Optimal PID Controller Parameters Research Articles

Optimization of PID control parameters for marine dual-fuel engine using improved particle swarm algorithm

This study presents a comprehensive investigation into the optimization of PID control parameters for marine dual-fuel engines using an improved particle swarm algorithm. Through the development of a Matlab/Simulink simulation model, the thermodynamic behavior of the engine and the functionality of its control system are analyzed. The PID control parameters for air–fuel ratio control and mode switching control systems are fine-tuned utilizing the improved particle swarm algorithm (PSO). Simulation results demonstrate that the proposed improved PID-PSO approach outperforms traditional PID and traditional PSO-PID control methods in terms of reduced overshoot, minimized steady-state error, faster response times, and improved stability across various operating conditions and response modes. In comparison to traditional PID and PSO-PID controllers, the improved PSO-PID controller reduces the response time by 0.47 s and 0.21 s, the maximum overshoot by 98.43% and 96.05%, and decreases the absolute errors by 87.42% and 90.55%, respectively, in air–fuel ratio control using the step response method. The study's findings offer valuable insights into enhancing the performance and efficiency of marine dual-fuel engines through advanced control strategies.

Analysis of control algorithms for water quality testing robots

The research background of the water quality detection robot lies in effectively monitoring and assessing the quality of water bodies to address the increasingly severe issue of water pollution. This technology, employing advanced sensors and machine learning algorithms, facilitates automated acquisition of water quality data, enabling real-time analysis and reporting of various indicators in the water bodies. Consequently, it provides crucial evidence for safeguarding the environment and human health. This article investigates the problem of posture control in water quality detection robots operating in complex underwater environments. It compares three control algorithms: the traditional PID fuzzy control algorithm, and a BP neural network-based PID algorithm. Through experimental simulation, these algorithms are evaluated based on their differences in system dynamic performance. The results indicate that the utilization of the BP neural network PID algorithm significantly enhances both stability and control precision of the robot when dealing with non-linear environments characterized by uncertainties. This algorithm combines traditional PID control with neural network technology, allowing for adaptive optimization of PID controller parameters through training the neural network. As a result, it overcomes limitations associated with traditional PID and fuzzy control algorithms while improving overall performance.

Parameter optimization of nonlinear PID controller using RBF neural network for continuous stirred tank reactor

The temperature system of the Continuous Stirred Tank Reactor (CSTR) has the characteristics of strong nonlinearity and uncertain parameters. The linear PID controller makes it difficult to meet CSTR’s control requirements. Nonlinear PID (NPID) can improve the control effect of nonlinear controlled objects, but due to the influence of nonlinear function selection and manual parameter setting, when parameters are uncertain or subject to external interference, the control performance of the system will decrease. To improve the adaptive capability of the NPID controller, the RBF-NPID control algorithm is proposed. The learning ability of RBF neural network is used to adjust NPID parameters online to improve the control performance of the system. In order to verify the effectiveness of the proposed algorithm, a CSTR model was established in MATLAB and algorithm comparison research was carried out. Simulation results show the effectiveness and superiority of the proposed algorithm.

Applying Cognitive Methodology in Designing On-Line Auto-Tuning Robust PID Controller for the Real Heating System

A novel design and implementation of a cognitive methodology for the on-line auto-tuning robust PID controller in a real heating system is presented in this paper. The aim of the proposed work is to construct a cognitive control methodology that gives optimal control signal to the heating system, which achieve the following objectives: fast and precise search efficiency in finding the on- line optimal PID controller parameters in order to find the optimal output temperature response for the heating system. The cognitive methodology (CM) consists of three engines: breeding engine based Routh-Hurwitz criterion stability, search engine based particle swarm optimization (PSO) and aggregation knowledge engine based cultural algorithm (CA). Matlab simulation package is used to carry out the proposed methodology that finds and tunes the optimal values of the robust PID parameters on-line. In real-time, the LabVIEW package is guided to design the on-line robust PID controller for the heating system. Numerical simulations and experimental results are compared with each other and showed the effectiveness of the proposed control methodology in terms of fast and smooth dynamic response for the heating system, especially when the control methodology considers the external disturbance attenuation problem.

Parameter optimization of PID controller based on an enhanced whale optimization algorithm for AVR system

Parameter optimization of PID controller based on an enhanced whale optimization algorithm for AVR system

PID parameter tuning based on improved particle swarm optimization algorithm

To address the shortcomings of the particle swarm algorithm in solving the PID controller parameter optimization problem in the late stage, an improved particle swarm algorithm is proposed for solving the problem. The particle swarm algorithm is optimized by introducing a nonlinear decreasing inertia weight adjustment strategy so that the pre-flight speed of the particles does not drop too fast, thus balancing the global search ability of the particles with the local search ability and avoiding falling into a local optimum. Simulation experiments were conducted in Matlab/Simulink using the standard PSO, ZN formula method, and the improved PSO, respectively. The results show that the improved algorithm enhances the stability, convergence speed, and search accuracy of the PID parameters with better performance indicators.

Optimization of PID Controller Parameters for Speed Control of DC Motor Using Firefly and Fminsearch Algorithms

Optimization of PID Controller Parameters for Speed Control of DC Motor Using Firefly and Fminsearch Algorithms

Optimization of Hydraulic Fine Blanking Press Control System Based on System Identification

Fine-blanking is a molding process based on the common blanking process, which obtains hydrostatic stress through blank holder reverse jacking, in order to increase material plasticity. It requires special equipment, namely a fine-blanking press, to complete the fine-blanking process. In this paper, the problem of the speed of the slide block fluctuation found in the actual use of a 12,000 kN hydraulic fine-blanking press after multi-stage pressure source optimization is studied. Firstly, the mathematical model of the motion of the slide block in the blanking stage of the hydraulic fine blanking press is established, and the accurate mathematical model in the blanking stage of the hydraulic fine-blanking press is obtained through the least square method system identification experiment. Aiming at the complex working situation of the fine-blanking press, a phased PID control strategy is creatively proposed. The optimal PID control parameters are obtained by a genetic algorithm, and established a fuzzy PID controller for the blanking stage to accurately control the movement speed of the slide block. The results show that the new control strategy is very effective in improving the movement accuracy of the slide block, effectively improving the machining accuracy and reducing the impact vibration of the hydraulic system.

Robust PID optimal tuning of a Delta parallel robot based on a hybrid optimization algorithm of particle swarm optimization and differential evolution

AbstractIn this paper, we propose an approach to tune optimal parameters of a robust PID controller by means of computed torque control (CTC) strategy for trajectory tracking of a Delta parallel robot, using a hybrid optimization algorithm of Particle Swarm Optimization (PSO) and differential evolution (DE). It differs from previous works that they propose robust PID controller parameters tuning based on conventional gradient-based optimization algorithms and apply them to process control. First, we reduce the tuning problem of a robust PID controller with CTC strategy satisfying requirements including robustness and disturbance attenuation to an optimization problem with nonlinear constraints by considering the nonlinear dynamic model of a Delta parallel robot. Second, we set up the design characteristics for the trajectory tracking of a Delta parallel robot. Then, we propose a robust PID controller in a way of obtaining the global optimization solution of the nonlinear optimization problem by running a PSO-DE hybrid optimization algorithm of finding the global optimal solution by maintaining the diversity of swarm during evolution based on the evolution of cognitive experience. Simulation and experimental results demonstrate that the proposed controller outperforms previous works with respect to robust performance and active disturbance attenuation when it is applied to tracking control of a Delta parallel robot.

Parameter Optimization of PID Controller Based on Improved Beetle Herd Algorithm

The PID controller, although used in 95% of the loop operations in the automation field for practical control, still has a lot of shortcomings such as nonlinearity, hysteresis, and time-varying uncertainty, resulting in unsatisfactory control effects. To solve this problem, this paper proposes an improved beetle herd algorithm for PID parameter optimization. With the combination of the group collaborative optimization ability of particle swarm optimization, the global search and local search capability is strengthened through the test of the error-index ITAE and the optimized PID parameters. Finally, with the help of the PID controller simulation, the effectiveness of the algorithm is verified by comparing the effects of the four algorithms before and after the improvement.

Nonlinear PID Controller Parameters Optimization Using Improved Particle Swarm Optimization Algorithm for the CNC System

In this paper, a nonlinear PID (NLPID) controller is used to replace a traditional PID controller to overcome the influence of nonlinear factors in the computer numerical control (CNC) system. A particle swarm optimization based on a generalized opposition-based learning (G-PSO) algorithm is proposed to optimize the NLPID controller. The convergence speed and global optimization ability of the particle swarm optimization (PSO) algorithm are improved by introducing generalized opposition-based learning. The natural selection mutation is introduced into the G-PSO algorithm to further avoid the particles falling into local optimization. Different from the existing research, this paper designs a special fitness function according to the control objectives of improving system response speed and suppressing overshoot. By comparing the differential evolution (DE) algorithm, the ant lion optimizer (ALO) and the genetic algorithm (GA) through simulation, it is proven that the G-PSO algorithm has a faster convergence speed and better global optimization ability. Compared to Fuzzy PID and MRAC PID, G-PSO NLPID is shown to be more suitable for CNC systems. Additionally, it is proven through experiments that the rise time and settling time of the NLPID controller optimized by the G-PSO algorithm are 22.22% and 24.52% faster, respectively, than the traditional PID controller, and the system overshoot is successfully suppressed.

Self-Optimizing Path Tracking Controller for Intelligent Vehicles Based on Reinforcement Learning

The path tracking control system is a crucial component for autonomous vehicles; it is challenging to realize accurate tracking control when approaching a wide range of uncertain situations and dynamic environments, particularly when such control must perform as well as, or better than, human drivers. While many methods provide state-of-the-art tracking performance, they tend to emphasize constant PID control parameters, calibrated by human experience, to improve tracking accuracy. A detailed analysis shows that PID controllers inefficiently reduce the lateral error under various conditions, such as complex trajectories and variable speed. In addition, intelligent driving vehicles are highly non-linear objects, and high-fidelity models are unavailable in most autonomous systems. As for the model-based controller (MPC or LQR), the complex modeling process may increase the computational burden. With that in mind, a self-optimizing, path tracking controller structure, based on reinforcement learning, is proposed. For the lateral control of the vehicle, a steering method based on the fusion of the reinforcement learning and traditional PID controllers is designed to adapt to various tracking scenarios. According to the pre-defined path geometry and the real-time status of the vehicle, the interactive learning mechanism, based on an RL framework (actor–critic—a symmetric network structure), can realize the online optimization of PID control parameters in order to better deal with the tracking error under complex trajectories and dynamic changes of vehicle model parameters. The adaptive performance of velocity changes was also considered in the tracking process. The proposed controlling approach was tested in different path tracking scenarios, both the driving simulator platforms and on-site vehicle experiments have verified the effects of our proposed self-optimizing controller. The results show that the approach can adaptively change the weights of PID to maintain a tracking error (simulation: within ±0.071 m; realistic vehicle: within ±0.272 m) and steering wheel vibration standard deviations (simulation: within ±0.04°; realistic vehicle: within ±80.69°); additionally, it can adapt to high-speed simulation scenarios (the maximum speed is above 100 km/h and the average speed through curves is 63–76 km/h).

PID Parameter Tuning for Buck Controllers Based on an Improved Differential Evolution Algorithm

With the wide application of Buck converters, it is of great significance to select the optimal PID controller parameters to ensure the dynamic and steady-state performance of the Buck converters. On this basis, a PID parameter tuning method based on an improved differential evolution algorithm is proposed. Also, a crossover approach with population generation across orders of magnitude, elastic boundary absorption, and retention of some combined features is put forward. In addition, an adaptive mutation factor, and a cost function that suppresses overshoot and is sensitive to the tuning time are designed. The results of 10 tests show that the differential evolution algorithm fails in the case of parameters in a range cross orders of magnitude, while the algorithm proposed in this paper enables a better combination of PID parameters selected in terms of speed and stability, with a wide search space and stable convergence results.

A new artificial ecosystem-based optimization integrated with Nelder-Mead method for PID controller design of buck converter

Over the last decade, there has been a constant development in control techniques for DC-DC power converters which can be classified as linear and nonlinear. Researchers focus on obtaining maximum efficiency using linear control techniques to avoid complexity although nonlinear control techniques may achieve full dynamic capabilities of the converter. This paper has a similar purpose in which a novel hybrid metaheuristic optimization algorithm (AEONM) is proposed to design an optimal PID controller for DC-DC buck converter’s output voltage regulation. The AEONM employs artificial ecosystem-based optimization (AEO) algorithm with Nelder-Mead (NM) simplex method to ensure optimal PID controller parameters are efficiently tuned to control output voltage of the buck converter. Initial evaluations are performed on benchmark functions. Then, the performance of AEONM-based PID is validated through comparative results of statistical boxplot, non-parametric test, transient response, frequency response, time-domain integral-error-performance indices, disturbance rejection and robustness using AEO, particle swarm optimization and differential evolution. A comparative performance analysis of transient and frequency responses is also performed against simulated annealing, whale optimization and genetic algorithms for further performance assessment. The comparisons have shown the proposed hybrid AEONM algorithm to be superior in terms of enhancing the buck converter’s transient and frequency responses.

A self‐adaptive SAC‐PID control approach based on reinforcement learning for mobile robots

AbstractProportional–integral–derivative (PID) control is the most widely used in industrial control, robot control, and other fields. However, traditional PID control is not competent when the system cannot be accurately modeled and the operating environment is variable in real time. To tackle these problems, we propose a self‐adaptive model‐free SAC‐PID control approach based on reinforcement learning for automatic control of mobile robots. A new hierarchical structure is developed, which includes the upper controller based on soft actor‐critic (SAC), one of the most competitive continuous control algorithms, and the lower controller based on incremental PID controller. SAC receives the dynamic information of the mobile robot as input and simultaneously outputs the optimal parameters of incremental PID controllers to compensate for the error between the path and the mobile robot in real time. In addition, the combination of 24‐neighborhood method and polynomial fitting is developed to improve the adaptability of SAC‐PID control method to complex environment. The effectiveness of the SAC‐PID control method is verified with several different difficulty paths both on Gazebo and real mecanum mobile robot. Furthermore, compared with fuzzy PID control, the SAC‐PID method has merits of strong robustness, generalization, and real‐time performance.

Optimization of PID Controller Parameters for Automatic Generation Control in Two-Area Heating System Using Firefly Algorithm

Optimization of PID Controller Parameters for Automatic Generation Control in Two-Area Heating System Using Firefly Algorithm

Modeling, Analysis and Experimental Validation of the Fuel Cell Association with DC-DC Power Converters with Robust and Anti-Windup PID Controller Design

Based on full and rigorous study, this paper addresses the issue of ensuring a feasible association in practice between a Proton Exchange Membrane Fuel Cell (PEMFC) and DC-DC power converters including a buck and boost converter. This association is mathematically modeled, analyzed, and controlled by an optimal PID controller. Using absolute stability tools with a rigorous analytical approach that takes into consideration windup effects, in addition to the nonlinear behavior of the Proton Exchange Membrane Fuel Cell, sufficient conditions are provided to ensure that the closed-loop system is L2-stable. These conditions allow the optimal PID controller parameters to be chosen and ensure the closed-loop system stability, by tracking reference outputs, and an optimal performance against perturbations. Formal analysis, numerical simulations, and experimental validation were carried out to verify that the PID controller designed with an anti-windup action is robust and meets all of the pre-defined objectives. Moreover, this study demonstrates that the association between the PEMFC and the DC-DC converters is achieved if only certain conditions are met.

基于粒子群优化算法对离散PID控制器参数的优化研究

基于粒子群优化算法对离散PID控制器参数的优化研究

A Fractional Order Control Strategy for LFC via Big Bang Big Crunch & Grey Wolf Optimization Algorithms

The Grey Wolf Optimization (GWO) is well known meta-heuristic algorithm and has been previously used for optimization of various conventional PID and FOPID controllers. This paper deals with the application of Grey Wolf Optimizer (GWO) algorithm and internal model control (IMC) for optimization of fractional order PID (FOPID) controller parameters to the load disturbance of system. This is applicable for one (or) single area non reheated electrical system. The simulation results are compared with the non re-heated Big Bang Big Crunch (BBBC) optimization outputs. In this paper, BBBC optimization has the two bounding cases (lower & upper), they are before and after the perturbation cases. Also it is observed that in case of the BBBC output responses, the settling time value of load frequency is more when compared with the GWO. From the simulation results it is concluded that GWO out performs as compared with BBBC as it produces less error and settling time.

Fractional Order PID Controller Design for Multivariable Systems using TLBO

Abstract The multivariable systems have to control by using multiloop controllers and each closed loop controller has unique characteristics. The successful model structure for design of control system is extremely subject to the accurate choice of the tuning parameters ( K_p, K_i, K_d, \lambda, \mu ) of the controller. The choice of optimal tuning parameters of Fractional Order PID (FOPID) controller leads to accurate controlling of desired level in multivariable system. Here, for multivariable system, a FOPID controller design based on the advanced optimization technique called Teaching Learning based optimization (TLBO) algorithm is proposed. The goal of paper is (i) The elimination of interaction between the control loops and (ii) Reference tracking along the disturbance in each loop. These objectives are satisfied by using four cost function, namely, integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE) and integral time square error (ITSE). Out of these cost functions, ITAE based FOPID controller design using TLBO algorithm provides better performance in terms of fast reference tracking and disturbance elimination in the loop. Moreover, the comparative analysis of convergence characteristics of each objective of the controller by using TLBO is presented. The simulation study confirms that the TLBO algorithm based FOPID controller for multivariable systems (2 × 2) are more robust and exhibits superior response with respect to other algorithm.