This paper presents an intelligent feedforward controller based on the feedback linearization app... more This paper presents an intelligent feedforward controller based on the feedback linearization approach to control nonlinear systems. In particular, the nonlinear autoregressive moving average (NARMA-L2) network is trained to reproduce the forward dynamics of the controlled system. Consequently, the trained NARMA-L2 network can be immediately integrated into the inverse feedforward control (IFC) structure. In order to improve the NARMA-L2 structure's ability to approximate nonlinear systems, the NARMA-L2 controller is comprised of two wavelet neural networks (WNNs). In addition, the RASP1 function was used as the mother wavelet function in the structure of the WNN rather than the more common Mexican Hat, Gaussian, and Morlet functions. To prevent the limitations of gradient descent (GD) methods, an artificial gorilla troops optimization (GTO) algorithm is used to determine the optimal settings for the NARMA-L2 inverse controller parameters. In particular, a modified version of th...
Indonesian Journal of Electrical Engineering and Computer Science
Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot... more Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot topic in control and computer sciences. Specifically, finding the shortest route to the goal and avoiding hurdles are current subjects of autonomous mobile robots. The Modified Grey Wolf Optimization (MGWO) is demonstrated in this work using two approaches: first, the Adaptive Adjustment Approach of the Control Parameters, and second, the Adaptive Variable Weights method. Those two methods are utilized for updating the wolf position, accelerate convergence, and cut down on time. The proposed online optimization approach is used in three different environments including an environment with unknown static obstacles, dynamic obstacles, and an environment with a dynamic target. The online optimization method is performed using two phases which are the sensors reading phase and the path calculation phase. The proposed approach can solve a local minima problem in the static obstacles. A compa...
Mobile robots use is rising every day. Path planning algorithms are
needed to make a traveler of... more Mobile robots use is rising every day. Path planning algorithms are needed to make a traveler of robots with the least cost and without collisions. Many techniques have been developed in path planning for mobile robot worldwide, however, the most commonly used techniques are presented here for further study. This essay aims to review various path planning strategies for mobile robots using different optimization methods taken recent publisher’s paper in last five year.
As a powerful nonlinear control design strategy, feedback linearization provides viable design to... more As a powerful nonlinear control design strategy, feedback linearization provides viable design tools for a wide range of nonlinear systems. This paper presents an intelligent feedback linearization design using the inverse feedforward control (IFC) scheme to control nonlinear dynamical systems. Particularly, the nonlinear autoregressive moving average (NARMA-L2) network is trained to reproduce the controlled system's forward dynamics. Consequently, the trained NARMA-L2 network can be directly employed in the IFC structure. To enhance the approximation ability of the NARMA-L2 structure, two wavelet neural networks (WNNs) are utilized to constitute the NARMA-L2 controller. Moreover, the RASP1 function was utilized as the mother wavelet function instead of the commonly employed Mexican hat function. To avoid the limitations of the gradient descent (GD) methods, the genetic algorithm has been used as the training method to optimize the NARMA-L2 inverse controller parameters. The sim...
Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot... more Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot topic in control and computer sciences. Specifically, finding the shortest root to the goal and avoiding hurdles are current subjects of autonomous mobile robots. The main drawbacks of classic methods are the incapacity to move the robot in a dynamic and unknown environment, deadlock in a local minimum and complicated environments, and incapacity to foretell the speed vector of obstacles and non-optimality of the route. This article exhibits a recent path planning approach that utilizes the African Vultures Optimization (AVOA) for navigation of the mobile robot in static and dynamic unknown environments with a dynamic target. The proposed online optimization approach is used in three different environments including an environment with unknown static obstacles, an environment with unknown dynamic obstacles, and an environment with a dynamic target. The proposed approach can solve a loca...
Journal européen des systèmes automatisés, Feb 28, 2022
For autonomous mobile robots, determining the shortest path to the target is an indispensable req... more For autonomous mobile robots, determining the shortest path to the target is an indispensable requirement. In this work, two modifications of the Grey Wolf Optimization (GWO) method, which are called MGWO1 and MGWO2, are suggested for online path planning to make the mobile robot reach the goal using the shortest path and safely avoiding the obstacles in unknown environments. To avoid sharp curves, a cost function is derived using a path smoothing parameter and an integrated distance function. The results of the proposed approach are presented based on computer simulation in various unknown environments. A study was conducted to compare the performance of the proposed algorithm with those of other algorithms and the results indicated that the proposed GWO, MGWO1, and MGWO2 algorithms are competent in avoiding obstacles successfully including the local minima situation. Finally, the average enhancement rate in path length compared with Adaptive Particle Swarm Optimization (APSO), GWO is 5.30%, MGWO1 is 5.52%, and MGWO2 is 7.44%.
ABSTRACT Cited By (since 1996): 1, Export Date: 4 February 2013, Source: Scopus, Language of Orig... more ABSTRACT Cited By (since 1996): 1, Export Date: 4 February 2013, Source: Scopus, Language of Original Document: English, Correspondence Address: Lutfy, O. F.; Electrical and Electronic Engineering Dept., Faculty of Engineering, University Putra Malaysia, 43400 Selangor, Malaysia; email: [email protected], References: Kiranoudis, C.T., Maroulis, Z.B., Marinos-Kouris, D., Dynamic simulation and control of conveyor-belt dryers (1994) Drying Technology, 12 (7), pp. 1575-1603;
... This approach has been successfully employed to control nonlinear MIMO systems (Toha and Tokh... more ... This approach has been successfully employed to control nonlinear MIMO systems (Toha and Tokhi, 2009; Yao and Chai, 2007; Zhou and Jagannathan ... a different training technique in training the ANFIS as a controller for nonlinear MIMO systems was utilized in (Mahmoud et al ...
Significant developments and technical trends in the area of grain drying technology are reviewed... more Significant developments and technical trends in the area of grain drying technology are reviewed. In particular, the innovations in different control strategies applied to grain driers along with the modelling and simulation techniques developed for different grain driers are reviewed. The review concentrates also on the analysis, investigation, assessment and performance evaluation of existing real-time grain drying systems in terms of energy consumption, grain quality and all the issues that aid in optimizing their operating efficiency. The increase in demand for energy as well as the persistent efforts to keep the environment as clean as possible has provided the impetus to search for alternative energy sources. Solar dryers represent a good substitution to conventional energy sources, and hence, this research area is also reviewed in this paper.
This paper presents a PID-like adaptive neuro-fuzzy inference system (ANFIS) controller that can ... more This paper presents a PID-like adaptive neuro-fuzzy inference system (ANFIS) controller that can be trained by the global-best harmony search (GHS) technique to control nonlinear systems. Instead of the hybrid learning methods that are widely used in the literature to train the ANFIS structure, the GHS technique alone is used to train the ANFIS as a feedback controller, and hence, the necessity for the teaching signal required by other techniques has been eliminated. Moreover, the input and output scaling factors for this controller are also determined by the GHS. To show the effectiveness of this controller and its learning method, two nonlinear plants, including the continuous stirred tank reactor (CSTR), were used to test its performance in terms of generalization ability and reference tracking. In addition, this controller robustness to output disturbances has been also tested and the results clearly indicate the remarkable performance of this controller.
This paper proposes a simplified ANFIS (Adaptive Neuro-Fuzzy Inference System) structure acting a... more This paper proposes a simplified ANFIS (Adaptive Neuro-Fuzzy Inference System) structure acting as a PID-like feedback controller to control nonlinear systems. Only few rules have been utilized in the rule base of this controller to provide the control actions, instead of the full combination of all possible rules. The proposed controller has several advantages over the conventional ANFIS structure particularly the reduction in execution time and memory resources without sacrificing the controller performance, and hence, it is more suitable for real time control. In addition, the real-coded Genetic Algorithm (GA) has been utilized to train this ANFIS controller, instead of the hybrid learning methods that are widely used in the literature, and hence, the necessity for the teaching signal required by other techniques has been eliminated. Moreover, the GA was used to find the optimal settings for the input and output scaling factors for this controller, instead of the widely used trial and error method. Three nonlinear systems, including the CSTR (Continuous Stirred Tank Reactor), have been selected to be controlled by this controller to demonstrate its accuracy and generalization ability. In addition, this controller robustness to output disturbances has been also tested and the results clearly indicated the remarkable performance of this controller. The result of comparing the performance of this controller with a conventional ANFIS controller and a conventional PID controller has shown the superiority of the proposed ANFIS structure.
Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2008
This paper presents a genetically trained PID (proportional-integral-derivative)-like ANFIS (adap... more This paper presents a genetically trained PID (proportional-integral-derivative)-like ANFIS (adaptive neuro-fuzzy inference system) acting as a feedback controller to control non-linear systems. Three important issues are addressed in this paper, which are, first, the evaluation of the ANFIS as a PID-like controller; second, the utilization of the GA (genetic algorithm) alone to train the ANFIS controller, instead of the hybrid learning methods that are widely used in the literature; and, third, the determination of the input and output scaling factors for this controller by the GA. The GA, with real-coding operators, is used to adjust all of the ANFIS parameters, which include the input and output scaling factors, the centres and widths of the input membership functions (MFs), and the consequent parameters. To show the effectiveness of this controller and its learning method, several non-linear plants, including the CSTR (continuous stirred tank reactor), have been selected to be c...
Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2011
The grain drying process is characterized by its complex and non-linear nature. As a result, conv... more The grain drying process is characterized by its complex and non-linear nature. As a result, conventional control system design cannot handle this process appropriately. This work presents an intelligent control system for the grain drying process, utilizing the capabilities of the adaptive neuro-fuzzy inference system (ANFIS) to model and control this process. In this context, a laboratory-scale conveyor-belt grain dryer was specifically designed and constructed for this study. Utilizing this dryer, a real-time experiment was conducted to dry paddy (rough rice) grains. Then, the input–output data collected from this experiment were presented to an ANFIS network to develop a control-oriented dryer model. As the main controller, a simplified proportional–integral–derivative (PID)-like ANFIS controller is utilized to control the drying process. A real-coded genetic algorithm (GA) is used to train this controller and to find its scaling factors. From the robustness tests and a comparat...
International Conference on Electrical, Control and Computer Engineering 2011 (InECCE), 2011
This paper presents a simplified ANFIS (Adaptive Neuro-Fuzzy Inference System) structure acting a... more This paper presents a simplified ANFIS (Adaptive Neuro-Fuzzy Inference System) structure acting as a PID-like feedback controller to control nonlinear multi-input multi-output (MIMO) systems. Only few rules have been utilized in the rule base of this controller to provide the control actions, instead of the full combination of all possible rules. As a result, the proposed controller has several advantages over the conventional ANFIS structure particularly the reduction in execution time without sacrificing the controller performance, and hence, it is more suitable for real time control. In addition, the real-coded genetic algorithm (GA) has been utilized to train this MIMO ANFIS controller, instead of the hybrid learning methods that are widely used in the literature. Consequently, the necessity for the teaching signal required by other techniques has been eliminated. Moreover, the GA was used to find the optimal settings for the input and output scaling factors for this controller, instead of the widely used trial and error method. To demonstrate the accuracy and the generalization ability of the proposed controller, two nonlinear MIMO systems have been selected to be controlled by this controller. In addition, this controller robustness to output disturbances has been also evaluated and the results clearly showed the remarkable performance of this MIMO controller.
The Arabian Journal for Science and Engineering, Apr 4, 2014
This paper presents a wavelet neural network (WNN)-based model reference adaptive control (WNNMRA... more This paper presents a wavelet neural network (WNN)-based model reference adaptive control (WNNMRAC) scheme to control arbitrary complex nonlinear systems. As the training method for the WNN, a newly developed optimization technique, called the micro artificial immune system (Micro-AIS), is employed to find the optimal values for the WNN parameters. Two modifications were suggested to enhance the performance of the original Micro-AIS, resulting in a more powerful optimization algorithm. Utilizing the proposed control approach, it is not necessary to construct a pseudo-plant, which was a prerequisite in other works, for controlling the nonlinear systems. To demonstrate the effectiveness of the proposed direct WNNMRAC, three single-input single-output complex nonlinear systems are selected, including a non-minimum phase system, a time-delay system, and a minimum phase system. From several performance evaluation tests, the WNNMRAC has shown its effectiveness in terms of accurate control performance, applicability to different types of nonlinear systems, robustness to external disturbances, and good generalization ability. In addition, a simulation test to control nonlinear multi-input multi-output (MIMO) system has shown that the WNNMRAC can be extended to control nonlinear MIMO systems. Finally, from a comparative study, the WNNMRAC has confirmed its superiority over a conventional neural network model reference adaptive control.
This paper presents an intelligent model reference adaptive control (MRAC) utilizing a self netwo... more This paper presents an intelligent model reference adaptive control (MRAC) utilizing a self network (SRWNN) to control nonlinear systems. The proposed SRWNN is an improved reported wavelet neural network (WNN). In particular, this improvement was achieved by adopting two modifications to the original WNN structure. These modifications include, firstly, the utilization of a specific initialization phas improve the convergence to the optimal weight values, and secondly, the inclusion of self wavelons of the wavelet layer. Furthermore, an on performance of the SRWNN-based MRAC. As the training method, the recently developed modified micro artificial immune system (MMAIS) was used to optimize the parameters of the SRWNN. The effectiveness of this control approach was demonstrated by controlling several nonlinear dynamica evaluation tests were conducted, including control performance tests, robustness tests, and generalization tests. From these tests, the SRWNN-based MRAC has exhibited i...
Indonesian Journal of Electrical Engineering and Computer Science
Servo-hydraulic systems have been extensively employed in various industrial applications. Howeve... more Servo-hydraulic systems have been extensively employed in various industrial applications. However, these systems are characterized by their highly complex and nonlinear dynamics, which complicates the control design stage of such systems. In this paper, an extended wavelet functional link neural network (EWFLNN) is proposed to control the displacement response of the servo-hydraulic system. To optimize the controller's parameters, a recently developed optimization technique, which is called the modified sine cosine algorithm (M-SCA), is exploited as the training method. The proposed controller has achieved remarkable results in terms of tracking two different displacement signals and handling external disturbances. From a comparative study, the proposed EWFLNN controller has attained the best control precision compared with those of other controllers, namely, a proportional-integralderivative (PID) controller, an artificial neural network (ANN) controller, a wavelet neural netw...
This paper presents a PID-like adaptive neuro-fuzzy inference system (ANFIS) controller that can ... more This paper presents a PID-like adaptive neuro-fuzzy inference system (ANFIS) controller that can be trained by the global-best harmony search (GHS) technique to control nonlinear systems. Instead of the hybrid learning methods that are widely used in the literature to train the ANFIS structure, the GHS technique alone is used to train the ANFIS as a feedback controller, and hence, the necessity for the teaching signal required by other techniques has been eliminated. Moreover, the input and output scaling factors for this controller are also determined by the GHS. To show the effectiveness of this controller and its learning method, two nonlinear plants, including the continuous stirred tank reactor (CSTR), were used to test its performance in terms of generalization ability and reference tracking. In addition, this controller robustness to output disturbances has been also tested and the results clearly indicate the remarkable performance of this controller.
Chaotic particle swarm optimization (CPSO) is a newly developed optimization technique which comb... more Chaotic particle swarm optimization (CPSO) is a newly developed optimization technique which combines the benefits of particle swarm optimization (PSO) and the chaotic optimization. This combination aims at avoiding the premature convergence of the PSO and the shortcomings of the chaotic optimization, in particular, the slow searching speed and the low accuracy when applied in optimizing a large search space. In addition, unlike conventional artificial neural networks (ANNs), the radial basis function neural network (RBFNN) has a more compact structure and consequently, it requires less training time compared with other ANNs and neuro-fuzzy systems. In this paper, an adaptive CPSO technique is utilized to train a RBFNN to act as a controller for nonlinear dynamical systems. Since the CPSO is a derivative-free optimization method, there is no need for a teaching signal to train the RBFNN to operate as a controller. The adaptive CPSO is employed to optimize all the modifiable paramete...
American Scientific Research Journal for Engineering, Technology, and Sciences, 2019
This paper presents an intelligent Model Reference Adaptive Control (MRAC) strategy based on a Si... more This paper presents an intelligent Model Reference Adaptive Control (MRAC) strategy based on a Simplified Recurrent Neural Network (SRNN) for nonlinear dynamical systems. This network is an enhanced version of a previously reported modified recurrent network (MRN). More precisely, the enhancement in the SRNN structure was realized by employing unity weight values between the context and the hidden layers in the original MRN structure. The newly developed Gbest-guided Gravitational Search Algorithm (GGSA) was adopted for optimizing the parameters of the SRNN structure. To show the efficiency of the proposed SRNN-based MRAC, three different nonlinear systems were considered as case studies, including complex difference equations and the water bath temperature control system. From an extensive set of evaluation tests, which includes a control performance test, a disturbance rejection test, and a generalization test, the proposed SRNN-based MRAC system demonstrated its effectiveness wit...
This paper presents an intelligent feedforward controller based on the feedback linearization app... more This paper presents an intelligent feedforward controller based on the feedback linearization approach to control nonlinear systems. In particular, the nonlinear autoregressive moving average (NARMA-L2) network is trained to reproduce the forward dynamics of the controlled system. Consequently, the trained NARMA-L2 network can be immediately integrated into the inverse feedforward control (IFC) structure. In order to improve the NARMA-L2 structure's ability to approximate nonlinear systems, the NARMA-L2 controller is comprised of two wavelet neural networks (WNNs). In addition, the RASP1 function was used as the mother wavelet function in the structure of the WNN rather than the more common Mexican Hat, Gaussian, and Morlet functions. To prevent the limitations of gradient descent (GD) methods, an artificial gorilla troops optimization (GTO) algorithm is used to determine the optimal settings for the NARMA-L2 inverse controller parameters. In particular, a modified version of th...
Indonesian Journal of Electrical Engineering and Computer Science
Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot... more Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot topic in control and computer sciences. Specifically, finding the shortest route to the goal and avoiding hurdles are current subjects of autonomous mobile robots. The Modified Grey Wolf Optimization (MGWO) is demonstrated in this work using two approaches: first, the Adaptive Adjustment Approach of the Control Parameters, and second, the Adaptive Variable Weights method. Those two methods are utilized for updating the wolf position, accelerate convergence, and cut down on time. The proposed online optimization approach is used in three different environments including an environment with unknown static obstacles, dynamic obstacles, and an environment with a dynamic target. The online optimization method is performed using two phases which are the sensors reading phase and the path calculation phase. The proposed approach can solve a local minima problem in the static obstacles. A compa...
Mobile robots use is rising every day. Path planning algorithms are
needed to make a traveler of... more Mobile robots use is rising every day. Path planning algorithms are needed to make a traveler of robots with the least cost and without collisions. Many techniques have been developed in path planning for mobile robot worldwide, however, the most commonly used techniques are presented here for further study. This essay aims to review various path planning strategies for mobile robots using different optimization methods taken recent publisher’s paper in last five year.
As a powerful nonlinear control design strategy, feedback linearization provides viable design to... more As a powerful nonlinear control design strategy, feedback linearization provides viable design tools for a wide range of nonlinear systems. This paper presents an intelligent feedback linearization design using the inverse feedforward control (IFC) scheme to control nonlinear dynamical systems. Particularly, the nonlinear autoregressive moving average (NARMA-L2) network is trained to reproduce the controlled system's forward dynamics. Consequently, the trained NARMA-L2 network can be directly employed in the IFC structure. To enhance the approximation ability of the NARMA-L2 structure, two wavelet neural networks (WNNs) are utilized to constitute the NARMA-L2 controller. Moreover, the RASP1 function was utilized as the mother wavelet function instead of the commonly employed Mexican hat function. To avoid the limitations of the gradient descent (GD) methods, the genetic algorithm has been used as the training method to optimize the NARMA-L2 inverse controller parameters. The sim...
Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot... more Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot topic in control and computer sciences. Specifically, finding the shortest root to the goal and avoiding hurdles are current subjects of autonomous mobile robots. The main drawbacks of classic methods are the incapacity to move the robot in a dynamic and unknown environment, deadlock in a local minimum and complicated environments, and incapacity to foretell the speed vector of obstacles and non-optimality of the route. This article exhibits a recent path planning approach that utilizes the African Vultures Optimization (AVOA) for navigation of the mobile robot in static and dynamic unknown environments with a dynamic target. The proposed online optimization approach is used in three different environments including an environment with unknown static obstacles, an environment with unknown dynamic obstacles, and an environment with a dynamic target. The proposed approach can solve a loca...
Journal européen des systèmes automatisés, Feb 28, 2022
For autonomous mobile robots, determining the shortest path to the target is an indispensable req... more For autonomous mobile robots, determining the shortest path to the target is an indispensable requirement. In this work, two modifications of the Grey Wolf Optimization (GWO) method, which are called MGWO1 and MGWO2, are suggested for online path planning to make the mobile robot reach the goal using the shortest path and safely avoiding the obstacles in unknown environments. To avoid sharp curves, a cost function is derived using a path smoothing parameter and an integrated distance function. The results of the proposed approach are presented based on computer simulation in various unknown environments. A study was conducted to compare the performance of the proposed algorithm with those of other algorithms and the results indicated that the proposed GWO, MGWO1, and MGWO2 algorithms are competent in avoiding obstacles successfully including the local minima situation. Finally, the average enhancement rate in path length compared with Adaptive Particle Swarm Optimization (APSO), GWO is 5.30%, MGWO1 is 5.52%, and MGWO2 is 7.44%.
ABSTRACT Cited By (since 1996): 1, Export Date: 4 February 2013, Source: Scopus, Language of Orig... more ABSTRACT Cited By (since 1996): 1, Export Date: 4 February 2013, Source: Scopus, Language of Original Document: English, Correspondence Address: Lutfy, O. F.; Electrical and Electronic Engineering Dept., Faculty of Engineering, University Putra Malaysia, 43400 Selangor, Malaysia; email: [email protected], References: Kiranoudis, C.T., Maroulis, Z.B., Marinos-Kouris, D., Dynamic simulation and control of conveyor-belt dryers (1994) Drying Technology, 12 (7), pp. 1575-1603;
... This approach has been successfully employed to control nonlinear MIMO systems (Toha and Tokh... more ... This approach has been successfully employed to control nonlinear MIMO systems (Toha and Tokhi, 2009; Yao and Chai, 2007; Zhou and Jagannathan ... a different training technique in training the ANFIS as a controller for nonlinear MIMO systems was utilized in (Mahmoud et al ...
Significant developments and technical trends in the area of grain drying technology are reviewed... more Significant developments and technical trends in the area of grain drying technology are reviewed. In particular, the innovations in different control strategies applied to grain driers along with the modelling and simulation techniques developed for different grain driers are reviewed. The review concentrates also on the analysis, investigation, assessment and performance evaluation of existing real-time grain drying systems in terms of energy consumption, grain quality and all the issues that aid in optimizing their operating efficiency. The increase in demand for energy as well as the persistent efforts to keep the environment as clean as possible has provided the impetus to search for alternative energy sources. Solar dryers represent a good substitution to conventional energy sources, and hence, this research area is also reviewed in this paper.
This paper presents a PID-like adaptive neuro-fuzzy inference system (ANFIS) controller that can ... more This paper presents a PID-like adaptive neuro-fuzzy inference system (ANFIS) controller that can be trained by the global-best harmony search (GHS) technique to control nonlinear systems. Instead of the hybrid learning methods that are widely used in the literature to train the ANFIS structure, the GHS technique alone is used to train the ANFIS as a feedback controller, and hence, the necessity for the teaching signal required by other techniques has been eliminated. Moreover, the input and output scaling factors for this controller are also determined by the GHS. To show the effectiveness of this controller and its learning method, two nonlinear plants, including the continuous stirred tank reactor (CSTR), were used to test its performance in terms of generalization ability and reference tracking. In addition, this controller robustness to output disturbances has been also tested and the results clearly indicate the remarkable performance of this controller.
This paper proposes a simplified ANFIS (Adaptive Neuro-Fuzzy Inference System) structure acting a... more This paper proposes a simplified ANFIS (Adaptive Neuro-Fuzzy Inference System) structure acting as a PID-like feedback controller to control nonlinear systems. Only few rules have been utilized in the rule base of this controller to provide the control actions, instead of the full combination of all possible rules. The proposed controller has several advantages over the conventional ANFIS structure particularly the reduction in execution time and memory resources without sacrificing the controller performance, and hence, it is more suitable for real time control. In addition, the real-coded Genetic Algorithm (GA) has been utilized to train this ANFIS controller, instead of the hybrid learning methods that are widely used in the literature, and hence, the necessity for the teaching signal required by other techniques has been eliminated. Moreover, the GA was used to find the optimal settings for the input and output scaling factors for this controller, instead of the widely used trial and error method. Three nonlinear systems, including the CSTR (Continuous Stirred Tank Reactor), have been selected to be controlled by this controller to demonstrate its accuracy and generalization ability. In addition, this controller robustness to output disturbances has been also tested and the results clearly indicated the remarkable performance of this controller. The result of comparing the performance of this controller with a conventional ANFIS controller and a conventional PID controller has shown the superiority of the proposed ANFIS structure.
Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2008
This paper presents a genetically trained PID (proportional-integral-derivative)-like ANFIS (adap... more This paper presents a genetically trained PID (proportional-integral-derivative)-like ANFIS (adaptive neuro-fuzzy inference system) acting as a feedback controller to control non-linear systems. Three important issues are addressed in this paper, which are, first, the evaluation of the ANFIS as a PID-like controller; second, the utilization of the GA (genetic algorithm) alone to train the ANFIS controller, instead of the hybrid learning methods that are widely used in the literature; and, third, the determination of the input and output scaling factors for this controller by the GA. The GA, with real-coding operators, is used to adjust all of the ANFIS parameters, which include the input and output scaling factors, the centres and widths of the input membership functions (MFs), and the consequent parameters. To show the effectiveness of this controller and its learning method, several non-linear plants, including the CSTR (continuous stirred tank reactor), have been selected to be c...
Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2011
The grain drying process is characterized by its complex and non-linear nature. As a result, conv... more The grain drying process is characterized by its complex and non-linear nature. As a result, conventional control system design cannot handle this process appropriately. This work presents an intelligent control system for the grain drying process, utilizing the capabilities of the adaptive neuro-fuzzy inference system (ANFIS) to model and control this process. In this context, a laboratory-scale conveyor-belt grain dryer was specifically designed and constructed for this study. Utilizing this dryer, a real-time experiment was conducted to dry paddy (rough rice) grains. Then, the input–output data collected from this experiment were presented to an ANFIS network to develop a control-oriented dryer model. As the main controller, a simplified proportional–integral–derivative (PID)-like ANFIS controller is utilized to control the drying process. A real-coded genetic algorithm (GA) is used to train this controller and to find its scaling factors. From the robustness tests and a comparat...
International Conference on Electrical, Control and Computer Engineering 2011 (InECCE), 2011
This paper presents a simplified ANFIS (Adaptive Neuro-Fuzzy Inference System) structure acting a... more This paper presents a simplified ANFIS (Adaptive Neuro-Fuzzy Inference System) structure acting as a PID-like feedback controller to control nonlinear multi-input multi-output (MIMO) systems. Only few rules have been utilized in the rule base of this controller to provide the control actions, instead of the full combination of all possible rules. As a result, the proposed controller has several advantages over the conventional ANFIS structure particularly the reduction in execution time without sacrificing the controller performance, and hence, it is more suitable for real time control. In addition, the real-coded genetic algorithm (GA) has been utilized to train this MIMO ANFIS controller, instead of the hybrid learning methods that are widely used in the literature. Consequently, the necessity for the teaching signal required by other techniques has been eliminated. Moreover, the GA was used to find the optimal settings for the input and output scaling factors for this controller, instead of the widely used trial and error method. To demonstrate the accuracy and the generalization ability of the proposed controller, two nonlinear MIMO systems have been selected to be controlled by this controller. In addition, this controller robustness to output disturbances has been also evaluated and the results clearly showed the remarkable performance of this MIMO controller.
The Arabian Journal for Science and Engineering, Apr 4, 2014
This paper presents a wavelet neural network (WNN)-based model reference adaptive control (WNNMRA... more This paper presents a wavelet neural network (WNN)-based model reference adaptive control (WNNMRAC) scheme to control arbitrary complex nonlinear systems. As the training method for the WNN, a newly developed optimization technique, called the micro artificial immune system (Micro-AIS), is employed to find the optimal values for the WNN parameters. Two modifications were suggested to enhance the performance of the original Micro-AIS, resulting in a more powerful optimization algorithm. Utilizing the proposed control approach, it is not necessary to construct a pseudo-plant, which was a prerequisite in other works, for controlling the nonlinear systems. To demonstrate the effectiveness of the proposed direct WNNMRAC, three single-input single-output complex nonlinear systems are selected, including a non-minimum phase system, a time-delay system, and a minimum phase system. From several performance evaluation tests, the WNNMRAC has shown its effectiveness in terms of accurate control performance, applicability to different types of nonlinear systems, robustness to external disturbances, and good generalization ability. In addition, a simulation test to control nonlinear multi-input multi-output (MIMO) system has shown that the WNNMRAC can be extended to control nonlinear MIMO systems. Finally, from a comparative study, the WNNMRAC has confirmed its superiority over a conventional neural network model reference adaptive control.
This paper presents an intelligent model reference adaptive control (MRAC) utilizing a self netwo... more This paper presents an intelligent model reference adaptive control (MRAC) utilizing a self network (SRWNN) to control nonlinear systems. The proposed SRWNN is an improved reported wavelet neural network (WNN). In particular, this improvement was achieved by adopting two modifications to the original WNN structure. These modifications include, firstly, the utilization of a specific initialization phas improve the convergence to the optimal weight values, and secondly, the inclusion of self wavelons of the wavelet layer. Furthermore, an on performance of the SRWNN-based MRAC. As the training method, the recently developed modified micro artificial immune system (MMAIS) was used to optimize the parameters of the SRWNN. The effectiveness of this control approach was demonstrated by controlling several nonlinear dynamica evaluation tests were conducted, including control performance tests, robustness tests, and generalization tests. From these tests, the SRWNN-based MRAC has exhibited i...
Indonesian Journal of Electrical Engineering and Computer Science
Servo-hydraulic systems have been extensively employed in various industrial applications. Howeve... more Servo-hydraulic systems have been extensively employed in various industrial applications. However, these systems are characterized by their highly complex and nonlinear dynamics, which complicates the control design stage of such systems. In this paper, an extended wavelet functional link neural network (EWFLNN) is proposed to control the displacement response of the servo-hydraulic system. To optimize the controller's parameters, a recently developed optimization technique, which is called the modified sine cosine algorithm (M-SCA), is exploited as the training method. The proposed controller has achieved remarkable results in terms of tracking two different displacement signals and handling external disturbances. From a comparative study, the proposed EWFLNN controller has attained the best control precision compared with those of other controllers, namely, a proportional-integralderivative (PID) controller, an artificial neural network (ANN) controller, a wavelet neural netw...
This paper presents a PID-like adaptive neuro-fuzzy inference system (ANFIS) controller that can ... more This paper presents a PID-like adaptive neuro-fuzzy inference system (ANFIS) controller that can be trained by the global-best harmony search (GHS) technique to control nonlinear systems. Instead of the hybrid learning methods that are widely used in the literature to train the ANFIS structure, the GHS technique alone is used to train the ANFIS as a feedback controller, and hence, the necessity for the teaching signal required by other techniques has been eliminated. Moreover, the input and output scaling factors for this controller are also determined by the GHS. To show the effectiveness of this controller and its learning method, two nonlinear plants, including the continuous stirred tank reactor (CSTR), were used to test its performance in terms of generalization ability and reference tracking. In addition, this controller robustness to output disturbances has been also tested and the results clearly indicate the remarkable performance of this controller.
Chaotic particle swarm optimization (CPSO) is a newly developed optimization technique which comb... more Chaotic particle swarm optimization (CPSO) is a newly developed optimization technique which combines the benefits of particle swarm optimization (PSO) and the chaotic optimization. This combination aims at avoiding the premature convergence of the PSO and the shortcomings of the chaotic optimization, in particular, the slow searching speed and the low accuracy when applied in optimizing a large search space. In addition, unlike conventional artificial neural networks (ANNs), the radial basis function neural network (RBFNN) has a more compact structure and consequently, it requires less training time compared with other ANNs and neuro-fuzzy systems. In this paper, an adaptive CPSO technique is utilized to train a RBFNN to act as a controller for nonlinear dynamical systems. Since the CPSO is a derivative-free optimization method, there is no need for a teaching signal to train the RBFNN to operate as a controller. The adaptive CPSO is employed to optimize all the modifiable paramete...
American Scientific Research Journal for Engineering, Technology, and Sciences, 2019
This paper presents an intelligent Model Reference Adaptive Control (MRAC) strategy based on a Si... more This paper presents an intelligent Model Reference Adaptive Control (MRAC) strategy based on a Simplified Recurrent Neural Network (SRNN) for nonlinear dynamical systems. This network is an enhanced version of a previously reported modified recurrent network (MRN). More precisely, the enhancement in the SRNN structure was realized by employing unity weight values between the context and the hidden layers in the original MRN structure. The newly developed Gbest-guided Gravitational Search Algorithm (GGSA) was adopted for optimizing the parameters of the SRNN structure. To show the efficiency of the proposed SRNN-based MRAC, three different nonlinear systems were considered as case studies, including complex difference equations and the water bath temperature control system. From an extensive set of evaluation tests, which includes a control performance test, a disturbance rejection test, and a generalization test, the proposed SRNN-based MRAC system demonstrated its effectiveness wit...
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Papers by Omar Farouq
needed to make a traveler of robots with the least cost and without
collisions. Many techniques have been developed in path planning for
mobile robot worldwide, however, the most commonly used techniques are
presented here for further study. This essay aims to review various path
planning strategies for mobile robots using different optimization methods
taken recent publisher’s paper in last five year.
needed to make a traveler of robots with the least cost and without
collisions. Many techniques have been developed in path planning for
mobile robot worldwide, however, the most commonly used techniques are
presented here for further study. This essay aims to review various path
planning strategies for mobile robots using different optimization methods
taken recent publisher’s paper in last five year.