This paper presents a novel indirect learning architecture which uses a single neural network in implementation. The new architecture generates the error signal required in training the controller network by an innovative design using a memory element and few switches. The new controller needs only half the number of neurons and connection weights in comparison with the original indirect learning architecture. Also given are the simulation results in controlling a nonlinear plant.
A novel 9 rules self-tuning PD+I fuzzy logic controller applicable for a class of nonlinear plants is proposed in this paper. The controller comprises of three separate fuzzy logic controllers with each uses minimum number of rules and the output scaling factor is tuned automatically depending on the tracking error dynamic conditions. The controller is applied to a two-link revolute robot for the tracking control. Simulation results show that the robustness and tracking performance of the proposed controller is comparable to standard PD+I fuzzy logic controller at low and medium speed motions. However, the performance of the proposed new design far exceeds the standard design at high speed motions.
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