Abstract
ABSTRACTThe modelling problem of nonlinear control system is studied, and a higher generality nonlinear U model is established. Based on the nonlinear U model, RBF neural network and PD parallel control algorithm are proposed. The difference between the control input value and the output value of the neural network is taken as the learning target by using the online learning ability of the neural network. The gradient descent method is used to adjust the PD output value, and ultimately track the ideal output. The Newton iterative algorithm is used to complete the transformation of the nonlinear model, and the nonlinear characteristic of the plant is reduced without loss of modelling precision, consequently, the control performance of the system is improved. The simulation results show that RBF neural network and PD parallel control system can control the nonlinear system. Moreover, the control system with Newton iteration can improve the control effect and anti-interference performance of the system.
Highlights
In daily production activities, nonlinear characteristics are ubiquitous, especially with the rapid development of large-scale mechanical informationization, intelligence, and integration, and the study of nonlinear system characteristics has become important
According to the characteristics of nonlinear U model, this paper proposes the parallel control system based on RBF neural network and PID, and analyses the importance of Newton iteration algorithm in the control system
Assuming a single-input single-output (SISO) nonlinear controlled plant, the NARMAX model can be expressed as y(t) = f (y(t − 1), . . . , y(t − n), u(t − 1), . . . , u(t − n), e(t), . . . , e(t − n))
Summary
Nonlinear characteristics are ubiquitous, especially with the rapid development of large-scale mechanical informationization, intelligence, and integration, and the study of nonlinear system characteristics has become important. For a class of unknown nonlinear delay objects, an adaptive control algorithm is proposed, using neural network to identify U model of time-varying parameters [13]. For a class of uncertain nonlinear systems, an adaptive controller design scheme based on RBF network is proposed, so that the output of the nonlinear system is the expected output in case of uncertainty or unknown interference [19]. For the tracking control problem of a class of uncertain strictly feedback nonlinear systems, a new robust adaptive control design method is proposed by using RBF neural network to approximate all unknown parts of the system [20]. According to the characteristics of nonlinear U model, this paper proposes the parallel control system based on RBF neural network and PID, and analyses the importance of Newton iteration algorithm in the control system.
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