Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence

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Intelligent tracking control of a DC motor driver using self-organizing TSK type fuzzy neural networks

[[abstract]]In this paper, a self-organizing Takagi–Sugeno–Kang (TSK) type fuzzy neural network (STFNN) is proposed. The self-organizing approach demonstrates the property of automatically generating and pruning the fuzzy rules of STFNN without the preliminary knowledge. The learning algorithms not only extract the fuzzy rule of STFNN but also adjust the parameters of STFNN. Then, an adaptive self-organizing TSK-type fuzzy network controller (ASTFNC) system which is composed of a neural controller and a robust compensator is proposed. The neural controller uses an STFNN to approximate an ideal controller, and the robust compensator is designed to eliminate the approximation error in the Lyapunov stability sense without occurring chattering phenomena. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived to speed up the convergence rates of the tracking error. Finally, the proposed ASTFNC system is applied to a DC motor driver on a field-programmable gate array chip for low-cost and high-performance industrial applications. The experimental results verify the system stabilization and favorable tracking performance, and no chattering phenomena can be achieved by the proposed ASTFNC scheme.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Development of Novel Compound Controllers to Reduce Chattering of Sliding Mode Control

The robotics and dynamic systems constantly encountered with disturbances such as micro electro mechanical systems (MEMS) gyroscope under disturbances result in mechanical coupling terms between two axes, friction forces in exoskeleton robot joints, and unmodelled dynamics of robot manipulator. Sliding mode control (SMC) is a robust controller. The main drawback of the sliding mode controller is that it produces high-frequency control signals, which leads to chattering. The research objective is to reduce chattering, improve robustness, and increase trajectory tracking of SMC. In this research, we developed controllers for three different dynamic systems: (i) MEMS, (ii) an Exoskeleton type robot, and (iii) a 2 DOF robot manipulator. We proposed three sliding mode control methods such as robust sliding mode control (RSMC), new sliding mode control (NSMC), and fractional sliding mode control (FSMC). These controllers were applied on MEMS gyroscope, Exoskeleton robot, and robot manipulator. The performance of the three proposed sliding mode controllers was compared with conventional sliding mode control (CSMC). The simulation results verified that FSMC exhibits better performance in chattering reduction, faster convergence, finite-time convergence, robustness, and trajectory tracking compared to RSMC, CSMC, and NSFC. Also, the tracking performance of NSMC was compared with CSMC experimentally, which demonstrated better performance of the NSMC controller

A Compound Fuzzy Disturbance Observer Based on Sliding Modes and Its Application on Flight Simulator

A compound fuzzy disturbance observer based on sliding modes is developed, and its application on flight simulator is presented. Fuzzy disturbance observer (FDO) is an effective method in nonlinear control. However, traditional FDO is confined to monitor dynamic disturbance, and the frequency bandwidth of the system is restricted. Sliding mode control (SMC) compensates the high-frequency component of disturbance while it is limited by the chattering phenomenon. The proposed method uses the sliding mode technique to deal with the uncompensated dynamic equivalent disturbance. The switching gain of sliding mode control designed according to the error of disturbance estimation is a small value. Therefore, the proposal also helps to decrease the chattering. The validity of the proposal method is confirmed by experiments on flight simulator