Table of contents
1. Algorithm simulation effect
2. Algorithms involve an overview of theoretical knowledge
4. Complete algorithm code file
1. Algorithm simulation effect
The matlab2013b simulation results are as follows:
2. Algorithms involve an overview of theoretical knowledge
The control principle and working steps of the neural network fuzzy PID controller based on Simulink. First, we will introduce the basic principles of fuzzy control and neural network control, and then introduce how to combine these two control strategies to achieve better control results. Finally, we will describe in detail the implementation steps of the neural network fuzzy PID controller based on Simulink, and provide a specific case analysis.
Basic principles of fuzzy control and neural network control.
Fuzzy control is an experience-based control strategy that can be used to control nonlinear and complex systems. A fuzzy controller usually includes four main steps: fuzzification, rule base, reasoning, and defuzzification.
In the fuzzy stage, the input variables and output variables are transformed into fuzzy sets, that is, they are mapped to fuzzy sets. In this way, continuous variables can be transformed into discrete variables, which is convenient for subsequent control processing.
In the rule base stage, some rules are set to describe the relationship between input variables and output variables.
In the inference stage, the input variables are matched with the rules in the rule base to obtain a fuzzy output variable.
In the defuzzification stage, the fuzzy output variable is mapped to the actual control quantity to obtain a specific control quantity.
Neural network control is a learning-based control strategy, which can adaptively adjust the weight of the controller through training data in order to achieve better control effects. A neural network controller usually includes several main parts such as an input layer, a hidden layer, and an output layer.
In the input layer, the input variables are passed into the neural network controller.
In the hidden layer, the neural network controller adaptively adjusts the weights through the training data in order to achieve better control effects.
In the output layer, the output of the neural network controller is transmitted to the control system to realize the control of the controlled object.
Basic principle of neural network fuzzy PID controller
Neural network fuzzy PID controller is a method of combining neural network and fuzzy controller to realize control. It can use the adaptiveness of the neural network and the experience of the fuzzy controller to achieve a better control effect. Neural network fuzzy PID controller usually includes several main parts such as fuzzification, neural network control and defuzzification.
In the fuzzy stage, the input variables and output variables are transformed into fuzzy sets, that is, they are mapped to fuzzy sets. In this way, continuous variables can be transformed into discrete variables, which is convenient for subsequent control processing.
In the neural network control stage, the fuzzy output variables are transmitted to the neural network controller, and the weights are adaptively adjusted through the training data in order to achieve better control effects.
In the defuzzification stage, the output of the neural network controller is mapped to the actual control quantity to obtain a specific control quantity.
In the PID controller, P, I, and D represent proportional, integral, and differential control, respectively. Proportional control acts on the transient response of the system, integral control acts on the steady-state response of the system, and differential control acts on the damping characteristics of the system. Combining neural network and fuzzy controller can achieve better control effect.
The implementation steps of the neural network fuzzy PID controller based on Simulink are as follows:
(1) Establish the system model: use the modules in Simulink to establish the model of the controlled object, including the relationship between its input and output. For example, when controlling the motion of a robot, a motion model of the robot can be established.
(2) Design fuzzy controller: use the fuzzy logic controller module in Simulink to realize the design of fuzzy controller. First, the input variables and output variables need to be transformed into fuzzy sets, and then some rules are set to describe the relationship between input variables and output variables. Finally, optimize according to the actual situation.
(3) Design neural network: Use the neural network module in Simulink to realize the design of neural network. Firstly, it is necessary to determine the input variables and output variables of the neural network, and then adjust the weights adaptively according to the training data in order to achieve better control effects.
(4) Combining fuzzy controller and neural network: use the Fuzzy Logic Controller with Neural Network module in Simulink to combine the fuzzy controller and neural network. The fuzzy output variables are transmitted to the neural network controller, and the weights are adaptively adjusted through the training data in order to achieve better control effects. Finally, the output of the neural network controller is mapped to the actual control quantity to obtain a specific control quantity.
(5) Adjust the controller parameters: According to the actual situation, the controller parameters need to be adjusted. This step can be realized through the parameter setting block in Simulink.
(6) Simulation and analysis: Use Simulink for simulation and analysis to evaluate the performance of the controller and optimize it according to the actual situation.
3. MATLAB core program
4. Complete algorithm code file
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