Abstract
This paper presents the development of a Genetic Algorithm Supervisory Loop (GASL) to enhance the performance of Adaptive Controllers in the Self Tuning Regulator (STR) framework. The GASL is designed to tune the identification model and control parameters to achieve an optimum performance of the process. The STR controller used to demonstrate the technique effectiveness is a real time Proportional plus Integral plus Derivative (PID) algorithm. The addition of the GASL to the PID STR provides superior performance by expanding the STR’s ability to maintain control of nonlinear systems under large and quick shifts in system modes and/or disturbances. In test cases, the basic STR shows performance deterioration, while the GAS enhanced STR maintained desirable performances. The GASL developed is applied to a nonlinear adaptive suspension system for investigation and assessment. Investigations results show the effectiveness of utilizing the GASL within the adaptive STR framework.
Highlights
Systems with structural and performance complexities require sophisticated controller design techniques
Adaptive Controller (AC) techniques including those of the Gain Scheduling (GS), Self Tuning Regulator STR), Model Reference Adaptive Control (MRAC) can provide solutions to many such systems
The genetic algorithm tunes the identification model and control parameters to achieve an optimum performance of the process as developed
Summary
Systems with structural and performance complexities require sophisticated controller design techniques. AC can be effective for such systems, they in turn have limitations when changes in the system are drastic or multimodal in nature Such limitations are mainly due to their required a priori and off line designer choice settings, and their lack of on line learning [1]. Intelligent Adaptive Control (IAC) structures can present an effective approach to control complex and uncertain systems within stringent specifications. Such structures can range from utilizing simple fuzzy logic to complex neural networks [1,2]. This expression will be satisfied if terms S, T and R are as follows:
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