Fuzzy Iterative Learning Control Research Articles

Fuzzy iterative learning control based on genetic algorithm

In order to improve the control precision and to speed up the convergence rate of the controlled system,a kind of fuzzy PD type iterative learning control algorithm was put forward based on genetic algorithm.In the proposed approach,the iterative learning controller was designed by fuzzy Takagi-Sugeno-Kang(TSK) system,the parameters of fuzzy TSK system were calculated by genetic algorithm,and then appropriate updating law was created.Appropriate iterative learning control algorithm of controlled system was designed and compared with PD iterative learning control algorithm and fuzzy PID iterative learning control algorithm,and then the proposed algorithm was used in double joint manipulator simulation.The simulation results show the effectiveness of the proposed algorithm.

A Clonal Selection Algorithm Based Fuzzy Optimal Iterative Learning Control Algorithm

In order to realize effective tracking of output of non-linear plants with model uncertainty in specified time domain, a clonal selection algorithm based fuzzy optimal iterative learning control algorithm is proposed. In the algorithm, a clonal selection algorithm is employed to search optimal input for next iteration, and another clonal selection algorithm is used to update the parameters of Takagi-Sugeno-Kang fuzzy system model of the plant. Simulations show that the proposed method converges faster than GA-ILC in iterative domain，and is able to deal with model uncertainty well

Development of Self -Tuning Fuzzy Iterative LearningControl for Controlling a Mechatronic System

In this paper, a self-tuning fuzzy iterative learning control algorithm is proposed to ensure the improvement and enhancement in the performance of the control system by using the benefits of both feedback control due to fuzzy controller and feedforward compensation due to iterative learning controller(ILC) merged in the same control system structure. The performance of proposed algorithm was assessed in a mechatronic system to illustrate the validation of the proposed procedure and the effectiveness of the self-tuning fuzzy iterative learning controller. The simulations results show that the proposed self-tuning fuzzy iterative learning control (STFILC) can reduce the trajectory error in far less number of iterations.

Fuzzy Iterative Learning Control of Servo System

According to the repeatability characteristics of servo system, a fuzzy iterative learning controller is proposed which combining advantages of fuzzy control and iterative learning control. Fuzzy control has better robustness, and does not require accurate model of the system, only need the previous experience, the design method is simple. Fuzzy controller is used in the position-loop, but the static error is difficult to eliminate, iterative learning controller use the control error to adjust the previous control input to reduce the error of next time. The combination of these two intelligent control algorithms can improve the control performance of servo system effectively. Simulation results show that the fuzzy control combined with iterative learning control can achieve better control performance in servo system.

Fuzzy iterative learning control of electro-hydraulic servo system for SRM direct-drive volume control hydraulic press

A new kind of volume control hydraulic press that combines the advantages of both hydraulic and SRM (switched reluctance motor) driving technology is developed. Considering that the serious dead zone and time-variant nonlinearity exist in the volume control electro-hydraulic servo system, the ILC (iterative learning control) method is applied to tracking the displacement curve of the hydraulic press slider. In order to improve the convergence speed and precision of ILC, a fuzzy ILC algorithm that utilizes the fuzzy strategy to adaptively adjust the iterative learning gains is put forward. The simulation and experimental researches are carried out to investigate the convergence speed and precision of the fuzzy ILC for hydraulic press slider position tracking. The results show that the fuzzy ILC can raise the iterative learning speed enormously, and realize the tracking control of slider displacement curve with rapid response speed and high control precision. In experiment, the maximum tracking error 0.02 V is achieved through 12 iterations only.

A Combined Adaptive Law for Fuzzy Iterative Learning Control of Nonlinear Systems With Varying Control Tasks

To deal with the iterative control of uncertain nonlinear systems with varying control tasks, nonzero initial resetting state errors, and nonrepeatable mismatched input disturbance, a new adaptive fuzzy iterative learning controller is proposed in this paper. The main structure of this learning controller is constructed by a fuzzy learning component and a robust learning component. For the fuzzy learning component, a fuzzy system used as an approximator is designed to compensate for the plant nonlinearity. For the robust learning component, a sliding-mode-like strategy is applied to overcome the nonlinear input gain, input disturbance, and fuzzy approximation error. Both designs are based on a time-varying boundary layer which is introduced not only to solve the problem of initial state errors but also to eliminate the possible undesirable chattering behavior. A new adaptive law combining time- and iteration-domain adaptation is derived to search for suitable values of control parameters and then guarantee the closed-loop stability and error convergence. This adaptive algorithm is designed without using projection or deadzone mechanism. With a suitable choice of the weighting gain, the memory size for the storage of parameter profiles can be greatly reduced. It is shown that all the adjustable parameters as well as internal signals remain bounded for all iterations. Moreover, the norm of tracking state error vector will asymptotically converge to a tunable residual set even when the desired tracking trajectory is varying between successive iterations.