Neural-adaptive control using alternate weights
This paper proposes a novel robust neural-adaptive control method for controlling underdamped non-minimum phase system. Without robust modifications to the training rule, adaptive approximators experience weight weight drift which typically causes control chatter and excitation of the natural frequency. Popular robust modifications, like e-modification and deadzone, significantly reduce performance. In the proposed method, an alternate neural network, providing approximately the same output, guides the training. The proposed algorithm trains the alternate weights in a manner so as to avoid the weight drift caused by underdamped vibrations. Experimental results show dramatic improvement in performance over e-modification when controlling a flexible-joint robot.
neural-adaptive control flexible-joint robots direct-adaptive control cerebellar model articulation controller lyapunov stability backstepping