PSO-PID Controller Research Articles

Tuning optimal PID controller

In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters for nonlinear multiple-input multiple-output (MIMO) system using the particle swarm optimisation (PSO) algorithm is presented. Firstly, a nonlinear system was described based on Takagi-Sugeno (T-S) fuzzy models. Assuming that the antecedent parameters of T-S models were kept, the consequent parameters were identified online by using the weighted recursive least square (WRLS) method. Secondly, the identified parameters of fuzzy model were used to directly receive the model predicted output with direct iterative for the T-S model. The fast tuning of optimum PID controller parameters yields high-quality solution. In order to assist estimating the performance of the proposed PSO-PID controller, a new time-domain performance criterion function was also defined. Finally, the application results for continuous stirred tank reactor (CSTR) process show that the proposed algorithm is an effective control strategy with excellent tracing ability.

A Comparative Study of the Z-N, Adaptation Law and PSO Methods of Tuning the PID Controller of a Synchronous Machine

A proportional–integral–derivative (PID)-based power system stabilizer (PSS) was designed to damp low-frequency oscillations in power systems under different operating conditions. The parameters of the PID controller were tuned using a particle swarm optimization (PSO) algorithm. The performance of the PSO-PID-type PSS was thoroughly investigated and compared with those of the Ziegler–Nichols (Z-N) and adaptation law methods. The effectiveness of the PSO-PID with PSS is applied to a single-machine infinite-bus system connected to a non-linear load with different kinds of faults. The variations in the speed deviation, rotor angle and load angle were compared in a Simulink based MATLAB environment. The damping performance of the PSO-PID controller over a wide range of operating conditions is excellent.

Design of PID Controller for Maglev System Based on an Improved PSO with Mixed Inertia Weight

A Maglev system was modeled by the exact feedback linearization to achieve two same linear subsystems. The proportional-integral-differential controllers (PID) based on particle swarm optimization (PSO) algorithm with four different inertia weights were then used to regulate both linear subsystems. These different inertia weights were Fixed Inertia Weight (FIW), Linear Descend Inertia Weight (LIW), Linear Differential Descend Inertia Weight (LDW), and mixed inertia weight (FIW–LIW-LDW). On the other hand, the parameters of the PSO-PID controllers via mixed inertia weight (FIW–LIW-LDW) were optimized, the parameter values in the electromagnet 1 and electromagnet 2 were both 0.4. Simulation results demonstrate that the control performance and robustness of PSO-PID based on mixed inertia weight (FIW–LIW-LDW) was superior to that of three PSO-PID controllers based on single inertia weights. For electromagnet 1, the overshoot of PSO-PID controller with mixed inertia weight reduced 3.36% than that of PSO-PID controller with FIW, 5.81% than that of PSO-PID controller with LIW, and 6.34% than that of PSO-PID controller with LDW; for electromagnet 2, the overshoot of PSO-PID controller with mixed inertia weight reduced 1.07% than that of PSO-PID controller with FIW, 12.56% than that of PSO-PID controller with LIW, 7.97% than that of PSO-PID controller with LDW; the adjusting time of PSO-PID controller with mixed inertia weight reduced 0.395s than that of PSO-PID controller with FIW, 34.1s than that of PSO-PID controller with LIW, and 33.494s than that of PSO-PID controller with LDW

采用双闭环PID控制及PSO算法的双足轮式倒立摆控制系统

为了解决双足轮式倒立摆的稳定控制问题，提出一种采用双闭环PID的控制器及粒子群优化算法(Particle swarm optimization, PSO)的双足轮式倒立摆控制系统，并设计与控制效果相关的代价函数，以增加控制系统的稳定性，在matlab仿真条件下，实现了双足轮式倒立摆的稳定控制，实验结果表明，经过整定的控制器参数较原参数的阶跃响应更好，上升时间和稳定时间较未整定PID控制器的短，并且本算法已经应用到基于面阵CCD的直立式智能搜索识别采样系统中，验证了PSO-PID控制器的可用性。In order to solve the feet-wheeled inverted pendulum’s stability control problem, a control system of a feet-wheeled inverted pendulum based on double closed-loop PID controller combined with particle swarm optimization is proposed. In addition, we designed a cost function related to the control effect to increase the stability of the control system. Under matlab simulation conditions, stable control of the feet-wheeled inverted pendulum was achieved. Experimental results showed that after tuning, the step response of the controller parameters was better than that of the original parameters with the rise time and the settling time was shorter than that of the non-tuning PID controller. This algorithm has been applied to a vertical intelligent search-identify-sampling system based on the area CCD, showing the availability of the PSO-PID controller.

PSO Based PID Controller Design for a Precise Tracking of Two-Axis Piezoelectric Micro positioning Stage

PSO Based PID Controller Design for a Precise Tracking of Two-Axis Piezoelectric Micro positioning Stage

A PSO Optimized Fuzzy Control Scheme for Mobile Robot Path Tracking

In this paper, a fuzzy controllers type Takagi_Sugeno is optimized by method of Particle Swarm Optimization (PSO). This algorithm automatically adjust the membership function of fuzzy controllers to control a trajectory of nonholonomic mobile robot that involves path trajectory using two optimized fuzzy controllers one for speed control and the other for azimuth control. The mobile robot is modelled in Simulink and PSO algorithm is implemented using MATLAB. Simulation results show good performance for the proposed control scheme. The results will compared with PSO-PID controllers that control the same model of mobile robot.

Improved Particle Swarm Optimization by Updating Constraints of PID Control for Real Time Linear Motor Positioning

This paper proposes an Improved Particle Swarm Optimization (IPSO) technique for adjusting the gains of a PID controller. The new approach introduces particle space constraints so as to improve the performance of velocity updating and position updating capability. Numerical simulations and experimental results based on PID, PSO-PID and IPSO-PID control systems are compared. Real time experimental results show that the proposed IPSO algorithm has great computational convergence and ensure the stability of the controlled system without the strict constraints on the updating velocity. Tests on the linear synchronous motor (LSM) via the digital signal Microchip (dsPIC) processor demonstrate effectiveness and robustness in positioning with disturbance excitation.

PSO-Based PID Controller Design for a Class of Stable and Unstable Systems

Nonlinear processes are very common in process industries, and designing a stabilizing controller is always preferred to maximize the production rate. In this paper, tuning of PID controller for a class of time delayed stable and unstable process models using Particle Swarm Optimization (PSO) algorithm is discussed. The dimension of the search space is only three (, , and ); hence, a fixed weight is assigned for the inertia parameter. A comparative study is presented between various inertia weights such as 0.5, 0.75, and 1. From the result, it is evident that the proposed method helps to attain better controller settings with reduced iteration number. The efficacy of the proposed scheme has been validated through a comparative study with classical controller tuning methods and heuristic methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Finally, a real-time implementation of the proposed method is carried on a nonlinear spherical tank system. From the simulation and real-time results, it is evident that the PSO algorithm performs well on the stable and unstable process models considered in this work. The PSO tuned controller offers enhanced process characteristics such as better time domain specifications, smooth reference tracking, supply disturbance rejection, and error minimization.

DTC torque ripple minimization based on PSO-PID controller

In conventional direct torque controlled (DTC) induction motor drive, there is usually undesired torque and flux ripple. The given torque is the speed output regulator; therefore, it necessary continues tuning for adjusting parameters Kp, Ki. In conventional proportional- integral (PI) speed controller, the performance of motor may differ over time that may cause unexpected torque disturbances, causing sluggishness of a control system. Tuning PID parameters are essential to DTC system to improve the performance of the system at a low speeds. In this paper particle swarm optimization (PSO) proposed to correct the parameters (Kp, Ki) of a speed controller in order to minimize torque ripple, flux ripple, and stator current distortion. Firstly, introduce brief description for conventional DTC design and PSO. Secondly, close loop speed controller in DTC for induction motor using PSO technique to provide high performance of accuracy. Finally, simulation results demonstrate that a reduction of torque and flux ripples is achieved in a whole speed range. Key words: Particle swarm optimization, PID controller, direct torque control.

Optimized proportional integral derivative (PID) controller for the exhaust temperature control of a gas turbine system using particle swarm optimization

In this paper, the particle swarm optimization (PSO) technique is used in optimising theproportional integral derivative (PID) controller parameters for the exhaust temperature control of a gas turbine system. The performance of the PID controller whose parameters are tuned based on the PSO method (PSO-PID) is compared with the conventional PID (CPID) controller that employs the Ziegler-Nichols method. A new performance criterion, known as multipurpose performance criterion (MPPC) is proposed and used in the PSO algorithm. Time domain performance of the PSO-PID controller, such as the maximum overshoot QUOTE , rise time QUOTE , settling time QUOTE and absolute error (AE) are being optimized based on the MPPC and compared with other performance criteria such as the integral of time multiplied by absolute error (ITAE), integral of time multiplied by square error (ITSE), integral square error (ISE) and integral of absolute error (IAE). Result shows that the PSO technique, combined with the MPPC performance criterion is very effective to yield optimal transient response of the gas turbine exhaust temperature. An adjustable weighting factor in the MPPC technique makes it more reliable, consistent and flexible as compared to the commonly used performance criteria. Key words: Proportional integral derivative (PID) controller, particle swarm optimization, multipurpose performance criterion, gas turbine exhaust temperature.

A Nonlinear PID Controller for Electro-Hydraulic Servo System Based on PSO Algorithm

Linear PID controller is adopted by electro-hydraulic servo system widely. The proportional, integral and derivative coefficients of a linear controller are fixed which brings the contradiction in speediness and overshoot. This paper designs a series of nonlinear functions which are used for nonlinear PID control for electro-hydraulic servo system. The proportional, integral and derivative parts of this nonlinear PID controller adjust themselves according to the error. In order to determine parameters of the nonlinear functions, PSO Algorithm and ITAE guideline are applied in the paper. Results of simulation indicate that the nonlinear PSO-PID controller using the functions proposed in this paper presents a better dynamic response than the linear PID controller.

Tuning of PID Controller Using Particle Swarm Optimization (PSO)

The aim of this research is to design a PID Controller using PSO algorithm. The model of a DC motor is used as a plant in this paper. The conventional gain tuning of PID controller (such as Ziegler-Nichols (ZN) method) usually produces a big overshoot, and therefore modern heuristics approach such as genetic algorithm (GA) and particle swarm optimization (PSO) are employed to enhance the capability of traditional techniques. However, due to the computational efficiency, only PSO will be used in this paper. The comparison between PSO-based PID (PSO-PID) performance and the ZN-PID is presented. The results show the advantage of the PID tuning using PSO-based optimization approach.

PSO-BELBIC scheme for two-coupled distillation column process

In the two-coupled distillation column process, keeping the tray temperatures within a specified range around their steady state values assures the specifications for top and bottom product purity. The two-coupled distillation column is a 4 Input/4 Output process. Normally, control engineers decouple the process into four independent loops. They assign a PID controller to control each loop. Tuning of conventional PID controllers is very difficult when the process is subject to external unknown factors. The paper proposes a Brain Emotional Learning Based Intelligent Controller (BELBIC) to replace conventional PID controllers. Moreover, the values of BELBIC and PID gains are optimized using a particle swarm optimization (PSO) technique with minimization of Integral Square Error (ISE) for all loops. The paper compares the performance of the proposed PSO-BELBICs with that of conventional PSO-PID controllers. PSO-BELBICs prove their usefulness in improving time domain behavior with keeping robustness for all loops.

CONTROL OF A SCARA ROBOT: PSO-PID APPROACH

This paper presents an approach where the Particle swarm optimization (PSO) is employed to get optimum PID controller parameters to control of a SCARA robot. PSO is an evolutionary technique which is originated from swarm intelligence. A practical application of the PSO-PID controller is performed with the system dynamics. A simulation study is also included with the proposed controller by using the systems dynamic equations with the actuator dynamics and optimum PID parameters. Different trajectories are studied. Performances of the traditional controllers; PD, PID and the application of PSO-PID controller are then compared. Simulation results are presented. The numerical results definitely enforced the potential use of PSO-PID controller herein.

A Particle Swarm Optimization Approach for Optimum Design of PID Controller in AVR System

In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an AVR system using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters of an AVR system. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. Fast tuning of optimum PID controller parameters yields high-quality solution. In order to assist estimating the performance of the proposed PSO-PID controller, a new time-domain performance criterion function was also defined. Compared with the genetic algorithm (GA), the proposed method was indeed more efficient and robust in improving the step response of an AVR system.