Abstract
Artificial intelligence methods are widely applied in advanced control practices such as fuzzy calculation-based intelligent control method, neural network-based predictive control method, etc. Control performances are expected to be improved such as: faster control speed, smaller steady-state error, and less repeated manual tuning workloads in harmful environments for engineers. Main works are as follows: Firstly, change ratio-based fuzzy adjusted PID (FA-PID) method is improved. The adjusted parameters of FA-PID are the multiplication result of the change ratioa at the current control cycle and the control parameters at the time of previous adjustment cycle. Secondly, prediction of back propagation neural networks-based fuzzy-PID (BPNN-F-PID) and prediction of back propagation neural networks-based FA-PID (BPNN-FA-PID) are improved, in which the adjusted control parameters are calculated according to the predicted output of control system. Thirdly, comparative simulations of all the above methods are implemented, a series of better effects are found. Effects are as follows: The improved controllers have better performance such as faster control speed, better ability of anti-interference, restrained overshoot and smaller steady-state error.
Highlights
In practical control applications, classical control (CC) methods such as proportional integral derivative (PID) methods are widely used [1]
(3) Artificial intelligence (AI)-CC methods have more practical values because they are based on the classical control methods which already have a wide range of applications
(7) A Takagi-Sugeno fuzzy PID control is proposed for variable pitch system and the PI parameters of the current loop are set by using the internal model principle
Summary
Classical control (CC) methods such as proportional integral derivative (PID) methods are widely used [1]. AI-based improved control methods are widely concerned in practical engineering including the above 90% applications. According to the above background, the motivations of this study are as follows: (1) Try to solve the control problems in the non-linear and time-varying environment which widely exist in the practical applications such as classical PID control, fuzzy control without PID, neural network based PID control [5], etc. (2) Try to improve the performance of existing classical PID and advanced fuzzy PID methods by increasing the control speed, reducing the steady-state error which are needed in most practical applications. AI-CC is valuable to be studied [20] because of the following reasons: (1) AI-CC methods are based on the classical control theory so most classical control theories can be used for theoretical analysis such as dynamic, non-linear and complex features analysis. AI-CC is valuable to be studied [20] because of the following reasons: (1) AI-CC methods are based on the classical control theory so most classical control theories can be used for theoretical analysis such as dynamic, non-linear and complex features analysis. (2) AI-CC methods have both advantages of intelligent methods and classical control methods [20]. (3) AI-CC methods have more practical values because they are based on the classical control methods which already have a wide range of applications
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