Norlela Ishak

Self-tuning fuzzy PID controller for electro-hydraulic cylinder

Hydraulic systems are widely used in industrial applications. This is due to its high speed of response with fast start, stop and speed reversal possible. The torque to inertia ratio is also large with resulting high acceleration capability. The nonlinear properties of hydraulic cylinder make the position tracking control design challenging. This paper presents the development and implementation of self-tuning fuzzy PID controller in controlling the position variation of electro-hydraulic actuator. The hydraulic system mathematical model is approximated using system identification technique. The simulation studies were done using Matlab Simulink environment. The output performance was compared with the design using pole-placement controller. The roots mean squared error for both techniques showed that self-tuning Fuzzy PID produced better result compared to using pole-placement controller.

Optimized PID control using Nelder-Mead method for electro-hydraulic actuator systems

Despite the application of advanced control technique to improve the performance of electro-hydraulic position control, PID control scheme seems able to produce satisfactory result. PID is preferable in industrial applications because it is simple and robust. The main problem in its application is to tune the parameters to its optimum values. This study will look into an optimization of PID parameters using Nelder-Mead approach for electro-hydraulic position control system. The electro-hydraulic system was represented by an ARX model structure obtained through MATLAB System Identification Toolbox. Second-order and third-order model of the system had been evaluated. Simulation and real-time studies show that ARX211 produced the best response in terms of transient speed and RMSE performance criteria even though the model has the least percentage of best fit.

Tracking control for electro-hydraulic actuator using ZPETC

Hydraulic actuator is widely used in industrial applications because it exhibits linear movements, fast response, smooth reversal and accurate positioning of heavy load. The torque to inertia ratio is also large which resulting high acceleration capability. Reducing the position tracking error of the hydraulic system is a very challenging task. This paper presents the development and implementation of Zero Phase Error Tracking Control (ZPETC) in controlling the position variation of electro-hydraulic actuator. The electro-hydraulic system mathematical model was approximated using system identification technique with non-minimum phase system being considered. The studies were done using Matlab Simulink environment. The simulated tracking performance was validated with the real-time system. The tracking root mean squared error for real-time system was slightly higher than the simulation due to plant-model mismatch. Both results provide good performances in tracking control.

PI-Fuzzy Logic Control for 3 Phase BLDC Motor for Electric Vehicle Application

Brushless DC motor has been widely used in electric vehicle because of its high performance and simplicity. However this motor is a multi-variable, non-linear system and easily can be influenced by the parameter variations and disturbances. The conventional controllers are unable to handle this problem. To overcome this problem a nonlinear PI-fuzzy logic controller is used to control the speed of electric vehicle traction motor. The development of this control strategy is presented in this paper. The proposed controller has simple structure and also due to its modest fuzzy rule in rulebase is relatively easy for implementation. The control is performed by Matlab/Simulink software. The simulation test results have been satisfactory in both steady and transient states. This controller has high accuracy, suitable performance, high robustness and high tracking efficiency.

Positioning and Tracking Control of an XY table

Position tracking accuracy of XY table is important for precise operations in manufacturing field. On the basis of minimizing the tracking error, a linear digital tracking control system is considered. Error Filter Feedforward Zero Phase Error Tracking Control (ZPETC) is designed and used in this system. The controller is used as it has the ability to give good performance in high speed frequency. The system transfer function is obtained via system identification technique using Matlab Toolbox. The performance of the controller in reducing the tracking error using high frequency input is analyzed through simulation. The system is also tested by implementing conventional feedforward controller for comparison purposes. Both controller performances are compared and the result shows tremendous tracking performance.

Model identification and real-time linear position control of servomotor

Plant model identification using experimental data is easy to be obtained with modern tools software such as Matlab and LabView. This powerful software provides reliable and approximate model structure. This paper presents model identification using Matlab System Identification Toolbox from open-loop input-output experimental data that obtained from linear position of a DC servo motor. The effectiveness of the proposed model was tested by a closed loop controller using pole-placement method. Simulation and real time control were conducted to show the reliability of the obtained model. The result shows acceptable performances in both simulation and real time studies.

Model reference input for an optimal PID tuning using PSO

Optimization of PID parameters had been a popular issue among academia and industrial players. This is because it is undoubtedly a simple and robust controller for most applications. Recently, many intelligent approaches using optimization techniques had emerged in trying to propose an efficient way of finding the optimal setting for the PID. Among all is using Particle Swarm Optimization (PSO). The PSO was utilized to search for optimum Kp, Ki and Kd that will minimized some objective functions typically IAE and ITSE. These objective functions will manage to find the optimal PID setting in terms of error elimination but yet they cannot guarantee satisfaction in transient response requirements in specific. This research proposed a new approach in PID optimization by introducing a model reference that represents the actual desired response of the controlled variable. This approach will satisfy not only error elimination, but specific transient requirements can be fulfilled perfectly. PSO was applied to find the PID setting by minimizing the error between model reference and process output signal. This paper presents simulation results using MATLAB Simulink to demonstrate the effectiveness of using model reference over step reference input alone. The proposed method was found to be superior in terms of accuracy and consistency in the results over using a step response reference signal alone.

Real-time application of self-tuning PID in electro-hydraulic actuator

Hydraulic actuators are characterized by their ability to impart large forces at high speeds and are used in many industrial motion systems. The nonlinear properties of hydraulic cylinder had challenged researchers to design a suitable controller for position control, motion control and tracking control. This paper presents the development and implementation of self-tuning fuzzy PID controller in controlling the position variation of electro-hydraulic actuator. The electro-hydraulic system was represented by an ARX model structure obtained through MATLAB System Identification Toolbox. Then, by experiment on a practical electro-hydraulic actuator test-bed, the effectiveness of this control scheme is verified through comparison with simulation using Matlab Simulink environment. The research results show that the self-tuning fuzzy PID controller has good performance based on RMSE index.

Modeling and controller design for non-minimum phase system with application to XY-table

In discrete-time control system, model obtained from small or reducing sampling-time would produce a non-minimum phase model. Feed-forward controller designed of this model using the inverse transfer function of the closed-loop system would have internal stability that usually not guaranteed. To overcome this stability problem, ZPETC that capable of cancelling all poles, cancellable-zeros, and phase error was proposed by Tomizuka. This paper presents model identification and controller design for non-minimum phase system using the zero phase error tracking control (ZPETC) method. The non-minimum phase system model was approximated using Matlab System Identification Toolbox from open-loop input-output experimental data using 80ms sampling time. This approximated model was used in simulation and real-time studies. The effectiveness of ZPETC to overcome the non-minimum phase problem on XY-table shows that the proposed method gave acceptable performances.

Model identification and controller design for servomotor

The availability of modern tools such as Matlab System Identification Toolbox and LabVIEW System Identification Toolkit make plant model identification using experimental data easy to be implemented and less cumbersome than using Physic laws. This method can provides reliable and approximate model structure. Currently, many researchers keen on using Matlab System Identification Toolbox to approximate their plant models. This paper presents model identification using Matlab System Identification Toolbox from open-loop input-output experimental data that obtained from a Quanser DC servomotor. The reliability of obtained model was tested by designing feedback control system using pole-placement method. Simulation and real-time studies were conducted to show the reliability of the obtained model. Results show that the obtained model shows acceptable performances in both the simulation and real-time studies.