presented DVR with the controller proposed has been assessed by time-domain simulations in the MATLAB/Simulink platform.

Practical requirements on the design of control systems, especially process control systems, are usually specified in terms of time-domain response, such as overshoot and rise time, or frequency-domain response, such as resonance peak and stability margin. Although numerous methods have been developed for the design of the proportional-integralderivative ͑PID͒ controller, little work has been done in relation to the quantitative time-domain and frequency-domain responses. In this paper, we study the following problem: Given a nominal stable process with time delay, we design a suboptimal PID controller to achieve the required time-domain response or frequency-domain response for the nominal system or the uncertain system. An H ϱ PID controller is developed based on optimal control theory and the parameters are derived analytically. Its properties are investigated and compared with that of two developed suboptimal controllers: an H 2 PID controller and a Maclaurin PID controller. It is shown that all three controllers can provide the quantitative time-domain and frequency-domain responses.

PLC, the core element of modern automation systems, due to serial execution, exhibits limitations like slow speed and poor scan time. Improved PLC design using FPGA has been proposed based on parallel execution mechanism for enhancement of performance and flexibility. Modelsim as simulation platform and VHDL used to translate, integrate and implement the logic circuit in FPGA. Xilinx's Spartan kit for implementation-testing and VB has been used for GUI development. Salient merits of the design include cost-effectiveness, miniaturization, user-friendliness, simplicity, along with lower power consumption, smaller scan time and higher speed. Various functionalities and applications like typical PLC and industrial alarm annunciator have been developed and successfully tested. Results of simulation, design and implementation have been reported.

A thyristor driven pump is operated by varying the DC input signal in the firing circuit of thyristor drive. This operation suffers from difficulties due to the nonlinear relation between thyristor output and DC input. In the present paper, an opto-isolator based linearization technique of a typical thyristor driven pump has been proposed. The design, fabrication and the necessary circuit diagram along with theoretical explanations of the resultant output has been described. The operation of the linearized thyristor driven pump has been studied experimentally and the experimental data before and after linearization are reported. The characteristic graphs are found to have very good linearity.

Many investigations have been done on tuning proportional-integral-derivative (PID) controllers in single-input single-output (SISO) systems. However, only a few investigations have been carried out on tuning PID controllers in cascade control systems. In this paper, a new approach, namely the use of a Smith predictor in the outer loop of a cascade control system, is investigated. The method can be used in temperature control problems where the secondary part of the process (the inner loop) may have a negligible delay while the primary loop (the outer loop) has a time-delay. Two different approaches, including an autotuning method, to ®nd the controller parameters are proposed. It is shown by some examples that the proposed structure as expected can provide better performance than conventional cascade control, a Smith predictor scheme or single feedback control system. #

Fractional order controller design with a small number of tuning parameters is very attractive. Few attempts have been done recently for some limited cases of models. In this paper, a new approach is developed to design simple fractional-order controllers to handle fractional order processes. The fractional property is not especially imposed by the controller structure but by the closed-loop reference model. The resulting controller is fractional but it has a very interesting structure for its implementation. Indeed, the controller can be decomposed into two transfer functions: a PI D-controller and a simple fractional filter. The new structure is named PI D-FOF-controller. The design method is based on the internal model control (IMC) paradigm.

Korea Electric Power Corporation (KEPCO), the sole utility company in Korea, has been designing a large class of nearly identical 500 MW power plants as part of its standard design. These power plants each of which is capable of daily startup and shutdown operation, are being constructed and operated specifically to meet the large daily swings in power demand in Korea. Each power plant in this class is of a 500 MW output design, with a coal/oil firing furnace and a once-through, supercritical boiler.To address the concern of startup time in a large power plant after being off-line for eight to ten hours of night time, a steam bypass system (HP Bypass System) is being utilized. The HP Bypass System performs four functions: (1) it shortens the unit startup time; (2) it controls the main steam pressure during normal on-line operation; (3) it maintains the main steam at minimum threshold conditions while the turbine/generator is off-line; (4) it performs the safety function of protecting against steam line overpressurization, which eliminates the need for high pressure safety/relief valves.In this paper the standard 500 MW power plant design in Korea, the daily startup and shutdown design criteria, and the HP Bypass control with safety functions will be discussed.

The main objective of this paper is to present a technique for computing the membership functions of the intuitionistic fuzzy set (IFS) by utilizing imprecise, uncertain and vague data. In literature so far, membership functions of IFS are computed via using fuzzy arithmetic operations within collected data and hence contain a wide range of uncertainties. Thus it is necessary for optimizing these spread by formulating a nonlinear optimization problem through ordinary arithmetic operations instead of fuzzy operations. Particle swarm optimization (PSO) has been used for constructing their membership functions. Sensitivity as well as performance analysis has also been conducted for finding the critical component of the system. Finally the computed results are compared with existing results. The suggested framework has been illustrated with the help of a case.

This paper studies the detection of broken rotor bars in induction motors. The hypothesis on which detection is based is that the apparent rotor resistance of an induction motor will increase when a rotor bar breaks. Here, the apparent rotor resistance is that in the balanced steady-state single-phase electricol model of an induction motor. To detect broken rotor bars, measurements of stator voltage, stator current, stator excitation frequency, and rotor velocity are taken over a small range of velocity. These measurements are processed by a near-leastsquare-error estimator to produce estimated motor states and parameters. In particular, rotor resistance is estimated and compared with its nominal value to detect broken rotor bars. As part of this estimation process, it is necessary to compensate for the thermal variation in rotor resistance. The broken-rotor-bar detector is evaluated experimentally using one stator and three rotors from identical 3-hp induction motors. In one rotor, a bar is broken by milling into the rotor. The apparent rotor resistance, as estimated, is clearly greater for the rotor with the broken bar.

This paper presents two new adaptive model predictive control algorithms both consisting of an on-line process identification part and a predictive control part. Both parts are executed at each sampling instant. The predictive control part of the first algorithm is the Nonlinear Model Predictive Control strategy and the control part of the second algorithm is the Generalized Predictive Control strategy. In the identification parts of both algorithms the process model is approximated by a series-parallel neural network structure which is trained by a recursive least squares (ARLS) method. The two control algorithms have been applied to: 1) the temperature control of a fluidized bed furnace reactor FBFR) of a pilot plant and 2) the auto-pilot control of a F-16 aircraft. The training and validation data of the neural network are obtained from the open-loop simulation of the FBFR and the nonlinear F-16 aircraft models. The identification and control simulation results show that the first algorithm outperforms the second one at the expense of extra computation time. instead of the first principles model reduces the computational burden when an accurate model is required for real-time control implementation [23], [25]. This is because a nonlinear discrete NN model of high accuracy is available immediately after or at each instant of the network training process. The accuracy of a nonlinear NN model used to identify a physical process instead of a first principles model of a physical process [9]-[12], [21] depends on the model structure of the system to be identified by the NN; the NN architecture; the selection of the inputs to the NN; the NN training algorithm; how the model will be used in an adaptive control algorithm and the structure of the closed-loop system i.e. the arrangement of the system, the NN model and the adaptive controller. Over the years, different architectures of NNs have evolved [19], [26] and it has been shown that a multilayer perceptron (MLP) NN with one hidden and output layers is capable of approximating any continuous and/or nonlinear functions reasonably well with an arbitrary degree of accuracy. The most widely used NN architecture for dynamic system modeling is the dynamic feedforward NN (DFNN) [23], [27]-[29]. The use of recurrent NN (RNN) for modeling nonlinear dynamic systems has also been reported [25], [30]-[32]. RNNs are more powerful than DFNNs because they contain the basic FNN structure with feedback connections from the output to the input layer via a state layer (the so-called Jordan network [33]) or from the output unit to the input unit of the hidden layer via a context layer (the so-called Elman network [34]). However, training these networks presents difficulties due to their feedback structures [35]. The usual methods for training RNNs are: 1) the real-time recurrent learning (RTRL) [36] which is an unrestrictive on-line, exact, stable but computationally expensive method for determining the derivatives of the state functions of a dynamic system with respect to the internal parameters of the system; and 2) the backpropagation through time (BPTT) [37] where the network is unfolded into a multilayer feedforward network that increases by one at each time step with growing memory requirements [33], [34]. Both RTRL and BPTT are variations of the backpropagation (BP) algorithm [38] which is a basic gradient descent algorithm [39], [40].

In this paper, a comparative study is done on the time and frequency domain tuning strategies for fractional order (FO) PID controllers to handle higher order processes. A new fractional order template for reduced parameter modeling of stable minimum/nonminimum phase higher order processes is introduced and its advantage in frequency domain tuning of FOPID controllers is also presented. The time domain optimal tuning of FOPID controllers have also been carried out to handle these higher order processes by performing optimization with various integral performance indices. The paper highlights on the practical control system implementation issues like flexibility of online autotuning, reduced control signal and actuator size, capability of measurement noise filtration, load disturbance suppression, robustness against parameter uncertainties etc. in light of the above tuning methodologies.

The issues of stochastically varying network delays and packet dropouts in Networked Control System (NCS) applications have been simultaneously addressed by time domain optimal tuning of fractional order (FO) PID controllers. Different variants of evolutionary algorithms are used for the tuning process and their performances are compared. Also the effectiveness of the fractional order PI λ D μ controllers over their integer order counterparts is looked into. Two standard test bench plants with time delay and unstable poles which are encountered in process control applications are tuned with the proposed method to establish the validity of the tuning methodology. The proposed tuning methodology is independent of the specific choice of plant and is also applicable for less complicated systems. Thus it is useful in a wide variety of scenarios. The paper also shows the superiority of FOPID controllers over their conventional PID counterparts for NCS applications.

A non-symmetrical Bouc–Wen model is proposed in this paper for magnetorheological (MR) fluid dampers. The model considers the effect of non-symmetrical hysteresis which has not been taken into account in the original Bouc–Wen model. The model parameters are identified with a Genetic Algorithm (GA) using its flexibility in identification of complex dynamics. The computational efficiency of the proposed GA is

In this paper, a decentralized PI/PID controller design method based on gain and phase margin specifications for two-input-two-output (TITO) interactive processes is proposed. The decouplers are designed for systems to minimize the interaction between the loops, and the first order plus dead time (FOPDT) model is achieved for each decoupled subsystem based on the frequency response fitting. An independent PI/PID controller is designed for each reduced order decoupled subsystem to obtain the desired gain and phase margins, and the performance is verified on the original interactive system to show the effectiveness of the proposed design method for the general class of TITO systems. Simulation examples are incorporated to validate the usefulness of the presented algorithm. An experimentation is performed on the Level-Temperature reactor process to show the practical applicability of the proposed method for the interactive system.

The electro-hydraulic servo system (EHSS) demonstrates a relatively low level of efficiency compared to other available actuation methods. The objective of this paper is to increase this efficiency by introducing a variable supply pressure into the system and controlling this pressure during the task of position tracking. For this purpose, an EHSS structure with controllable supply pressure is proposed and its dynamic model is derived from the basic laws of physics. A switching control structure is then proposed to control both the supply pressure and the cylinder position at the same time, in a way that reduces the overall energy consumption of the system. The stability of the proposed switching control system is guaranteed by proof, and its performance is verified by experimental testing.

The temperature control of a polymerization reactor described by Chylla and Haase, a control engineering benchmark problem, is used to illustrate the potential of adaptive control design by employing a selftuning regulator concept. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences, e.g., changing ambient temperatures or impurity of the monomer. The conventional cascade control provides a robust operation, but often lacks in control performance concerning the required strict temperature tolerances. The self-tuning control concept presented in this contribution solves the problem. This design calculates a trajectory for the cooling jacket temperature in order to follow a predefined trajectory of the reactor temperature. The reaction heat and the heat transfer coefficient in the energy balance are estimated online by using an unscented Kalman filter (UKF). Two simple physically motivated relations are employed, which allow the non-delayed estimation of both quantities. Simulation results under model uncertainties show the effectiveness of the self-tuning control concept.

This paper presents some tests which have been carried out on a commercial model of Coriolis mass flowmeter under medium-pressure conditions (p > 15 bar), utilizing a primary standard flow calibration facility. The experimental results show that the fluid pressure affects the accuracy of the tested meter, which provides underestimated readings (i.e. negative errors). Finally, some diagrams which allow to correct the output signal (or the reading) provided by the tested meter (according to the operating fluid pressure) are also presented.

The paper is concerned with a new instrument, nanobalance, measuring thrust and noise of space micro-thrusters in the range 0-1 mN with submicronewton accuracy. Low-noise micro-thrusters will be essential for upcoming ''dragfree'' space missions like GOCE, SMART-2, LISA, and Darwin. Nanobalance departs from traditional thrust measuring concepts so as to exploit sensitivity of in-vacuum Fabry-Pérot interferometers to subnanometric displacements. That is achieved by an optical cavity embraced by two equal pendulums suspended at a constant distance, one of which carrying the micro-thruster under test. Any thrust, changing the optical path length of the injected laser beam, is detected as the frequency variation restoring a standing wave in the cavity. The paper presents the measurement principles, the error budget as derived from recent tests as well as instrument automation, which is essential for facilitating instrument setup and adaptation from measurement trial to trial. The authors are responsible for instrument automation and measurement processing.

In this paper, a continuous globally stable tracking control algorithm is proposed for a spacecraft in the presence of unknown actuator failure, control input saturation, uncertainty in inertial matrix and external disturbances. The design method is based on variable structure control and has the following properties: (1) fast and accurate response in the presence of bounded disturbances; (2) robust to the partial loss of actuator effectiveness; (3) explicit consideration of control input saturation; and (4) robust to uncertainty in inertial matrix. In contrast to traditional fault-tolerant control methods, the proposed controller does not require knowledge of the actuator faults and is implemented without explicit fault detection and isolation processes. In the proposed controller a single parameter is adjusted dynamically in such a way that it is possible to prove that both attitude and angular velocity errors will tend to zero asymptotically. The stability proof is based on a Lyapunov analysis and the properties of the singularity free quaternion representation of spacecraft dynamics. Results of numerical simulations state that the proposed controller is successful in achieving high attitude performance in the presence of external disturbances, actuator failures, and control input saturation.

This paper presents the formulation of a novel block-backstepping based control algorithm to address the stabilization problem for a generalized nonlinear underactuated mechanical system. For the convenience of compact design, first, the state model of the underactuated system has been converted into the blockstrict feedback form. Next, we have incorporated backstepping control action to derive the expression of the control input for the generic nonlinear underactuated system. The proposed block backstepping technique has further been enriched by incorporating an integral action additionally for enhancing the steady state performance of the overall system. Asymptotic stability of the overall system has been analyzed using Lyapunov stability criteria. Subsequently, the stability of the zero dynamics has also been analyzed to ensure the global asymptotic stability of the entire nonlinear system at its desired equilibrium point. The proposed control algorithm has been applied for the stabilization of a benchmarked underactuated mechanical system to verify the effectiveness of the proposed control law in realtime environment. (M. Maitra). Please cite this article as: Rudra S, et al. Nonlinear state feedback controller design for underactuated mechanical system: A modified block backstepping approach. ISA Transactions (2014), http://dx.doi.org/10.1016/j.isatra.2013.12.021i ISA Transactions ∎ (∎∎∎∎) ∎∎∎-∎∎∎ Please cite this article as: Rudra S, et al. Nonlinear state feedback controller design for underactuated mechanical system: A modified block backstepping approach. ISA Transactions (2014), http://dx.

Genetic Algorithm (GA) has been used in this paper for a new approach of sub-optimal model reduction in the Nyquist plane and optimal time domain tuning of PID and fractional order (FO) PI λ D μ controllers. Simulation studies show that the Nyquist based new model reduction technique outperforms the conventional H 2 norm based reduced parameter modeling technique. With the tuned controller parameters and reduced order model parameter data-set, optimum tuning rules have been developed with a test-bench of higher order processes via Genetic Programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by weighted sum of error index and controller effort. From the reported Pareto optimal front of GP based optimal rule extraction technique a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP based tuning rules has been compared with original GA based control performance for the PID and PI λ D μ controllers, handling four different class of representative higher order processes. These rules are very useful for process control engineers as they inherit the power of the GA based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three dimensional plots of the required variation in PID/FOPID controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP based tuning rules has also been shown with credible simulation examples.

Letter to authors on publishing in ISA transactions Hi Future Authors, Years in several roles related to publishing journal articles, especially during my time as Editor-in-Chief of ISA Transactions, have led to my formulating general opinions as to what is good (and not) in writing. Here is what I offer as a guide to authors of technical articles. I hope you find it useful. Also, I am pleased that you are reading this. ISA Transactions, The Journal of Automation, has grown remarkably in quality and impact over the past decade, whether by count of manuscripts submitted, acceptance rate, number of published articles, impact factor, number of associate editors, stature of people involved in contributing to the journal, or impact on the International Society for Automation. It is a pleasure to be associated with ISA Transactions. Your reading this indicates that you are also looking toward ISA Transactions, which we appreciate. It is also my hope that the following insight leads to continued progress of ISA Transactions, and our profession. Sincerely, R. Russell Rhinehart 1. Scientific journals ISA Transactions is a science and engineering journal, and articles in such journals must comply with what is termed ''the scientific method''. This means the following. • Concluding statements and claims are the result of rational, logical, fact-based analysis of new facts presented in the article and/or truths from former refereed work. • One person's interpretation is widely corroborated by subject matter experts. • The analysis should be grounded in fundamental mathematical statements, which could represent statistics, physics, chemistry, mathematics, economics, or engineering science. • The concluding claims must be comprehensively defended by data that establishes credibility, and by discussion of results provided in the article (you cannot generalize, hypothesize, forecast, or project applicability unless the claim is defended). • The work needs to be repeatable by others, and to reveal sufficient detail that others can independently affirm the findings.

Embedded Model Control allows one to proceed systematically from fine plant dynamics and control requirements to the Embedded Model (EM), which is the core of control design and algorithms. The model defines three interconnected parts: the controllable dynamics, the disturbance class to be rejected and the neglected dynamics. Controllable and disturbance dynamics must be observable from the plant measurements. Control

Active disturbance rejection control (ADRC) has been shown to be an effective tool in dealing with real world problems of dynamic uncertainties, disturbances, nonlinearities, etc. This paper addresses its existing limitations with plants that have a large transport delay. In particular, to overcome the delay, the extended state observer (ESO) in ADRC is modified to form a predictive ADRC, leading to significant improvements in the transient response and stability characteristics, as shown in extensive simulation studies and hardware-in-the-loop tests, as well as in the frequency response analysis. In this research, it is assumed that the amount of delay is approximately known, as is the approximated model of the plant. Even with such uncharacteristic assumptions for ADRC, the proposed method still exhibits significant improvements in both performance and robustness over the existing methods such as the dead-time compensator based on disturbance observer and the Filtered Smith Predictor, in the context of some well-known problems of chemical reactor and boiler control problems.

This paper presents a shunt Active Power Filter (APF) for power quality improvements in terms of harmonics and reactive power compensation in the distribution network. The compensation process is based only on source current extraction that reduces the number of sensors as well as its complexity. A Proportional Integral (PI) or Fuzzy Logic Controller (FLC) is used to extract the required reference current from the distorted line-current, and this controls the DC-side capacitor voltage of the inverter. The shunt APF is implemented with PWM-current controlled Voltage Source Inverter (VSI) and the switching patterns are generated through a novel Adaptive-Fuzzy Hysteresis Current Controller (A-F-HCC). The proposed adaptive-fuzzy-HCC is compared with fixed-HCC and adaptive-HCC techniques and the superior features of this novel approach are established. The FLC based shunt APF system is validated through extensive simulation for diode-rectifier/R-L loads.

Fuzzy logic based PID controllers have been studied in this paper, considering several combinations of hybrid controllers by grouping the proportional, integral and derivative actions with fuzzy inferencing in different forms. Fractional order (FO) rate of error signal and FO integral of control signal have been used in the design of a family of decomposed hybrid FO fuzzy PID controllers. The input and output scaling factors (SF) along with the integro-differential operators are tuned with real coded genetic algorithm (GA) to produce optimum closed loop performance by simultaneous consideration of the control loop error index and the control signal. Three different classes of fractional order oscillatory processes with various levels of relative dominance between time constant and time delay have been used to test the comparative merits of the proposed family of hybrid fractional order fuzzy PID controllers. Performance comparison of the different FO fuzzy PID controller structures has been done in terms of optimal set-point tracking, load disturbance rejection and minimal variation of manipulated variable or smaller actuator requirement etc. In addition, multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) has been used to study the Pareto optimal trade-offs between the set point tracking and control signal, and the set point tracking and load disturbance performance for each of the controller structure to handle the three different types of processes.

This paper addresses the fuzzy system reliability analysis using different types of intuitionistic fuzzy numbers. Till now, in the literature, to analyze the fuzzy system reliability, it is assumed that the failure rates of all components of a system follow the same type of fuzzy set or intuitionistic fuzzy set. However, in practical problems, such type of situation rarely occurs. Therefore, in the present paper, a new algorithm has been introduced to construct the membership function and non-membership function of fuzzy reliability of a system having components following different types of intuitionistic fuzzy failure rates. Functions of intuitionistic fuzzy numbers are calculated to construct the membership function and non-membership function of fuzzy reliability via non-linear programming techniques. Using the proposed algorithm, membership functions and non-membership functions of fuzzy reliability of a series system and a parallel systems are constructed. Our study generalizes the various works of the literature. Numerical examples are given to illustrate the proposed algorithm.

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bispectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multiclass support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.

In this paper, a new method for the calculation of all stabilizing PI controllers is given. The proposed method is based on plotting the stability boundary locus in the (kp, ki)-plane and then computing the stabilizing values of the parameters of a PI controller. The technique presented does not require sweeping over the parameters and also does not need linear programming to solve a set of inequalities. Thus it offers several important advantages over existing results obtained in this direction. Beyond stabilization, the method is used to shift all poles to a shifted half plane that guarantees a specified settling time of response. Computation of stabilizing PI controllers which achieve user specified gain and phase margins is studied. It is shown via an example that the stabilizing region in the (kp, ki)-plane is not always a convex set. The proposed method is also used to design PID controllers. The limiting values of a PID controller which stabilize a given system are obtained in the (kp, ki)-plane, (kp, kd)-plane and (ki, kd)-plane. Furthermore, the proposed method is used to compute all the parameters of a PI controller which stabilize a control system with an interval plant family. Examples are given to show the benefits of the method presented.

In this paper, we improve the previous work by considering that a control place can have multiple selfloops. Then, two integer linear programming problems (ILPPs) are formulated. Based on the first ILPP, an iterative deadlock control policy is developed, where a control place is computed at each iteration to implement as many marking/transition separation instances (MTSIs) as possible. The second ILPP can find a set of control places to implement all MTSIs and the objective function is used to minimize the number of control places. It is a non-iterative deadlock control strategy since we need to solve the ILPP only once. Both ILPPs can make all legal markings reachable in the controlled system, i.e., the obtained supervisor is behaviorally optimal. Finally, we provide examples to illustrate the proposed approaches.

Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved.

Feature selection plays a crucial role in applications where data consists of hundreds of features due to curse of dimensionality. This paper presents two feature selection methods by modifying the main operators of Biogeography-Based Optimization algorithm. The difference between these methods is in employing binary or integer coding. The simulations perform on datasets with different feature dimensions and classes. The results indicate the effectiveness of the proposed methods in comparison with other most frequently used meta-heuristic strategies in feature selection problems.

This paper considers the design of observer-based controller for a class of continuous-time nonlinear systems presented by Takagi-Sugeno (T-S) model with unmeasurable premise variables. This T-S structure can represent a larger class of nonlinear systems as compared to the measurable premise variable case but its analysis is more complicated. To reduce the design complexity, a common output model for subsystems is employed by the use of local nonlinear rules. As a result, the proposed T-S structure reduces the number of rules in the Sugeno model as well as the analysis complexity. The proposed controller guarantees exponential convergence of states based on the fuzzy Lyapunov function analysis and Linear Matrix Inequality (LMI) formulation. Simulation results illustrate effectiveness of the proposed method.

PLC, the core element of modern automation systems, due to serial execution, exhibits limitations like slow speed and poor scan time. Improved PLC design using FPGA has been proposed based on parallel execution mechanism for enhancement of performance and flexibility. Modelsim as simulation platform and VHDL used to translate, integrate and implement the logic circuit in FPGA. Xilinx's Spartan kit for implementation-testing and VB has been used for GUI development. Salient merits of the design include cost-effectiveness, miniaturization, user-friendliness, simplicity, along with lower power consumption, smaller scan time and higher speed. Various functionalities and applications like typical PLC and industrial alarm annunciator have been developed and successfully tested. Results of simulation, design and implementation have been reported.

Electrical impedance tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. The image reconstruction for EIT is an inverse problem, which is both non-linear and ill-posed. The traditional regularization method cannot avoid introducing negative values in the solution. The negativity of the solution produces artifacts in reconstructed images in presence of noise. A statistical method, namely, the expectation maximization (EM) method, is used to solve the inverse problem for EIT in this paper. The mathematical model of EIT is transformed to the non-negatively constrained likelihood minimization problem. The solution is obtained by the gradient projection-reduced Newton (GPRN) iteration method. This paper also discusses the strategies of choosing parameters. Simulation and experimental results indicate that the reconstructed images with higher quality can be obtained by the EM method, compared with the traditional Tikhonov and conjugate gradient (CG) methods, even with non-negative processing.

In many applications involving a robot in contact with a surface it is important to control the interaction between the manipulator and its environment, usually by employing force sensors. However, sometimes it is desirable to remove them due to a variety of reasons, e.g. high costs, noisy measurements and a narrow bandwidth. To overcome these drawbacks, in this work it is proposed as a velocity/force observer based on the Generalized Proportional Integral (GPI) technique. Joint velocities and contact forces are estimated with only position measurements and then used in a force/position control scheme. Ultimate boundedness of the observation errors is formally proven and an arbitrarily small ultimate bound is then achieved. Simulation results are used to validate the proposed approach.

In this paper, modeling and PWM based control of an electro-pneumatic system, including the four 2-2 valves and a double acting cylinder are studied. Dynamic nonlinear behavior of the system, containing fast switching solenoid valves and a pneumatic cylinder, as well as electrical, magnetic, mechanical, and fluid subsystems are modeled. A DC-DC power converter is employed to improve solenoid valve performance and suppress system delay. Among different position control methods, a proportional integrator derivative (PID) controller and fuzzy logic controller (FLC) are evaluated. An experimental setup, using an AVR microcontroller is implemented. Simulation and experimental results verify the effectiveness of the proposed control strategies.

Today's market requirements drive companies in all industries to find ways to become more competitive, reduce project time lines and keep costs low. We at Genzyme Corporation's new Allston Landing Biopharmaceutical Manufacturing Facility have used standards-such as Instrument Society of America (ISA) draft standards for Batch Control Systems Models and Technology (ISA dSP88.01)-to save engineering time, money and help us meet our Food and Drug Administration (FDA) regulatory objectives with respect to implementation of a process management system.

Most RF beacons-based mobile robot navigation techniques rely on approximating line-of-sight (LOS) distances between the beacons and the robot. This is mostly performed using the robot's received signal strength (RSS) measurements from the beacons. However, accurate mapping between the RSS measurements and the LOS distance is almost impossible to achieve in reverberant environments. This paper presents a partially-observed feedback controller for a wheeled mobile robot where the feedback signal is in the form of noisy RSS measurements emitted from radio frequency identification (RFID) tags. The proposed controller requires neither an accurate mapping between the LOS distance and the RSS measurements, nor the linearization of the robot model. The controller performance is demonstrated through numerical simulations and real-time experiments.

In this work a new method for designing predictive controllers for linear single-input/single-output systems is presented. It uses only one prediction of the process output J time intervals ahead to compute the correspondent future error. Then, the predictive feedback controller is defined by introducing a filter which weights the last w predicted errors. In this way, the resulting control action is computed by observing the system future behavior and also by weighting present and past errors. This last feature improves the closed-loop performance to disturbance rejection as shown through simulations of two linear systems and a nonlinear continuous stirred tank reactor.

The purpose of this paper is to examine issues related to the distribution system reliability improvement using photovoltaic (PV) grid-connected systems. The output characteristics of a PV system are experimentally measured. The measured data are used to investigate the effects of PV system installation to improve the distribution system's reliability.

This paper describes the anti-windup compensator (AWC) design methodologies for stable and unstable cascade plants with cascade controllers facing actuator saturation. Two novel full-order decoupling AWC architectures, based on equivalence of the overall closed-loop system, are developed to deal with windup effects. The decoupled architectures have been developed, to formulate the AWC synthesis problem, by assuring equivalence of the coupled and the decoupled architectures, instead of using an analogy, for cascade control systems. A comparison of both AWC architectures from application point of view is provided to consolidate their utilities. Mainly, one of the architecture is better in terms of computational complexity for implementation, while the other is suitable for unstable cascade systems. On the basis of the architectures for cascade systems facing stability and performance degradation problems in the event of actuator saturation, the global AWC design methodologies utilizing linear matrix inequalities (LMIs) are developed. These LMIs are synthesized by application of the Lyapunov theory, the global sector condition and the ℒ 2 gain reduction of the uncertain decoupled nonlinear component of the decoupled architecture. Further, an LMI-based local AWC design methodology is derived by utilizing a local sector condition by means of a quadratic Lyapunov function to resolve the windup problem for unstable cascade plants under saturation. To demonstrate effectiveness of the proposed AWC schemes, an underactuated mechanical system, the ball-and-beam system, is considered, and details of the simulation and practical implementation results are described.

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bispectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multiclass support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.