This paper surveys how some "intelligence" can be incorporated in sliding-mode controllers (SMCs) by the use of computational intelligence methodologies in order to alleviate the well-known problems met in practical implementations of SMCs. The use of variable-structure system theory in design and stability analysis of fuzzy controllers is also discussed by drawing parallels between fuzzy control and SMCs. An overview of the research and applications reported in the literature in this respect is presented.

The fusion of computationally intelligent methodologies and sliding-mode control-a survey

A nonlinear controller based on a fuzzy model of MIMO dynamical systems is described and analyzed. The fuzzy model is based on a set of ARX models that are combined using a fuzzy inference mechanism. The controller is a discrete-time nonlinear decoupler, which is analyzed both for the adaptive and the fixed parameter cases. A detailed stability analysis is carried out, and the main result is that the closed loop is globally stable and robust with respect to unstructured uncertainty, which may include modeling error and disturbances. In addition, bounds on the asymptotic and transient performance are given. The main assumptions on the system and model are that they must not have strong nonminimum-phase effects, except time-delay, and the unstructured uncertainty must not be too large. A simulation example illustrates some of the properties of the modeling method and model based control structure. >

Fuzzy model based control: stability, robustness, and performance issues

A self-organizing fuzzy controller to augment a sliding-mode control (SOFSMC) scheme for a class of nonlinear systems is proposed. The motivation behind this scheme is to combine the best features of self-organizing fuzzy control and sliding-mode control to achieve rapid and accurate tracking control of a class of nonlinear systems. The chatter encountered by most sliding-mode control schemes is greatly alleviated without sacrificing invariant properties. A stability analysis is presented; the design guidelines and the class of applicable systems are clearly identified. To verify the scheme, the authors performed experiments on its implementation in a magnetic levitation system. The results show that both alleviation of chatter and robust performance are achieved; the advantages of the scheme are indicated in comparison with the conventional sliding-mode design. >

A self-organizing fuzzy sliding-mode controller design for a class of nonlinear servo systems

This paper presents a new approach to indirect vector control of induction motors. Two nonlinear controllers, one of sliding mode type and the other PI-fuzzy logic-based, define a new control structure. Both controllers are combined by means of an expert system based on Takagi-Sugeno fuzzy reasoning. The sliding-mode controller acts mainly in a transient state while the PI-like fuzzy controller acts in the steady state. The new structure embodies the advantages that both nonlinear controllers offer: sliding-mode controllers increasing system stability limits, and PI-like fuzzy logic based controllers reducing the chattering in permanent state. The scheme has been implemented and experimentally validated.

Speed control of induction motors using a novel fuzzy sliding-mode structure

Design of fuzzy sliding-mode control systems

The authors propose a method of nonlinear feedback control, introducing fuzzy inference into the sliding mode control The fuzzy inference is introduced to treat the nonlinearity of the system and to reduce the control chattering A nonlinear system is expressed by fuzzy modeling which is composed of the weighted average of linear systems with fuzzy inference Stability analysis of the fuzzy control system is carried out on the basis of fuzzy stability theory based on Lyapunov's direct method The proposed control method is applied numerically to the stabilizing control of an electric power system with an automatic voltage regulator and a governor As a result, the large oscillation which indicates the nonlinear phenomenon is stabilized; the control chattering is reduced; and a good control effect which includes control stability is obtained >

Sliding mode controller design based on fuzzy inference for non-linear systems

Sliding-mode control can deal with nonlinearities of control systems and is robust. However, it has drawbacks, such as high control gain effect and control chattering. A method of nonlinear feedback control that introduces fuzzy interference into sliding-mode control, to treat nonlinearities and reduce chattering is proposed. The stability of the system is discussed using fuzzy stability theory based on Lyapunov's direct method. The method is applied numerically to the stabilizing control of an electric power system, and is shown to give good results. >

A. Ishigame

Papers

Sliding mode controller design based on fuzzy inference for non-linear systems

Sliding mode controller design based on fuzzy inference for nonlinear systems (power systems)