Abstract
Control parameters of classical control system are expected to be online tuned and optimized by intelligent methods, in order to improve performance and help engineers reduce a lot of repetitive work in dangerous and harmful working environments. Main ideas and works of this paper are as follows:Firstly, change ratio based expert PID control method (EA-PID) is proposed to expand range of control parameters. Expert rule table (ERT) of expert PID control method (E-PID) is replaced by change ratio table (CRT) of EA-PID. Adjusted parameters of EA-PID are the results of multiplying change ratios in current adjusting cycle and control parameters in previous adjusting cycle. Secondly, NARX prediction-based NARX-E-PID and NARX-EA-PID are proposed. The NARX neural network is designed as a time series predictor to predict the output of the control system, then control parameters are adjusted according to the predicted output. Thirdly, comparative simulations of all the above methods are implemented to verify the improved effects. Finally, theoretical analysis is provided to ensure the stability of control systems. Effect are as follows: Firstly, comparative simulations verify that the improved methods have faster control speed, smaller steady-state error, less overshoot, and better ability of anti-interference. Secondly, theoretical analysis shows that the unstable control systems with adjusted parameters can be changed into a stable system by stability judgment in each adjusting cycle.
Highlights
Classical control is widely applied in industrial practices, which mostly relies on forms of closed loop control [1]. 90% of controllers in applications are PID controllers [2], [3] because classical PID control method has many advantages such as low cost, proved stability, and easy operation [4]
3) Comparative conclusions about performance among the above 4 methods according to Fig.9 are as follows: NARX-expert PID control method (E-PID) has the fastest control speed, but NARX-E-PID has bigger overshoot
ASSUMPTIONS AND LTIs MODEL E-PID, EA-PID, NARX-E-PID, and NARX-EA-PID are based on classical PID so all the classical theories for PID control method can be used for system analysis such as features of stability, nonlinearity, etc
Summary
Classical control is widely applied in industrial practices, which mostly relies on forms of closed loop control [1]. 90% of controllers in applications are PID controllers [2], [3] because classical PID control method has many advantages such as low cost, proved stability, and easy operation [4]. PDC uses the past input and output to predict the output in a certain period of time in the future It minimizes the result of quadratic objective function with control constraints and prediction errors, the optimal control law of the current and future cycle is obtained. J. Liu et al.: NARX Prediction-Based Parameters Online Tuning Method of Intelligent PID System linear controllers. 2) The ERT is designed according to expert experience, which is simple than the gain controller nonlinear model of GSC. The gain controller of GSC model can solve the nonlinear problem of the linear PID control system. NARX (Nonlinear autoregressive with exogenous inputs Neural network) is a time series prediction method which can be adopted to predict the output of control system. NARX is adopted as the predictor to predict the system output at future time, the control parameters are adjusted according to the predicted results in each cycle
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