Reinforcement learning to adaptive control of nonlinear systems

Based on the feedback linearization theory, this paper presents how a reinforcement learning scheme that is adopted to construct artificial neural networks (ANNs) can linearize a nonlinear system effectively. The proposed reinforcement linearization learning system (RLLS) consists of two sub-systems: The evaluation predictor (EP) is a long-term policy selector, and the other is a short-term action selector composed of linearizing control (LC) and reinforce predictor (RP) elements. In addition, a reference model plays the role of the environment, which provides, the reinforcement signal to the linearizing process. The RLLS thus receives reinforcement signals to accomplish the linearizing behavior to control a nonlinear system such that it can behave similarly to the reference model. Eventually, the RILLS performs identification and -linearization concurrently. Simulation results demonstrate that the proposed learning scheme, which is applied to linearizing a pendulum system, provides better control reliability and robustness than conventional ANN schemes. Furthermore, a PI controller is used to control the linearized plant where the affine system behaves like a linear system.