Novel adaptive control algorithms via soft computing methods

Abstract

Typical objectives in control of dynamic systems are to ensure stability, tracking, and disturbance rejection. This thesis focuses on integration soft computing techniques with modern control methodologies for the developing novel adaptive control algorithms. The philosophy behind this study is that soft computing methods complement and not necessarily replace model-based control methodologies. The thesis presents extensive simulation studies and experimental verifications to demonstrate the characteristics of the proposed adaptive control methodologies. In the first part of the study, we study the problem of a class of nonlinear systems that can be linearized around an operating point and can be represented by a lower-order model with time delay. Many chemical and petrochemical processes fall into this category. A new algorithm to approximate higher order systems with first order plus time delay via neural network is proposed. An on-line proportional plus integral derivative (PID) tuning method is then applied to control such systems. In the second part, we address the problem of adaptive control of affine nonlinear dynamic systems. Based on a simple variable structure control, the sliding mode control, a novel adaptive fuzzy sliding mode control with chattering elimination has been developed. An algorithm has also been proposed to eliminate chattering at steady state. Next, an H-infinity control technique incorporating a fuzzy system is studied. We introduce an adaptive fuzzy control with state observer to guarantee a robust performance of the controlled system. The H-infinity control is used to guarantee the robustness in the presence of system uncertainties. We will also consider the multiple-input multiple-output nonlinear dynamic systems. We introduce the design of robust adaptive fuzzy control, with the aid of integrated sliding mode algorithm, an improved robust adaptive controller is proposed. This algorithm has been applied to a robot manipulator. We have also developed a direct adaptive fuzzy control based on a Takagi-Sugeno fuzzy system. We introduce a direct adaptive fuzzy control with state observer to estimate the unmeasured states of the multiple-input multiple-output controlled system. The controller has been tested for control of a two degree-of-freedom helicopter to track a given trajectory. Moreover, the system stability can be guaranteed based on Lyapunov's principle. Finally, a robust adaptive fuzzy control for a class of uncertain nonlinear systems is examined. We introduce the design of adaptive fuzzy control for a general class of strict-feedback uncertain nonlinear dynamic systems. The main idea of this method is to apply the fuzzy system to derive a novel robust adaptive tracking controller by use of the input-to-state stability and by combining the backstepping technique.