Chun-Fei Hsu

Wavelet Adaptive Backstepping Control for a Class of Nonlinear Systems

This paper proposes a wavelet adaptive backstepping control (WABC) system for a class of second-order nonlinear systems. The WABC comprises a neural backstepping controller and a robust controller. The neural backstepping controller containing a wavelet neural network (WNN) identifier is the principal controller, and the robust controller is designed to achieve L2 tracking performance with desired attenuation level. Since the WNN uses wavelet functions, its learning capability is superior to the conventional neural network for system identification. Moreover, the adaptation laws of the control system are derived in the sense of Lyapunov function and Barbalats lemma, thus the system can be guaranteed to be asymptotically stable. The proposed WABC is applied to two nonlinear systems, a chaotic system and a wing-rock motion system to illustrate its effectiveness. Simulation results verify that the proposed WABC can achieve favorable tracking performance by incorporating of WNN identification, adaptive backstepping control, and L2 robust control techniques

Fuzzy–Neural Sliding-Mode Control for DC–DC Converters Using Asymmetric Gaussian Membership Functions

A fuzzy-neural sliding-mode (FNSM) control system is developed to control power electronic converters. The FNSM control system comprises a neural controller and a compensation controller. In the neural controller, an asymmetric fuzzy neural network is utilized to mimic an ideal controller. The compensation controller is designed to compensate for the approximation error between the neural controller and the ideal controller. An online training methodology is developed in the Lyapunov sense; thus, the stability of the control system can be guaranteed. Finally, to investigate the effectiveness of the FNSM control scheme, it is applied to control a pulsewidth-modulation-based forward dc-dc converter. Experimental results show that the proposed FNSM control system is found to achieve favorable regulation performances even under input-voltage and load-resistance variations

Type-2 Fuzzy Logic Controller Design for Buck DC-DC Converters

Type-1 fuzzy logic controllers (TIFLCs) have been successfully developed and used in various applications. The experience and knowledge of human experts are needed to decide both the membership functions and the fuzzy rules. However, in the real-time applications, uncertainty associated with the available information always happens. This paper proposes a type-2 fuzzy logic control (T2FLC), which involves the fuzzifier, rule base, fuzzy inference engine, and output processor with type reduction and defuzzifier. Because the antecedent and/or consequent membership functions of the T2FLC are type-2 fuzzy sets, the T2FLC can handle rule uncertainties when the operation is extremely uncertain and/or the engineers cannot exactly determine the membership grades. Furthermore, the proposed T2FLC is applied to a buck DC-DC converter control. Experimental results show that the proposed T2FLC is robust against input voltage and load resistance variations for the converter control

ADAPTIVE CONTROL FOR MIMO UNCERTAIN NONLINEAR SYSTEMS USING RECURRENT WAVELET NEURAL NETWORK

Recurrent wavelet neural network (RWNN) has the advantages such as fast learning property, good generalization capability and information storing ability. With these advantages, this paper proposes an RWNN-based adaptive control (RBAC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The RBAC system is composed of a neural controller and a bounding compensator. The neural controller uses an RWNN to online mimic an ideal controller, and the bounding compensator can provide smooth and chattering-free stability compensation. From the Lyapunov stability analysis, it is shown that all signals in the closed-loop RBAC system are uniformly ultimately bounded. Finally, the proposed RBAC system is applied to the MIMO uncertain nonlinear systems such as a mass-spring-damper mechanical system and a two-link robotic manipulator system. Simulation results verify that the proposed RBAC system can achieve favorable tracking performance with desired robustness without any chattering phenomenon in the control effort.

FPGA-based adaptive PID control of a DC motor driver via sliding-mode approach

The proportional-integral-derivative (PID) controller has been extensively applied in practical industry due to its appealing characteristics such as simple architecture, easy design and parameter tuning without complicated computation. However, the PID controller usually needs some a priori manual retuning to make a successful industrial application. To attack this problem, this paper proposes an adaptive PID (APID) controller which is composed of a PID controller and a fuzzy compensator. Without requiring preliminary offline learning, the PID controller can automatically online tune the control gains based on the gradient descent method and the fuzzy compensator is designed to eliminate the effect of the approximation error introduced by the PID controller upon the system stability in the Lyapunov sense. Finally, the proposed APID control system is applied to a DC motor driver and implemented on a field-programmable gate array (FPGA) chip for possible low-cost and high-performance industrial applications. It is shown by the experimental results that the favorable position tracking performance for the DC motor driver can be achieved by the proposed APID control scheme after learning of the controller parameters.

Adaptive fuzzy wavelet neural controller design for chaos synchronization

Chaotic system is a nonlinear deterministic system that displays complex, noisy-like and unpredictable behavior, so how to synchronize chaotic system become a great deal in engineering community. In this paper, an adaptive fuzzy wavelet neural synchronization controller (AFWNSC) is proposed to synchronize two nonlinear identical chaotic gyros. The proposed AFWNSC system is composed of a neural controller and a fuzzy compensator. The neural controller uses a fuzzy wavelet neural network to online approximate an ideal controller and the fuzzy compensator is used to guarantee system stable without chattering phenomena. All the parameter learning algorithms of the proposed AFWNSC scheme are derived in the Lyapunov stability sense. Finally, some simulation results verify the chaotic behavior of two nonlinear identical chaotic gyros can be synchronized by the proposed AFWNSC scheme after learning of the controller parameters. Moreover, the convergence of the tracking error and control parameters can be accelerated by the developed proportional-integral type adaptation learning algorithm.

Robust wavelet-based adaptive neural controller design with a fuzzy compensator

In this paper, a robust wavelet-based adaptive neural control (RWANC) with a PI type learning algorithm is proposed. The proposed RWANC system is composed of a wavelet neural controller and a fuzzy compensation controller. The wavelet neural control is utilized to approximate an ideal controller and the fuzzy compensation controller with a fuzzy logic system in it is used to remove the chattering phenomena of conventional sliding-mode control completely. In the RWANC, the learning algorithm is derived based on the Lyapunov function, thus the closed-loop systems stability can be guaranteed. The chaotic system control has become an emerging topic in engineering community since the uncontrolled system displays complex, noisy-like and unpredictable behavior. Therefore, the proposed RWANC approach is applied to a second-order chaotic nonlinear system to investigate the effectiveness. Through the simulation results, the proposed RWANC scheme can achieve favorable tracking performance and the convergence of the tracking error and control parameters can be accelerated by the developed PI adaptation learning algorithm.

Adaptive CMAC neural control of chaotic systems with a PI-type learning algorithm

The cerebellar model articulation controller (CMAC) has the advantages such as fast learning property, good generalization capability and information storing ability. Based on these advantages, this paper proposes an adaptive CMAC neural control (ACNC) system with a PI-type learning algorithm and applies it to control the chaotic systems. The ACNC system is composed of an adaptive CMAC and a compensation controller. Adaptive CMAC is used to mimic an ideal controller and the compensation controller is designed to dispel the approximation error between adaptive CMAC and ideal controller. Based on the Lyapunov stability theorems, the designed ACNC feedback control system is guaranteed to be uniformly ultimately bounded. Finally, the ACNC system is applied to control two chaotic systems, a Genesio chaotic system and a Duffing-Holmes chaotic system. Simulation results verify that the proposed ACNC system with a PI-type learning algorithm can achieve better control performance than other control methods.

Adaptive asymmetric fuzzy neural network controller design via network structuring adaptation

This paper proposes a self-structuring fuzzy neural network (SFNN) using asymmetric Gaussian membership functions in the structure and parameter learning phases. An adaptive self-structuring asymmetric fuzzy neural-network control (ASAFNC) system which consists of an SFNN controller and a robust controller is proposed. The SFNN controller uses an SFNN with structure and parameter learning phases to online mimic an ideal controller, simultaneously. The structure learning phase consists of the growing-and-pruning algorithms of fuzzy rules to achieve an optimal network structure, and the parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. The robust controller is designed to compensate for the modeling error between the SFNN controller and the ideal controller. An online training methodology is developed in the Lyapunov sense, and thus the stability of the closed-loop control system can be guaranteed. Finally, the proposed ASAFNC system is applied to a second-order chaotic dynamics system. The simulation results show that the proposed ASAFNC can achieve favorable tracking performance.

Recurrent fuzzy-neural approach for nonlinear control using dynamic structure learning scheme

In this paper, a dynamic recurrent fuzzy neural network (DRFNN) with a structure learning scheme is proposed. The structure learning scheme consists of two learning phases: the node-constructing phase and the node-pruning phase, which enables the DRFNN to determine the nodes dynamically to achieve optimal network structure. Then, a self-structuring recurrent fuzzy neural network control (SRFNNC) system via the DRFNN approach is developed. The SRFNNC system is composed of a neural controller and a compensation controller. The neural controller using a DRFNN to mimic an ideal controller is the main controller, and the compensation controller is designed to compensate the difference between the neural controller and the ideal controller. In the SRFNNC system, all the parameters are evolved based on the Lyapunov function to ensure the system stability. Finally, to investigate the effectiveness of the proposed SRFNNC system, it is applied to control a second-order chaotic nonlinear system. A comparison between a fixed-structuring recurrent fuzzy neural network control and the proposed SRFNNC is made. Through the simulation results, the advantages of the proposed SRFNNC method can be observed.