Adaptive Neural Network Controller Design for a Class of Nonlinear Systems Using SPSA Algorithm
نویسنده
چکیده
In this paper, we propose a novel SPSA-based on-line adaptive decoupled control scheme by using PID neural network for a class of nonlinear systems. In addition, the update laws of parameters with adaptive optimal learning rate are proposed based on the Lyapunov stability theorem, this guarantees the stability of closed-loop system. In addition, the affect of the frictional force model and uncertainty are discussed and analyzes. The proposed approach is applied in the translational oscillations with a rotational actuator (TORA) system. In experimental results, the proposed control is realized by DSP to demonstrate the performance and the efficiency.
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