Search

Conventional L1 adaptive controller utilizes high adaptation gain to quickly adapt time varying uncertainties and disturbances. However, it contaminates the control signal with high frequency components. To eliminate this high frequency signal, it uses a low pass filter with fixed structure. Yet, the main problem with this fixed structured filter is that it may cancel the estimated values of uncertainties and disturbances along with the high frequencies. To overcome this problem, in this research work, an adaptive fuzzy low pass filter is proposed in place of the fixed structured filter of the L1 adaptive controller. In this proposed research work, the filter changes its structure according to the time varying uncertainties and disturbances. Hence, the contributory part of this work is that the proposed adaptive fuzzy filter efficiently cancels out high frequency components without nullifying the estimated values of uncertainties and disturbances from the control signal. The stability of the proposed adaptive fuzzy filter-based L1 adaptive controller is guaranteed analytically. The proposed method is employed on an electromechanical actuator, viz., DC motor experimental setup to validate its effectiveness. Adequate cancellation of high frequency signal incurs smooth control signal which in turn provides lower control energy. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach for designing stable adaptive fuzzy controllers, which employs a hybridization of a conventional Lyapunov-theory-based approach and a particle swarm optimization (PSO) based stochastic optimization approach. The objective is to design a self-adaptive fuzzy controller, optimizing both its structures and free parameters, such that the designed controller can guarantee desired stability and can simultaneously provide satisfactory performance. The design methodology for the controller simultaneously utilizes the good features of PSO (capable of providing good global search capability, required to provide a high degree of automation) and Lyapunov-based tuning (providing fast adaptation utilizing a local search method). Three different variants of the hybrid controller are proposed in this paper. These variants are implemented for benchmark simulation case studies and real-life experimentation, and their results demonstrate the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]