Neural PID Controller Research Articles

Robust ELM-PID tracing control on autonomous mobile robot via transformer-based pavement crack segmentation

Pavement crack tracing is paramount to missions encompassing automated crack sealing for road maintenance. However, existing methods still face several challenges, including the incapability to precisely extract crack trajectories and the challenge of tuning control parameters within intricate backgrounds. To address these limitations, the ViT-S2T network and the ELM-PID control system are proposed for crack tracing. Specifically, the ViT-S2T consists of two branches. The transformer-based feature extraction module (TFEM) integrates multi-head attention mechanism and multi-layer perceptron to capture global contextual crack semantic features. The incoherent segmentation masks (ISM) employs a binary classifier to predict the coarsest irrelevant mask and further performs up-sampling fusion of higher-resolution features. Moreover, the Neural-PID control method is designed to track crack trajectories, combining Extreme Learning Machines (ELM) and Proportional Integral Derivative (PID). The ELM-PID controller utilizes a three-layer backpropagation neural network and proposes the ELM model for adaptive adjustment by predicting the tuning parameters of PID. This framework is applied to real-time visual tracing for edge AI. Extensive tests performed on three arduous datasets of DeepCrack, CFD, and S2TCrack, achieving a precision of 82.76% and mAP@0.5 of 75.63% and speed of 0.0513 m/s, demonstrating the superior and robust nature of our approach in pavement crack tracing.

Research on the simulation method of a BP neural network PID control for stellar spectrum

This study investigated the multiple correlations among spectral simulation units based on digital micromirror device (DMD) spectral simulation, which leads to the problem that conventional spectral simulation methods such as PID control exhibit a low fitting accuracy or long fitting time in the spectral simulation of various targets. In this paper, a method of stellar spectrum simulation based on back propagation neural network-based PID (BP-PID) control is proposed to achieve high efficiency and high precision simulation of various spectral targets. The topology of the BP neural network was constructed based on the spectral modulation model of a DMD stellar spectrum simulation system, and the algorithm of the BP-PID control was designed. Finally, an experimental platform was built to verify the performance and spectral simulation accuracy of the BP-PID control algorithm. The results show that the overshoot and response time of the BP-PID control algorithm decreased by 79.01% and 30%, respectively compared with those of the PID control algorithm. The maximum spectral simulation accuracies of 2000K, 7000K, and 12000K color temperature increased by a factor of 2.311, 1.871, and 2.254, respectively, and the standard deviations of the spectral simulation error decreased by 56%, 41%, and 54%, respectively. In the range of 2000-12000K color temperature, the spectral simulation error of the BP-PID control algorithm is better than ±3.495%, and the standard deviation of the spectral simulation error is between 1.8255 and 2.2358. The proposed method can improve the spectral simulation accuracy and simulation efficiency of a star simulator, reduce the magnitude and spectrum calibration errors caused by the differential response, improve the star feature recognition accuracy of the orbiting star sensor, and hence, provide a theoretical and technical basis for the development of high-precision star sensors.

Design of a optimal robust adaptive neural network-based fractional-order PID controller for H-bridge single-phase inverter

Adaptive Neural Network- based Fractional-order PID Control (NN-FO-PID) approach is designed for H-bridge inverter. This inverter has an LC filter to decrease the level of Total Harmonic Distortion (THD) that can affect the efficiency of the system. In addition, the reduction of THD and stability insurance of the filter are challenging performances. To fulfill this function, a fractional order proportional integrated derivative (FO-PID) controller is developed for the inverter. A few merits of the Fractional-Order notion as a useful technique include reduced sensitivity to noise and parametric fluctuation; however, for a wider range of disturbances, such as noise, this approach shows an unsuitable practical application based on its fixed gain values. Moreover, having parameters uncertainties including parametric variations, load uncertainty, supply voltage variation uplifts this challenging condition severely, and the parameters need to be adjusted once more for more dependable operations. As a result, the control parameters must be optimized once more to provide ideal operations. Here, an adaptive mechanism is proposed based on neural network structure to optimize the gains of the FO-PID controller for better performances. In real applications, this approach has some benefits, since it uses the Black-box technique, which does not necessitate a precise mathematical model of the system, resulting in a reduced computational burden, simple implementation, and reduced dependence on the model’s states. Even under extremely difficult circumstances, the artificial neural network structure effectively optimizes the FO-PID gains in real-time. This benefit can reduce the amount of THD-level for the inverter, properly. Additionally, to have a proper response in the first step condition, and trying to reduce the level of dangerous conditions, based on benefits of the ant lion optimization method, it is considered to select the initial values of the FO-PID controller gains. Here to verify the superiority of the proposed controller, PID and FO-PID controllers are offered to drive a comparison with this method, which are optimized using the PSO optimization algorithm. The analysis of simulation data shows that the suggested control strategy is appropriate for maintaining stability as well as adequately compensating for disturbances and uncertainties in the inverter. Furthermore, with the help of this controller, THD level decreased lower than 2 percent.

Real time adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural network proportional-integral-derivative controller for nonlinear systems

This paper presents an adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural PID controller for handling the problems of uncertainties in nonlinear systems. The proposed controller combines probabilistic processing with a Takagi-Sugeno-Kang fuzzy neural system to proficiently address stochastic uncertainties in controlled systems. The stability of the controlled system is ensured through the utilization of Lyapunov function to adjust the controller parameters. By tuning the probability parameters of the controller design, an additional level of control is achieved, leading to enhance the controller performance. Furthermore, it can operate without relying on the system's mathematical model. The proposed control approach is employed in nonlinear dynamical plants and compared to other existing controllers to validate its applicability in engineering domains. Simulation and experimental investigations demonstrate that the proposed controller surpasses alternative controllers in effectively managing external disturbances, random noise, and a broad spectrum of system uncertainties.

Dynamic Modeling, Simulation, and Optimization of Vehicle Electronic Stability Program Algorithm Based on Back Propagation Neural Network and PID Algorithm

Dynamic Modeling, Simulation, and Optimization of Vehicle Electronic Stability Program Algorithm Based on Back Propagation Neural Network and PID Algorithm

Micro/nanopositioning coplanar stage with real-time compensation of volumetric error

A micro/nanopositioning coplanar stage is designed with real-time compensation of volumetric error. According to the topological structure of the stage, it has 13 geometric error components. Furthermore, there are positional shifts of a functional point from the displacement feedback point and the measurement point of geometric errors. To improve the positional accuracy, it is crucial to real-time measure, model, and compensate for the volumetric error at the functional point. Therefore, a volumetric error measuring unit (VEMU) is designed to simultaneously real-time measure 13 geometric error components. A volumetric error model is established using the Abbe and Bryan principles. A control unit with two backpropagation neural networks-based PID controllers combines with the VEMU and volumetric error model to realize the real-time compensation of the volumetric error. The positional error of the micro/nanopositioning coplanar stage is reduced approximately 97.6 %, from which the effectiveness and applicability of the proposed methods are verified.

Neural Network-Based Adaptive PID Controller Design for Over-Frequency Control in Microgrid Using Honey Badger Algorithm

Neural Network-Based Adaptive PID Controller Design for Over-Frequency Control in Microgrid Using Honey Badger Algorithm

Neural Fractional Order PID Controllers Design for 2-Link Rigid Robot Manipulator

The robotic manipulator is considered one of the complex systems that include multi-input, multi-output, non-linearity, and highly coupled. The uncertainty in the parameters and external disturbances have a negative influence on the performance of the system. Therefore, the controllers that will be designed for these systems must be able to deal with these complexities and difficulties. The Proportional, Integral, and Derivative (PID) controller is known to be simple and well robust, while the neural network has a solid ability to map complex functions. In this paper, we propose six control structures by combining the benefits of PID controller with integer and fractional order and the benefits of neural networks to produce hybrid controllers for a 2-Link Rigid Robot Manipulator (2-LRRM) handling with the problem of trajectory tracking. The Gorilla Forces Troops Optimization algorithm (GTO) was used to tune the parameters of the proposed controller schemes to minimize the Integral of Time Square Error (ITSE). In addition, the robustness of the performance of the suggested control systems is tested by altering the initial position, external disturbances and parameters and carried out using MATLAB. The best performance of the proposed controllers was the Neural Network Fractional Order Proportional Integral Derivative Controller (NNFOPID).

A Comparison of the Response and Voltage Regulation Performance of the Single-Channel DC/DC Boost Converter Circuit with Artificial Neural Networks, Fuzzy Logic and PID Controllers

A Comparison of the Response and Voltage Regulation Performance of the Single-Channel DC/DC Boost Converter Circuit with Artificial Neural Networks, Fuzzy Logic and PID Controllers

Fuzzy Adaptive Control Law for Trajectory Tracking Based on a Fuzzy Adaptive Neural PID Controller of a Multi-rotor Unmanned Aerial Vehicle

Fuzzy Adaptive Control Law for Trajectory Tracking Based on a Fuzzy Adaptive Neural PID Controller of a Multi-rotor Unmanned Aerial Vehicle

Fertilization Control System Research in Orchard Based on the PSO-BP-PID Control Algorithm

In order to improve the precision of the variable-rate fertilization system in orchards, this paper conducted a simulation by MATLAB and experimental research based on a variable-rate fertilization experiment platform. The variable-rate fertilization experimental platform was mainly composed of a power supply, DC motors, a PPC-15A1 on-board computer that contains a PCI8932 PC-DAQ, speed sensors, fertilizer dischargers, and a NAV60 module that can receive Beidou Navigation Satellite System positioning data. According to the fertilizer application mechanism of an external grooved wheel fertilizer applicator, the control system model of the variable-rate fertilization driven by the DC motor for orchards was established. A BP neural network adaptive PID controller based on particle swarm optimization (PSO) was proposed to improve the control precision of the system. The step response simulation results by MATLAB show that the overshoot of the BP-PID controller optimized by the PSO algorithm (PSO-BP-PID) is 12.7%, and the adjustment time is 0.557 s. The variable-rate fertilization experiments were conducted, in which the control system was tested by using the PSO-BP-PID controller. The variable fertilizer seeder control system of the Chinese national standard was adopted to evaluate the performance indexes of the system, such as the range of fertilizer amount adjustment, the response time of fertilizer amount adjustment, and the control precision of fertilizer amount. In the variable rate fertilization experiments, the average fertilization errors, respectively, are 1.16% and 1.07%, under the conditions of changing the target fertilization amount and the vehicle speed. The test results are consistent with the simulation results, and the variable-rate fertilization performance parameters are improved.

Design of a neural network-based PID controller for four-DoF Lower Limb Exoskeleton

Design of a neural network-based PID controller for four-DoF Lower Limb Exoskeleton

Research and Design of Precision Fertilizer Application Control System Based on PSO-BP-PID Algorithm

China has had the highest fertilizer use rate in the world for years, but today a large number of farmlands still use traditional manual fertilizer application methods, which rely entirely on personal experience and not only cause the waste of fertilizer and water resources but also make the local ecological environment polluted. This paper researches and designs a BP neural network PID controller based on PSO optimization to address the above problems. The PSO algorithm is used to optimize the initial weights of the BP neural network, and then optimize the control parameters of the PID to achieve accurate control of the liquid fertilizer flow. A precision fertilizer control system based on the STM32 microcontroller was also developed, and the performance of this controller was verified in tests. The results showed that compared with the conventional PID controller and BP neural network-based PID controller, this controller had good control accuracy and robustness, the average maximum overshoot was 6.35%, and the average regulation time was 41.17 s; when the fertilizer application flow rate was 0.6 m3/h, the shortest adjustment time is 30.85 s, which achieves the effect of precise fertilizer application.

The Brushless DC motor control system Based on neural network fuzzy PID control of power electronics technology

In order to improve The Brushless DC motor control system Based on Particle Swarm Optimization Algorithm with Improved Inertia Weights development of power electronics technology emergence, The brushless director current (DC) motor is a new type of mechatronic motor that has been developed rapidly with the development of power electronics technology, The performance of the brushless DC motor control system, this paper starts with the brushless DC motor to improve the motor structure or use new materials. This paper uses the neural network fuzzy PID control method to carry out the study of the brushless DC motor control system, and chooses the correct control model and control target to solve the influence of the commutation process on the system. Moreover, this paper analyzes and designs the application of model prediction in BLDCM current loop control, and uses this control system to build a simulation model of brushless DC motor control system in MATLAB. The simulation results show that the brushless DC motor control system based on neural network fuzzy PID control can effectively improve the control effect.

Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Considering the contradiction among the response speed, overshoot and stability of system when the motor load system adopts PID control, a control strategy combining RBF (Radial Basis Function) neural network supervisory control and expert PID control is designed to effectively improve this problem in this paper. First of all, the related algorithms of RBF neural network supervisory control composed of RBF neural network and PID control (RSC-PID) is introduced. This method can make the motor load system reach a steady state faster than simple PID control. But RSC-PID is also unsatisfactory in terms of overshoot. Based on the RSC-PID control method, a hybrid controller combining RBF neural network supervisory control and expert PID control (RSC-EPID) is proposed. This method combines RSC-PID control theory with expert PID control ideas, further improves the stability and rapidity of the system, reduces the overshoot. Moreover, when the input signal is a time-varying signal with interference, the motor load system shows better anti-interference performance after using RSC-EPID. The simulation results show that RSC-EPID control improves the tracking effect of the output signal of the motor load system, ensures the stability of the system, and improves the performance of the system.

Research on Speed Control of Three-Phase Alternating Current Asynchronous Motor Based on Back Propagation Neural Network Tuning Proportion Integration Differentiation Control

In order to optimize the speed control effect of three-phase AC asynchronous motor, the speed control method of three-phase AC asynchronous motor based on BP neural network tuning PID control is studied. The mathematical model of the three-phase AC asynchronous motor is constructed. After the three-phase AC asynchronous motor is transformed into a computer analytical calculation mode in the mathematical model mode, the current operating speed of the three-phase AC asynchronous motor is measured by the speed tracking measurement method of the three-phase AC asynchronous motor. The speed control method of three-phase AC asynchronous motor based on BP neural network tuning PID control is adopted to realize the speed control of three-phase AC asynchronous motor. The experimental results show that the proposed method can accurately measure the current running speed of three-phase AC asynchronous motor and complete the speed regulation of three-phase AC asynchronous motor within 15 s. It has significant application value in similar methods.

Energy Management in Hybrid Microgrid using Artificial Neural Network, PID, and Fuzzy Logic Controllers

Microgrids are described as linking many power sources (renewable energy and traditional sources) to meet the load consumption in real-time. Because renewable energy sources are intermittent, battery storage systems are required, typically used as a backup system. Indeed, an energy management strategy (EMS) is required to govern power flows across the entire Microgrid. In recent research, various methods have been proposed for controlling the micro-grids, especially voltage and frequency control. This study introduces a microgrid system, an overview of local control in Microgrid, and an efficient EMS for effective microgrid operations using three smart controllers for optimal microgrid stability. We design the Microgrid, which is made up of renewable solar generators and wind sources, Li-ion battery storage system, backup electrical grids, and AC/DC loads, taking into account all of the functional needs of a microgrid EMS and microgrid stability. In addition, the battery energy storage is managed through the performance control of battery charging and discharging using an efficiency controller. The proposed system control is based on the optimum supply of loads through the available renewable sources and the battery State of Charge (SOC). The simulation results using Matlab Simulink show the performance of the three techniques (PID, ANN, and FL) proposed for microgrid stability.

Accuracy Improvement of a Laser Diode-Based System for Measuring the Geometric Errors of Machine Tools

Active methods are proposed to improve the measurement accuracy of a compact laser diode-based (LD-based) system, which is designed to measure the geometric errors of machine tools. The LD has some advantages, such as a small size, low cost and high efficiency. However, the laser spot of the LD is elliptical and the stability in the output power of the LD is low, which limits the accuracy of the measurement system, where the LD is used as the laser source. An active shaping method is proposed to shape the elliptical laser spot of the LD without adding additional optical elements. In addition, the laser beam drifts, including the linear drift and angular drift, are compensated in real-time by a proposed improved active error compensator, which consists of two drift feedback units and a Backpropagation Neural Networks-based PID controller, during the long-distance measurement. A series of experiments were conducted to verify the effectiveness of the proposed methods and the capability of the constructed LD-based system.

A prescribed performance PID control of robotic cars with only posture measurements considering path curvature

This paper addresses the prescribed performance adaptive neural observer-based PID controller for robotic cars by considering the trajectory curvature compensation. Constrained relative distance and angle errors between a car and leader are transformed into a new Euler-Lagrange model of unconstrained errors via prescribed performance technique. Two filtered PID error variables are introduced to propose a neural adaptive robust observer-based controller which obtains the followings: (i) the prescribed performances of tracking errors are ensured, (ii) the controller singularity is avoided, (iii) the trajectory curvature is compensated to avoid unwanted deviations between the leader and follower, (iv) car velocities measurements are not required, and (v) controller compensates uncertainties by combining neural networks and adaptive robust control. Lyapunov's method proves the controller stability. Comparative simulations verify controller advantages.

Simulation and Experimental Study of Nonlinear Characteristics for Multi-mode Driving of Intelligent Vehicles

To study the nonlinear characteristics simulation and experiment of multimode driving of intelligent vehicle, an improved neural network PID controller is proposed to realize the safe and active braking requirements of cruise vehicles, according to the study of vehicle dynamics of different active braking forms and the joint simulation of road condition, driving condition, and braking mechanical structure. On the basis of the current research, the four-layer neural network combined with PID control is proposed in this paper, which can better control the braking compared with the traditional PID controller and BP neural network, safety, efficiency and stability have been improved in the following cars. According to the animation simulation demonstration of CAR SIM software, it can be determined that the improved neural network PID controller in the mechanical system can realize the safety and active braking requirements of cruise vehicle, and it is more accurate and efficient than the traditional BP neural network PID controller. Relevant parameters of the existing millimeter-wave radars are set by the radar sensors, and the long-distance horizontal detection range is plus or minus 10 degrees. The radar is installed on the longitudinal axis of the front bumper and has a height of 442[Formula: see text]mm. According to Car SIM’s preset radar sensor module parameters, the sensors can independently collect and transmit relevant data, it is also possible to cooperate with other sensors for the same variable parameter to increase the accuracy and avoid abnormal errors caused by the failure of a certain sensor. Compared with the traditional PID controller and BP neural network, the combination of four-layer neural network and PID control can carry out better braking control and improve the safety, efficiency, and stability of the following cars.