Fuzzy PID-based High-precision Target Tracking Control for Turret System

Research on A Fuzzy Adaptive PID Control Method for Four Rotor UAV Control System With the continuous improvement of national living standards, people have higher and higher requirements for the reliability of power supply. The emergence of unmanned aerial vehicle inspection technology provides a new idea to solve the contradiction between the increasing equipment scale and the increasing inspection requirements. However, at present, the stability of the traditional proportion integration differentiation (PID) control system of the four rotor unmanned aerial vehicle (UAV) is not high enough. During the inspection, there are accidents of drones hitting power equipment from time to time. In view of the above problems, this paper studies the fuzzy adaptive PID control method to support the stable flight of four rotor UAV. The principle of fuzzy control is analyzed. The fuzzy control method and PID control method are deeply integrated. A fuzzy adaptive PID control system is designed. The universe and membership function of the relevant control parameters of the system are formulated. The tuning rules of fuzzy adaptive PID control system are formulated. Finally, the improved control system is simulated. The simulation results show that the fuzzy adaptive PID control algorithm can effectively improve the stability, reliability and emergency ability of UAV. This method can effectively improve the adaptability of UAV to perform complex missions in complex environments. The research results have guiding significance for expanding the application field of UAV.

This paper investigates the fuzzy control method for the linear switched reluctance motor (LSRM) drive system. Modeling of the LSRM is constructed based on the finite element method (FEM) environment Maxwell and the control system is analyzed by the co-simulation environment with Simplorer. Both the performance of the fuzzy proportional integral differential (PID) controller and the traditional PID controller are compared. The simulation results prove that the fuzzy PID controller yields faster rising time and smaller overshoot, compared with the traditional PID controller.

This paper investigates the fuzzy Proportion Integration Differentiation (PID) controller for uncertain networked control systems (UNCSs). By combining T-S fuzzy model with the fuzzy PID controller, the uncertain networked control systems under transmission delays and external disturbances are proposed in this letter. Moreover, the asymptotically stability condition about the uncertain networked control systems is presented in the form of Lyapunov function and linear matrix inequality. The applicability of the proposed PID controller gain for uncertain networked control system is numerically tasted for various system parameters.

The recent development of drones has aroused great concern at home and abroad. For civil industry and agricultural plant protection industry, it can provide safer and more stable flight and improve production efficiency. For the exploration industry, the application of UAV (Unmanned Aerial Vehicle) will reduce unnecessary casualties. In places where damage is serious, UAV will bring accurate on-site information and data more quickly. It can make drones to replace people in high-risk operations, scientific and technological agricultural development and survey more stable and safe; For the military industry, it can be applied to different combat environments to increase its stability. In recent years, although UAV combat and reconnaissance are not the mainstream combat methods, UAV has become or an indispensable part. Improving the stability of UAV will also increase the military combat capability, so UAV has good development prospects and economic benefits. Nowadays, PID (Proportion Integration Differentiation) control plays an indispensable role in quad-rotor UAV. How to use different types of PID control combined with existing intelligent technology to improve the stability of quad-rotor UAV is a hot issue in recent years. This paper summarizes the previous research results in this field, briefly describes the motion mechanism of four-rotor UAV, wind disturbance resistance mechanism, the action principle and contract between traditional PID control and fuzzy PID control, as well as the effect of intelligent algorithm on PID control addition, and puts forward suggestions for PID control algorithm of the future UAV.

Fuzzy controller design of micro-unmanned helicopter relying on improved genetic optimization algorithm

Objective To design the control system of a magnetic resonance image (MRI) navigated pneumatic surgical robot. Method The driven system of the robot was constructed by combining with pneumatically driven and ultrasonic motor. The cylinder position control loop was established based on proportion integration differentiation (PID) control. The mathematical model of the pneumatic servo system was established by system identification. The PID parameter tuning and the simulation of the system characteristics were conducted. The stability analysis was conducted using the Nyquist criterion, and the acupuncture accuracy and the magnetic compatibility of the robot were verified. Results The simulation results showed that the maximum steady-state error was about 0.4 mm and the maximum tracking error was less than 2 mm, indicating that the PID control system had high control precision and trajectory tracking ability. The error of the acupuncture accuracy test was 0.72 mm. The results of water-modeling experiments showed that the magnetic resonance images were clear indicating that the influence of the robot on the MRI images was small. Conclusions The results of imulation, acupuncture and water-modeling tests showed that the pneumatic robot system can meet the requirements of brachytherapy surgery. Key words: Magnetic resonance imaging; Surgical robot; Pneumatic control system; PID control

This paper presents an optimal tuning of a proportional integral differential (PID) controller used to maintain glucose concentration at a desired set point. The PID controller synthesizes an appropriate feed rate profile for an E. coli fed-batch cultivation process. Mathematical models are developed based on dynamic mass balance equations for biomass, substrate, and product concentration of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation for bacterial phytase extracellular production. For model parameter identification and PID tuning, a hybrid metaheuristic technique—chaotic electromagnetic field optimization (CEFO)—is proposed. In the hybridization, a chaotic map is used for the generation of a new electromagnetic particle instead of the electromagnetic field optimization (EFO) search strategy. The CEFO combines the exploitation capability of the EFO algorithm and the exploration power of ten different chaotic maps. The comparison of the results with classical EFO shows the superior behaviour of the designed CEFO. An improvement of 30% of the objective function is achieved by applying CEFO. Based on the obtained mathematical models, 10 PID controllers are tuned. The simulation experiments show that the designed controllers are robust, resulting in a good control system performance. The closed-loop transient responses for the corresponding controllers are similar to the estimated models. The settling time of the control system based on the third PID controller for all estimated models is approximately 9 min and the overshoot is approximately 15%. The proposed CEFO algorithm can be considered an effective methodology for mathematical modelling and achievement of high quality and better performance of the designed closed-loop system for cultivation processes.

Humidity sensors are prone to condensation in high humidity and low temperature environments, reducing the sensor's measurement performance. Aiming at the current humidity sensor heating system that may not reach the target temperature due to open-loop control and high power consumption, a closed-loop control heating system for the humidity sensor is designed to perform real-time heating and temperature detection of the humidity-sensitive structure. The finite element analysis method is used to conduct thermal analysis on the heating structure, which obtains the temperature distribution of the sensor heating area, guides the heater structure design and gains the location distribution of temperature sensors. A variable parameter fuzzy PID (Proportion Integration Differentiation) control method with variable target temperature is proposed to perform closed-loop control on the temperature of the sensor’s sensitive structure. The heating target temperature is set as a function of the ambient temperature. Also the coefficient of PID changes with error. Thus, the power consumption of the system has been effectively reduced and the heating speed is apparently increased. Simulation experiments show that the heating system designed for controllable heating humidity sensor has uniform temperature distribution. When the ambient temperature is −70°C, the difference between upper and lower temperatures is only 0. 0878°C, with a low power consumption (5 × 106W/m3). At the same time, the overregulation of the temperature control system is low, which is 7. 227%. The system has high environmental adaptability and temperature stability.

Active queue management (AQM) can keep smaller queuing delay and higher throughput by purposefully dropping the packets at the intermediate routers. Comparing with RED algorithm, although the PID (proportional-integral-differential) controller for AQM improves the stability. It is no systematic method in selecting a group of parameters for these controllers to guarantee the transient performance, and the case is much worse especially when the network parameters are time-varying. We use generalized predictive control to propose a predictive PID (PPID) controller for AQM router supporting TCP with slightly modified ECN to improve the robustness and transient performance of the PID controller. The simulation results show that the integrated performance of the controller is obviously superior to those of PID controller.

Brushless direct current (BLDC) motor has been widely used in industrial production. Based on the method of model design, BLDC motor speed control is studied. In Matlab/Simulink, a fuzzy PID speed control model of BLDC motor is established, and C Code is generated by Real-Time Workshop (RTW) technology. After that, Code Warrior is compiled into machine Code and written into ECU. The results show that the fuzzy Proportion Integration Differentiation (PID) controller can improve the speed performance of the BLDC.

DC motor is an easy-to-apply motor but has inconsistent speed due to the existing load. PID (Proportional Integral Differential) is one of the standard controllers of DC motors. This study aimed to know the PID controller's performance in controlling the speed of a DC motor. The results showed that the PID controller could improve the error and transient response of the system response generated from DC motor speed control. Based on the obtained system response data from testing and tuning the PID parameters in controlling the speed of a DC motor, the PID controller parameters can affect the rate of a DC motor on the setpoint of 500, 1000, 1500: Kp = 0.05, Ki = 0.0198, Kd = 0.05.

The execution of the gaits generated with the help of a gait planner is a crucial task in biped locomotion. This task is to be achieved with the help of a suitable torque based controller to ensure smooth walk of the biped robot. It is important to note that the success of the developed proportion integration differentiation (PID) controller depends on the selected gains of the controller. In the present study, an attempt is made to tune the gains of the PID controller for the biped robot ascending and descending the stair case and sloping surface with the help of two non-traditional optimization algorithms, namely modified chaotic invasive weed optimization (MCIWO) and particle swarm optimization (PSO) algorithms. Once the optimal PID controllers are developed, a simulation study has been conducted in computer for obtaining the optimal tuning parameters of the controller of the biped robot. Finally, the optimal gait angles obtained by using the best controller are fed to the real biped robot and found that the biped robot has successfully negotiated the said terrains.

An electrostatically levitated gyroscope based on UV-LIGA fabrication process is introduced. The stable levitation is vitally important for the gyroscope to work efficiently. Two types of levitation control model of such a device are presented to realize initial levitation. The axial squeeze film damping coefficient is calculated by finite element analysis and deduced by analytical solution. From the analysis of the proportional integral differential (PID) control completed by the bias and the feedback linearization control (FLC) without bias. It can be seen that the PID control with the bias can linearize the control equation near the null position and FLC can realize the large travel with desired dynamic performance and global stability. But it has steady-state error, which can be switched to the PID controller to minimize. At last, the levitation control system is constructed.

The spray-paint robot is a nonlinear position control system. A self-adaptive PID (proportion-integral-differential) controller with BP (back propagation) neural network was designed for spray-paint robot. This controller took full advantages of PID control with simple arithmetic and neural network with good self-adaptive and anti-jamming ability, and by neural network's learning and on-line identification, adaptively adjusted PID control parameters that the control system could show good robustness and control performance to system parameter change. Simulation and experiments results prove that by making real-time adjustment on PID parameters, this system can obtain good tracking performance and greatly reduce the adjustment difficulty of the control system

PurposeThis study aims to realize the constant force grinding of automobile wheel hub.Design/methodology/approachA force control strategy of backstepping + proportion integration differentiation (PID) is proposed. The grinding end effector is installed on the flange of the robot. The robot controls the position and posture of the grinding end actuator and the grinding end actuator controls the grinding force output. First, the modeling and analysis of the grinding end effector are carried out, and then the backstepping + PID method is adopted to control the grinding end effector to track the expected grinding force. Finally, the feasibility of the proposed method is verified by simulation and experiment.FindingsThe simulation and experimental results show that the backstepping + PID strategy can track the expected force quickly, and improve the dynamic response performance of the system and the quality of grinding and polishing of automobile wheel hub.Research limitations/implicationsThe mathematical model is based on the pneumatic system and ideal gas, and ignores the influence of friction in the working process of the cylinder, so the mathematical model proposed in this study has certain limitations. A new control strategy is proposed, which is not only used to control the grinding force of automobile wheels, but also promotes the development of industrial control.Social implicationsThe automatic constant force grinding of automobile wheel hub is realized, and the manpower is liberated.Originality/valueFirst, the modeling and analysis of the grinding end effector are carried out, and then the backstepping + PID method is adopted to control the grinding end effector to track the expected grinding force. The nonlinear model of the system is controlled by backstepping method, and in the process, the linear system composed of errors is obtained, and then the linear system is controlled by PID to realize the combination of backstepping and PID control.