Computer Control System

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

Abstract: Today, computer control systems have achieved a highest position of their applicability through large scale industries to smaller activities such as controlling a movement of an object. Especially many researches are being carried out to develop control systems to automate the movements of vehicles as it is performed by a human being. This paper presents control systems to control the backing up motion of a truck and trailer vehicle into a particular location such as a loading dock. Salient feature of these systems is that it can be applied to any vehicle system which has a trailer attached to it. Two separate control systems are used to achieve the desired goal. In the first approach, the system uses fuzzy logic while the second approach uses an artificial neural network for the same purpose. The proposed controllers are tested for different initial positions and orientations of the truck and the trailer using a simulation model implemented with the MATLAB tool boxes. Comparisons of the results obtained are also presented. Under the conditions considered for the simulation it is observed that the fuzzy logic based control system is capable of controlling the vehicle within a larger area than that of the neural network based control system.

2.5D engraving machine is a machine used for etching, this machine has three axes which are X, Y and Z, which move two axes (X and Y) simultaneously, and the Z axis moves is not the same. Emerging issues how to implement a programming mechanism which is the most effective to perform the interpolation process the X and Y axes are driven by 5-phase stepper motor. Arduino microcontroller is used as a main controller, and Python TkInter as an interface. Hardware used are stepper motors which is controller by MOSFET IRF540 and IRF9540, and other supporting components. This controller apply the interpolation concept in linear and circular for the process of controlling the movement of two-axis stepper motors (X and Y). Linear interpolation method uses the difference between the X and Y coordinates of the destination with the previously coordinates, while the circular interpolation method using a change in frrequency of the pulses that are similar to the sine and cosine functions. From the results of tests that have been done, 2,5D engraving machine is able to do linear and circular interpolation on four quadrant. On testing the stepper motor speed, if the input frequency is 3000 Hz, then the motor speed is 176 rpm, in contrast to the result of calculation that ought to be worth 180 rpm. The value of the accuracy of the linear interpolation has an average difference of 0,43 mm, while the circular interpolation has an average difference of 2,7 mm compared with the estimated calculation. The average time required to perform the interpolation process is actually much longer 4,44 second for linear interpolation, while the circular interpolation has an average of 3,27 second time difference compared with the calculation results.

Today, computer control systems have achieved a highest position of their applicability through large scale industries to smaller activities such as controlling a movement of an object. Especially many researches are being carried out to develop control systems to automate the movements of vehicles as it is performed by a human being. This paper presents control systems to control the backing up motion of a truck and trailer vehicle into a particular location such as a loading dock. Salient feature of these systems is that it can be applied to any vehicle system which has a trailer attached to it. Two separate control systems are used to achieve the desired goal. In the first approach, the system uses fuzzy logic while the second approach uses an artificial neural network for the same purpose. The proposed controllers are tested for different initial positions and orientations of the truck and the trailer using a simulation model implemented with the MATLAB tool boxes. Comparisons o...