Analysis of the PID Controller Parameters for A Surface Mobility Platform Mobile Robot

Researching optimization problems of controller is need of industrial process control ,in which PID controller is widely used,which its parameters can be equivalent to optimization problems. In the industrial control the PID controller is widely used in excitation control system to improve its control performance.In order to find the optimal PID controller parameters effectively ,a kind of Adaptive Particle Swarm Optimization method (CAPSO) based on Cloud Theory is applied to fuzzy PID controller. Through the establishment of particle swarm algorithm of fuzzy PID controller parameters optimization model, which it can be used to optimize the membership function of fuzzy PID controller. Particle code adopts real coding. Particle dimension is related with the number of the input variables divided by fuzzy set and the number of control rules of whole fuzzy control system, thus the parameters of PID control are optimized on real time. The result from the simulation shows that compared with PID control and fuzzy control this system has several advantages which are small overshoots ,fast response ,and good stable performance to improve the control performance of excitation control system.

This paper presents a novel design using fuzzy logic control and phase-locked loops to obtain a DC motor speed control system with excellent regulation and high robustness. The fuzzy logic controller is incorporated in order to achieve quick control of motor speed smoothly. The fuzzy logic controller enhances the robustness of the motor control system, which can handle abrupt load variation and exhibits good disturbance behavior. The PLL becomes effective at steady-state conditions when the speed error is small. A control scheme using fuzzy control of DC motor speed with extremely accuracy provided by PLL is implemented. Simulation demonstrates the effectiveness of the proposed scheme. Test results show that a system which consists of both fuzzy control and PLL can yield a high performance DC motor speed control system.

Experience Mapping based Prediction Controller (EMPC) is a recently proposed controller for DC motor based position control, whose design is inspired from human motor control. As it is developed for a Type-1 system, the algorithms developed there, cannot be directly used for Type-0 systems. This paper presents the modified EMPC for Type-0 systems and the principle is applied to a DC motor speed control system. EMPC uses past experience for generating the control action in each scenario and it works in quasi-open-loop unlike conventional control systems which work in tight close loop. The paper presents the details of development of EMPC algorithm and simulations to show its performance on a DC motor based speed controller. The performance of EMPC is compared with that of a conventional PID controller for different scenarios. The speed control of DC motor using EMPC is practically implemented and the experimental results are presented.

The traditional DC double-loop speed control can not effectively overcome non-linear factors; it can not meet the needs of high-precision, high-performance requirements. This paper shows that the Fuzzy Self-Tuning PID Control strategy has been used in DC motor speed control systems to achieve real-time tuning PID parameters and introduces the entire design process of the controller. When referencing an actual motor parameter model a simulation model established by Matlab / Simulink, simulation results show that the Fuzzy Self-Tuning PID Controller is superior to the traditional PID control with its accuracy and robustness, thus enhancing its motor dynamics and static performance. From this it is seen that simulation results are concurrent with theoretical research, by verifying the reasonableness of program design, while it achieves an optimum control of control objectives.

PID controller is widely used in industry field, but when the industry presence exists noise interference, it is difficult for the conventional PID controller to achieve the expected control effect, in view of this situation, the method of combinating Kalman filter with the PID control is studied in this paper, the DC motor speed control system is simulated, the results shows that the PID controller based on Kalman filter is better to inhibit the effect of white noise, and it has a good dynamic response characteristics.

The wheeled mobile robots have been widely applied in daily applications for its simplicity, robustness, stability and low-cost of manufacturing. Typically, PID controller is implemented to drive a mobile robot. However, the performance of the robot is greatly depended on the PID control parameters are tuned and the robustness of the mechanical system. Since the analog controllers were replaced by digital controller, PID control parameters could be automatically tuned to adapt the variation of the system parameters and operating condition. In this manuscript, a mathematical model is developed for a line following mobile robotic system which is used as a restaurant serving robot. The serving robot is controlled by an adaptive PID controller using Q-learning algorithm. The simulation and experimental results are compared to verify the advantage of the adaptive PID controller over classical PID controller.

The stability of the system is the main objective for Control System Design criteria. PID control with closed loop system is the one of control which can improve the stability of the system from disturbance effect. In this study, DC Motor Speed Control is designed with PID LabVIEW 2010 Software and Proximity Sensor as a closed loop feedback. The system allows the operator to control speed of DC Motor from LabVIEW front panel which automatically stabilized when load ?which usually slow down the speed of DC Motor, applied. PID LabVIEW 2010 generates voltage output 0-5Vdc and drives DC Motor through analog output channel from NIDAQ 600, which received by Non-Inverting Amplifier and amplified to 0-12Vdc output. PID Control with Closed Loop system may dampen the speed response second order of DC Motor to have value of overshoot number ? 0 with steady state error of ?2%. The system feedback can stabilize this condition from outside interference. The result of this speed control system has success criteria of overshoot (Mp) = 0 and Error Steady State (Ess) = 0.82%.

This thesis, subject to the obstacles exist throughout the process of tuning and optimization of the prevailing DC Double close-loop PID controller parameters, explains to realize the tuning and the optimization of PID controller parameters by introducing the Artificial Bee Colony Algorithm on the basis of Nectar Collecting Behavior to the DC Double close-loop tuning system. The comparison and the analysis, processed by step response between the consequences of the Artificial Bee Colony Algorithm and Engineering Methods, indicate that the improved Artificial Bee Colony Algorithm can help us obtain a better dynamic performance index, and a faster following behaviors and robustness, due to which a practical and more comprehensive method to optimizing PID parameters is discovered;.

In this paper, a trajectory tracking control for a non-holonomic differential wheeled mobile robot (WMR) system is presented. A big number of investigations have been used the kinematic model of mobile robot which is a nonlinear model in nature, thus a hard task to control it. This work focuses on the design of fuzzy PID controller tuned with a firefly optimization algorithm for the kinematic model of mobile robot. The firefly optimization algorithm has been used to find the best values of controller's parameters. The aim of this controller is trying to force the mobile robot tracking a pre-defined continuous path with the least possible value of error. Matlab Simulation results show that a good performance and robustness of the controller. This is confirmed by the value of minimized tracking error and the smooth velocity especially concerning presence of external disturbance or change in initial position of mobile robot.

In this paper, a trajectory tracking control for a non-holonomic differential wheeled mobile robot (WMR) system is presented. A big number of investigations have been used the kinematic model of mobile robot which is a nonlinear model in nature, thus a hard task to control it. This work focuses on the design of fuzzy PID controller tuned with a firefly optimization algorithm for the kinematic model of mobile robot. The firefly optimization algorithm has been used to find the best values of controller's parameters. The aim of this controller is trying to force the mobile robot tracking a pre-defined continuous path with the least possible value of error. Matlab Simulation results show that a good performance and robustness of the controller. This is confirmed by the value of minimized tracking error and the smooth velocity especially concerning presence of external disturbance or change in initial position of mobile robot.

The speed sensorless control system on a DC motor is a DC motor speed control system that does not use a speed sensor to measure the motor speed. The motor speed value is estimated by an observer from the stator current and voltage that are measured using a sensor. This study uses an R observer method. The difference between the estimated speed and the reference speed is then used by the PID controller to adjust the motor speed to match the desired reference speed. PID parameter tuning using heuristic method. With a setpoint of 6800 RPM and using a combination of Kp = 0.5, Ki = 0.05, and Kd = 0.34, a speed value of 6792.76 RPM was obtained. Sensorless motor speed control using a PID controller produces an optimal system with a low Steady State Error (SSE) value of around 0.1%, very small oscillations of 0.39%, a fast rise time of 4 seconds, and a fast settling time of 6 seconds.

This paper according to the traditional PID control system of the nonlinear time-varying, is not easy to realize the on-line adjustment of the flaw and proposed one kind based on the FPGA (Field Programmable Gate Array), implementation of fuzzy PID control algorithm and its application to brushless DC motor speed control system. Using VHDL (Very High Speed Integrated Circuit HardwareDescription Language) language and Quartus II built-in macro module on the PID modules are constructed. In the construction of the PID module based on fuzzy control theory to join. The application of Matlab system simulation tools and Quartus II waveform simulator to construct fuzzy PID module simulation, and the application of fuzzy PID module on brushless DC motor speed control, then make whole speed control system is more reliable. The simulation results show that: based on FPGA realizing fuzzy PID control system has the advantages of simple structure, easy modification and can make the system reach the speed more stable results.

A new method for optimizing the fuzzy PID controller, based on an artificial electric field algorithm (AEFA), is proposed in this paper, aiming at improving the stability indicator of the Brushless DC (BLDC) motor for the small satellite attitude control flywheel. The BLDC motor is the basic part of the small satellite attitude control flywheel. In order to accurately control the attitude of the small satellite, a good motor control system is very important. Firstly, the mathematical model of the BLDC motor is established and the BLDC motor speed control system using traditional PID control is designed. Secondly, considering that the small satellite speed control system is a nonlinear system, a fuzzy PID control is designed to solve the shortcomings of the fixed parameters of the traditional PID control. Finally, we find that the control accuracy of the fuzzy PID control will change with the range of the input. Therefore, we introduce the AEFA to optimize fuzzy PID to achieve high-precision attitude control of small satellites. By simulating the BLDC motor system, the proposed fuzzy PID controller based on AEFA is compared with the traditional PID controller and the fuzzy PID controller. Results from different controllers show that the proposed control method could effectively reduce steady state error. In addition, the proposed fuzzy PID–AEFA controller has the better anti-jamming capability.

In the process of LED chip coating, the steady flow at the spray gun in a unit time has a significant impact on the final luminous effect. Traditional PID control is difficult to control the flow accurately due to the nonlinearity of phosphor powder. In order to improve the luminous quality, a neural network PID control method based on wolf pack algorithm is proposed(WPA-BPNN-PID) in this paper. First, the weights and thresholds of BP neural network are optimized by using the wolf pack algorithm to improve the training speed and prediction accuracy. Second, the optimal PID control parameters are searched online by using the optimized BP neural network. Finally, the traditional PID control algorithm, neural network PID control algorithm and WPA-BPNN-PID algorithm are simulated by MATLAB simulation software. The experimental results show that the proposed WPA-BPNN-PID algorithm has better dynamic and static performance, anti-interference ability, and thus can further accurately control the colloid flow of phosphor powder in unit time.

Because of their effectiveness, simplicity, and polyvalence, PID controllers are widely used in industrial applications. Within the field of electric motors, namely the brushless direct current motors (BLDC). To maximize the performance of BLDC motors, which are known for their dependability and efficiency, precise speed control is essential. PID must be adjusted each time they are used in an application in order to maximize performance. In this study, we presented a comparison between two PID controllers for Brushless DC motors (BLDC). The first suggested PID controller is based on the Ziegler-Nichols method, a well-known empirical adjustment and testing strategy, resulting in under optimal performance in the presence of optimum local solutions for large research spaces. The fuzzy inference system is used to adjust the controller parameters in the second PID controller. The performance with the proposed Zigler-PID and fuzzy- PID controllers is analyzed and compared using the developed MATLAB/simulink model. These results demonstrate the effectiveness of Fuzzy –PID in improving the precision of speed control and energy efficiency.