An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

Pei, Guojin; Yu, Miao; Xu, Yaohui; Ma, Cui; Lai, Houhu; Chen, Fokui; Lin, Hui

doi:10.3390/app11062685

Cited by 21 publications

(14 citation statements)

References 31 publications

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“…Aiming at the problems of current automobile diagnosis, in order to improve the accuracy and adaptability of diagnosis, auto fault self-diagnosis under load condition is realized. Due to the excellent performance of RNN [ 28 ] in timing data prediction, the short control time of BPNN-PID [ 29 ], the advantages of neighborhood strategy [ 30 , 31 ], and SOM's strong generalized ability [ 32 ], this paper proposes a multicondition fault self-diagnosis model based on SOM-BPNN network. Compared with the existing methods, the innovations of this paper are as follows: We use the BPNN-RNN variable speed integral PID control method to complete the high-precision control of the automobile chassis dynamometer and collect the exhaust data under the load of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] A Self‐Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM‐BPNN

Zhou

Cheng

Chang

et al. 2021

Computational Intelligence and Neuroscience

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Due to the complex and diverse forms of automobile emission detection faults and various interference factors, it is difficult to determine the fault types effectively and accurately use the traditional diagnosis model. In this paper, a multicondition auto fault diagnosis method based on a vehicle chassis dynamometer is proposed. 3σ method and data normalization were used to pretreat tail gas data. BPNN-RNN (Back Propagation Neural Networks-Recurrent Neural Networks) variable speed integral PID control method was used to achieve high-precision vehicle chassis dynamometer control. Accurate tail gas data were obtained. The simulation and test results of BPNN-RNN variable speed integral PID control were verified and analyzed. The PID control method can quickly adjust PID parameters (within 10 control cycles), control overshoot within 2% of the target value, eliminate the static error, and improve the control performance of the vehicle chassis dynamometer. Combined with BPNN (Back Propagation Neural Network) and SOM (Self-organizing Maps) network, a BPNN-SOM fault diagnosis model is proposed in this paper. By comparing and analyzing the fault diagnosis performance of various neural networks and SOM-BPNN algorithm, it is found that the SOM-BPNN model has the best comprehensive result, the prediction accuracy is 98.75%, the time is 0.45 seconds, and it has good real-time stability. The proposed model can effectively diagnose the vehicle fault, provide a certain direction for maintenance personnel to judge the vehicle state, and provide certain help to alleviate traffic pollution problem.

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Section: Introductionmentioning

confidence: 99%

[Retracted] A Self‐Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM‐BPNN

Zhou

Cheng

Chang

et al. 2021

Computational Intelligence and Neuroscience

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“…Signal forward propagation and error backpropagation are the main features of BPNN. It modifies the connection weights between each neuron by error backpropagation, and the process of backpropagation is the process of neural network learning [ 23 , 24 , 25 , 26 , 27 , 28 ]. The topology of the three-layer BPNN is shown in Figure 3 , where

and

are the input and output of the network, respectively, and

is the hidden layer output of the network.…”

Section: Fpga Design Of Bp Neural Network Pid Algorithmmentioning

confidence: 99%

A Design of FPGA-Based Neural Network PID Controller for Motion Control System

Wang

Jiang

et al. 2022

Sensors

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In the actual industrial production process, the method of adaptively tuning proportional–integral–derivative (PID) parameters online by neural network can adapt to different characteristics of different controlled objects better than the controller with PID. However, the commonly used microcontroller unit (MCU) cannot meet the application scenarios of real time and high reliability. Therefore, in this paper, a closed-loop motion control system based on BP neural network (BPNN) PID controller by using a Xilinx field programmable gate array (FPGA) solution is proposed. In the design of the controller, it is divided into several sub-modules according to the modular design idea. The forward propagation module is used to complete the forward propagation operation from the input layer to the output layer. The PID module implements the mapping of PID arithmetic to register transfer level (RTL) and is responsible for completing the output of control amount. The main state machine module generates enable signals that control the sequential execution of each sub-module. The error backpropagation and weight update module completes the update of the weights of each layer of the network. The peripheral modules of the control system are divided into two main parts. The speed measurement module completes the acquisition of the output pulse signal of the encoder and the measurement of the motor speed. The pulse width modulation (PWM) signal generation module generates PWM waves with different duty cycles to control the rotation speed of the motor. A co-simulation of Modelsim and Simulink is used to simulate and verify the system, and a test analysis is also performed on the development platform. The results show that the proposed system can realize the self-tuning of PID control parameters, and also has the characteristics of reliable performance, high real-time performance, and strong anti-interference. Compared with MCU, the convergence speed is far more than three orders of magnitude, which proves its superiority.

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“…The gradient descent is suggested in [9] for tuning and the convergence speed is analyzed. In [10], the NNs are used to determine the gains of PID and the backpropagation scheme based on Smith predictor is developed for training of NNs. In [11], a NN is learned to tune the PID gains and effect of nonlinearities is investigated.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Predictive controllers can be designed as multiple inputs, multiple outputs (MIMO), and can be combined, but they are generally considered separately. Considering U(K) as the controller output, Y(K) as the system output, and Y s (K) as the optimal system output at moment K, we can define the Equation (10). Also P can be considered for the predictive horizon and M for the control horizon.…”

Section: Predictive Controlmentioning

confidence: 99%

A New General Type-2 Fuzzy Predictive Scheme for PID Tuning

et al. 2021

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The proportional-integral-derivative controller is widely used in various industrial applications. But, in many noisy problems the strong methods are needed to optimize the proportional-integral-derivative parameters. In this paper, a novel method is introduced for adjusting the proportional-integral-derivative parameters through the model predictive control and generalized type-2 fuzzy-logic systems. The rules of suggested fuzzy system are online adjusted and the parameters of proportional-integral-derivative are tuned based on the fuzzy model such that a cost function to be minimized. The designed controller is applied on continuous stirred tank reactor and the performance is compared with other traditional approaches. The main advantages are that the accuracy is improved by online modeling and optimization and a predictive scheme is added to the conventional proportional-integral-derivative controller.

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An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

Cited by 21 publications

References 31 publications

[Retracted] A Self‐Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM‐BPNN

[Retracted] A Self‐Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM‐BPNN

A Design of FPGA-Based Neural Network PID Controller for Motion Control System

A New General Type-2 Fuzzy Predictive Scheme for PID Tuning

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