Abstract
The objective of the article is to provide an effective linearization control approach for a nonlinear system. Three reinforcement back propagation learning algorithms (RBPs), based on different step-ahead predictions, are proposed to build the affine linear model of a nonlinear system by means of a composed neural network structure. The approach is used to cancel the effect of nonlinearity of a plant. Reinforcement back propagations can compensate the nonlinearity of the system dynamics between the outputs of the reference model and the system responses. In other words, the role of the composed neural plant is to perform model matching for a linearized system. Based on the derivation of RBPs, a synthetic model, a reinforcement nonlinear control system (RNCS) is developed. This scheme excels the conventional approaches and RBPs. The proposed learning schemes are implemented to linearize a pendulum system. The simulation has been done to illustrate the performance of the proposed learning schemes.
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