Chung-Chun Kung

Observer-based indirect adaptive fuzzy sliding mode control with state variable filters for unknown nonlinear dynamical systems

This paper proposes an observer-based indirect adaptive fuzzy sliding mode controller with state variable filters for a certain class of unknown nonlinear dynamic systems in which not all the states are available for measurement. To design the proposed controller, we first construct the fuzzy models to describe the input/output behavior of the nonlinear dynamic system. Then, an observer is employed to estimate the tracking error vector. Based on the observer, a fuzzy sliding model controller is developed to achieve the tracking performance. Then, a filtered observation error vector is obtained by passing the observation error vector to a set of state variable filters. Finally, on the basis of the filtered observation error vector, the adaptive laws are proposed to adjust the free parameters of the fuzzy models. The stability of the overall control system is analyzed based on the Lyapunov method. Simulation results illustrate the design procedures and demonstrate the tracking performance of the proposed controller.

A fuzzy-sliding mode controller design

A fuzzy-sliding mode controller (FSMC) which has the characteristics of both fuzzy logic controller (FLC) and variable structure controller (VSC) is proposed. The FSMC has a fuzzy-sliding motion similar to the conventional sliding motion of the VSC. The distinction between them is that the switching hyperplane is a fuzzy set in the FSMC and is a crisp set in the VSC. By composing the advantages of both FLC and VSC, one can easily determine the membership functions and fuzzy rules and prespecify the behavior of control system.<<ETX>>

Modified Adaptive Fuzzy Sliding Mode Controller for Uncertain Nonlinear Systems

In this paper, a modified adaptive fuzzy sliding mode controller for a certain class of uncertain nonlinear systems is presented. We incorporate the fuzzy sliding mode control technique with a modified adaptive fuzzy control technique to design a modified adaptive fuzzy sliding mode controller so that the proposed controller is robust against the unmodeled dynamics and the approximation errors. Firstly, we establish a fuzzy model to describe the dynamic characteristics of the given uncertain nonlinear system. Then, based on the fuzzy model, a fuzzy sliding mode controller is designed. By considering both the information of tracking error and modeling error, the modified adaptive laws for tuning the adjustable parameters of the fuzzy model are derived based on the Lyapunov synthesis approach. Since the modified adaptive laws contain both the tracking error and the modeling error, it implies that the fuzzy model parameters would continuously converge until both the tracking error and modeling error converges to zero. An inverted pendulum control system is simulated to demonstrate the control performance by using the proposed method.

The robustness optimization of a multivariable feedback system in Hankel norm space

This paper is concerned with the optimization of robustness in a multi-input multi-output linear time-invariant finite dimensional feedback system. The objective is to derive a closed-form solution to the problems of the synthesis of optimal controllers that either minimize the weighted sensitivity function or maximize the excess robust ness with respect to Hankel norm criterion. An all-pass solution for the minimum Hankel norm is employed to treat our problems. Finally, the infinite norm case is also discussed.

Fuzzy-sliding mode controller design for tracking control of nonlinear system

The main purpose of this paper is to integrate fuzzy logic control and sliding mode control techniques to develop a fuzzy-sliding mode controller. In the design of the fuzzy-sliding mode controller, one can systematically determine the fuzzy control rules and predict the controlled system behavior. The sliding surface in the proposed fuzzy-sliding mode control system is a fuzzy set rather than a crisp set as given in the conventional sliding mode control system. Furthermore, it is seen that the inputs of the proposed fuzzy-sliding mode controller are fuzzy quantities of s and S/spl dot/. Hence, the number of the fuzzy-sliding mode-control rules is independent of the number of system state variables, and can be minimized. Simulation results show that the proposed fuzzy-sliding mode controller has the following advantages: 1) the fuzzy-sliding mode controller can control most of the complex systems without knowing their mathematical model; and 2) the dynamics behavior of the controlled system can be approximately dominated by a fuzzified sliding surface.

Fuzzy Controller Design: A Sliding Mode Approach

A fuzzy-sliding mode controller, which is designed by the techniques of the fuzzy logic controller and the sliding mode controller (or called variable structure control), is proposed in this work. Like the sliding mode of the sliding mode control system, the fuzzy-sliding mode control system has a fuzzy-sliding mode. The reason for calling “fuzzy-sliding mode” is that the sliding surface in the proposed scheme is a fuzzy set rather than a crisp set found in the conventional sliding mode control system. In the design of the fuzzy-sliding mode controller, one can easily determine the membership function, observe the fuzzy rules and predict the controlled system behavior. Furthermore, the number of inference rules, which is an exponential function of the number of system state variables in a conventional fuzzy logic controller, is reduced to a linear function of the number of system state variables in the fuzzy-sliding mode control system. Simulation results show that the proposed scheme has the following advantages: 1. The dynamics behavior of the controlled system can be approximately dominated by a fuzzified sliding surface. 2. Fuzzification of the sliding surface will not only increase the robustness to the system nonidealities but also decrease the chattering. 3. The fuzzy-sliding mode controller can control most of the complex ill-defined systems without knowing their mathematical models.

Adaptive fuzzy sliding mode control for a class of underactuated systems

This paper proposes an adaptive fuzzy sliding-mode controller for a class of underactuated systems. Here, the underactuated system is decoupled into two subsystems, and respectively define a sliding surface for each subsystem. The fuzzy models are applied to estimate the unknown functions of the controlled underactuated system. Then, we will propose the adaptive fuzzy sliding model controller to guarantee the tracking performance. Finally, computer simulations are given to demonstrate the tracking performance of the proposed control strategy.

Tracking control of nonlinear systems by fuzzy model-based controller

Fuzzy controllers have been successfully applied in many cases to which conventional control algorithms are difficult to be applied. Recently, it was proven that fuzzy systems are capable of approximating any real continuous function to arbitrary accuracy. This result motivates us to use the fuzzy systems as identifiers for nonlinear dynamic systems, and then design the fuzzy controllers based on the fuzzy system. There are two main objectives in this paper. (1) We use the Takagi and Sugenos fuzzy models (1985) as an identifier for nonlinear dynamic systems, and derive the identification algorithm. (2) The fuzzy model-based controller design method for tracking control is proposed based on this fuzzy system. Simulation results show that the identification algorithm exhibits good performance and the fuzzy model-based controllers could perform successful tracking ability.

Grey fuzzy sliding mode controller design with genetic algorithm

A grey fuzzy sliding mode controller with genetic algorithms (GA-GFSMC) is proposed. It employs the genetic algorithms, grey model, and sliding mode control techniques for designing the fuzzy controller. We first utilize the sliding mode control techniques to design the fuzzy control rules, so that the fuzzy sliding mode controller (FSMC) can be widely utilized in different control system. Then, we adopt a grey model as a predictor to make the one-step prediction into the future for the state behavior of the controlled plant. Thus we can obtain the control signals in advance based on the predicted values, and maintain the system safety limit. Finally, we apply the genetic algorithms to search the optimal set of parameters for the GFSMC, and hence to obtain the GA-GFSMC. Simulation results show that the GA-GFSMC can well control most of nonlinear systems without knowing their mathematical models, and it exhibits better performance than that of the GFSMC and FSMC.

A study of cluster validity criteria for the fuzzy c-regression models clustering algorithm

The fuzzy c-regression models (FCRM) clustering algorithm can fit data to locally regression models which are linear in their parameters and be used as a tool to the identification of complex nonlinear systems. To date, only a few cluster validity criteria have been proposed for the FCRM clustering algorithm to validate the partitions produced by the FCRM clustering algorithm. In this article, we examine the role of a subtle but important parameter - the weighting exponent m - plays in determining the validity of FCRM partitions. The criteria considered are the partition coefficient and two cluster validity criteria we have proposed before. The limit analysis is applied to study the behavior of these cluster validity criteria as mrarr1 and mrarrinfin . It is shown that the proposed cluster validity criteria provide well responses over a wide range of m to choose the correct cluster number.