Research and Design of Quantitative Casting Control System for Lead Anode Plate

As a kind of mobile robot, the intelligent trolley is also called a wheeled robot, and this field is the fastest growing in the current technological development. Smart trolleys have good automatic guidance functions, often used in modern logistics conveying systems. A fuzzy control algorithm has a good control effect for nonlinear systems and has a strong anti-interference ability. In order to optimize the multi-objective longitudinal following system of smart carts, therefore, this paper develops the study of fuzzy PID control algorithm for multi-objective longitudinal following of smart carts. Based on the incremental PID control, an improved PID algorithm is constructed by introducing incomplete differentiation, differential prior, and “optimal curvature” algorithm. Based on the actual control experience, the fuzzy control table is designed, and the adaptive control algorithm is obtained. Finally, the simulation results of the fuzzy PID control algorithm are tested by MATLAB/Simulink, and the most suitable fuzzy PID control algorithm for multi-objective optimal longitudinal following of intelligent carts is derived.

The traditional manual operation mode has problems such as high manpower cost, low efficiency, low safety, low operating precision, etc. Achieving unmanned intelligent systems in the field of mechanical and electrical engineering has become the key to transforming and upgrading relevant enterprises and improving their overall competitiveness. In this paper, an improved fuzzy PID control system is constructed based on a PLC control system combined with a fuzzy PID intelligence algorithm. It is applied to the pumping motor, coagulation dosing system, and other specific devices for simulation experiments, compared to the conventional PID algorithm, and evaluated the control system in this paper. The fuzzy PID control algorithm of this paper reduces the steady-state adjustment time of the response curve by 30% compared to the conventional PID algorithm, and the overshooting amount is reduced by 25%. With almost no overshoot, the pumping motor that was equipped with this paper’s PID algorithm reached its stable target speed in 0.3 seconds. In addition, this study found that in the coagulation dosing system model incorporating the fuzzy PID algorithm of this paper, when the model is accurate, only 20% of the overshooting amount occurs after the system has been running for some time. The conventional PID algorithm’s 50% overshoot is significantly less than this, and the time it takes to reach a steady state is also 50 minutes earlier than the conventional PID control. When the model is imprecise, fluctuations occur in both conventional PID control and fuzzy PID control, but the amplitude of the fluctuations in fuzzy PID control is relatively small. In conclusion, this paper shows that the fuzzy PID control algorithm in this paper has higher control accuracy, response speed, and stability than the conventional PID control algorithm.

Fuzzy Self-tuning PID Control Algorithm for Belt Conveyor Driven by Multi-motor

Aiming at the limitation of traditional PID algorithm in the field of control, the fuzzy control theory and traditional PID control algorithm are combined to realize parameter self-tuning function, a fuzzy self-tuning PID controller, and this algorithm is analyzed in-depth study. It provides a new solution of modern industrial control problem that combining with fuzzy control technology and PID control technology. The FPGA has high integration, short development cycle, the characteristics of strong reusability; fuzzy PID controller has a good practical value and optimistic application prospect based on FPGA.

Although the traditional PID controller is widely used in many fields, the system parameters varying and external disturbances existing in the DC servo system will cause large overshoot or poor stability. To improve the performance of the PID controller, a compound servo control system combining the conventional PID control and the fuzzy control is presented to meet the demand of a vehicular antenna servo system in this paper. Incorporating the fuzzy control and the conventional PID control, this paper presents a design method of the fuzzy PID controller that is based on the fuzzy tuning rules and formed by integrating two above control ideas. Simulation results are presented to show the efficiency of the proposed controller. The practical control effect shows that the control system that adopts the fuzzy PID controller has better performance than that of the traditional PID control system, and meets the performance requirements of the servo system.

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.

This paper presents a fuzzy tracking control methodology for hydraulic control system of robot with generalized pulse modulation (GPCM) control by combining the merits of fuzzy logic and conventional linear control theory. The hydraulic control systems with GPCM control have the high nonlinearity and uncertain dynamics, therefore the simple linear or nonlinear differential equations cannot sufficiently represent corresponding practical system. The designed controllers based on the model cannot guarantee the good performance such as stability and robustness. A hybrid of fuzzy and PID control algorithm is proposed as the solution. The control algorithm is separated into two parts: fuzzy control and PID control. The fuzzy controller is used to control the piston when the piston is near the desired position, and PID controller is applied when the piston is far away the target position. The hybrid fuzzy PID is implemented on computer and applied to control the arms of hydraulic robot with GPCM control. The results from the experiments show that the proposed hybrid fuzzy PID control has good performance.

With the development of wind power technology in recent years, wind power technology has made rapid development. In this paper, the wind farm is the main object of study, the energy storage elements is vanadium redox flow battery (VRB), the use of decoupling control of fuzzy PID control algorithm for real-time changes in PID parameters of the active and reactive power. The outer loop of motor side rectifier is power loop, the inner is current loop. The outer loop of grid side inverter is the voltage loop, the inner is current loop, and based on vector control, the rectifier and inverter achieve decoupling process.The traditional PID is poor in anti-interference ability and robustness,so this paper uses the fuzzy PID control algorithm. The MATLAB/simulation results show that the fuzzy control algorithm can achieve power tracking control,and has strong robustness.

Fuzzy logic control based on the Takagi–Sugeno inference method has been applied for the yeast alcoholic fermentation running in a continuous-time biochemical reactor in this paper. A type-1 fuzzy PID controller was designed to temperature control in a biochemical reactor. The fuzzy PID controller was also designed using type-2 fuzzy sets. The advantage of the fuzzy control is that it can be used very successfully for control of strongly non-linear processes and processes that are difficult to model because of complicated reaction kinetics. Obtained simulation results confirm this fact. The disadvantage of the fuzzy control design lies in the time-consuming tuning of controllers. The subtractive clustering method was used to identify the rule base. This approach was chosen to minimize the number of rules of the designed fuzzy logic controllers and to simplify the fuzzy controller design. Simulation results confirm that fuzzy PID controllers can assure better performance than conventional PID controllers. Yeast alcoholic fermentation is an exothermic process and the cooling is necessary to maintain the optimal temperature in the reactor. Fuzzy PID control reduces coolant consumption in comparison with conventional PID control and so it assures energy-efficient control of continuous-time alcoholic fermentation. Using type-2 fuzzy sets in the fuzzy PID controller design is very promising as the type-2 fuzzy controller offers even better results than the type-1 fuzzy controller.

To obtain perfect performances of the main steam temperature control system, a new scheme is proposed. The scheme substitutes fuzzy self-tuning PID controller for the main regulator of cascade control system. The controller is composed of fuzzy controller and PID controller. According to the error and error rate of the control system and fuzzy control rules, the fuzzy controller can online adjust the three parameters of the PID controller, then alter the output of the fuzzy self-tuning PID controller to maintain the main steam temperature. The results show that the proposed control scheme has smaller overshoot, shorter settling time, better capacity of anti-disturbance and stronger robustness than the conventional PID cascade control.

To achieve a better robustness and get a static response of a certain target missiles in longitudinal control, a mathematical model of target missiles is established. The uncontrolled trajectory, the predetermined trajectory and the requirements of the predetermined trajectory are analyzed next. Then the trajectory instruction angle is designed to ensure the stable flight of the target missiles system. To solve the problem that the PID controller cannot adjust parameters online and the output is easy to be disturbed by external noise, the fuzzy adaptive PID control is designed. It enhanced the robustness of the target missiles by fuzzy control, and also has a good response by using PID control. The simulation experiments show the fuzzy adaptive control system can track the predetermined trajectory rapidly and smoothly, and the static error and overshoot were significantly reduced. When the system are affected by noise, the fuzzy adaptive PID control can achieve a good robustness than traditional PID controller.

PID and fuzzy PID controller are applied into the wind turbine with nonlinear mathematical model. PID control has simple principle and its parameters setting are rather easy. Fuzzy control need not to establish the mathematical of the control system and has strong robustness. Fuzzy PID control is combined with PID control and fuzzy control. The advantages of fuzzy PID control are simple, easy in setting parameters and strong robustness. Fuzzy PID controller combines with closed-loop optimal fuzzy reasoning (COFR), which can effectively improve the robustness, when the robustness is special requirement. MATLAB software is used for simulations, results display that fuzzy PID controller which combines with COFR has better performances than PID controller when errors exist, especially robustness performance.

In order to meet the needs of modern high-performance aircraft, the fuzzy PID control strategy was used to design the electromechanical actuator control system. This control strategy combines the advantages of fuzzy control and PID control, and has better adaptability to time-varying complex systems. The electromechanical actuator control system adopted the three-loop structure, and current loop, speed loop and position loop were built on the platform of MATLAB / Simulink. Considering that the discrete universe fuzzy PID control algorithm has good real-time performance with small online computation, the fuzzy PID controller in discrete universe was designed in the position loop and the system was simulated. The simulation results show that the dynamic and steady performance of discrete universe fuzzy PID control is better than that of traditional PID control, and it is more suitable for electromechanical actuator system.

The paper is concerned with two types of fuzzy PID controllers, the rule-based fuzzy PID controller and the learning fuzzy PID controller. Initially, the development of the rule-based fuzzy PID controller is studied, and subsequently, the development of the learning fuzzy PID controller is outlined. For the rule-based fuzzy PID controller, the PID gains are firstly tuned using a conventional tuning method. The rule buffer of the rule-based fuzzy then readjusts the PID gains during the system operation. The learning fuzzy PID controller not only readjusts the PID gains, it also creates and updates the rules in the rule-buffer automatically during the application of the learning fuzzy PID controller to the process. The rule-based fuzzy PID controller and the learning fuzzy PID controller are both applied to a non-linear revolute-joint robot-arm for path tracking experiments. The results of the experiments for the rule-based fuzzy PID controller and the learning fuzzy controller are compared with a conventional PID controller in order to evaluate the results.

Because of the existing hybrid fuzzy PID controller does not perform, using electric hydraulic servo system application (SEHS). Therefore, when the system parameters change will require a new adjustment of PID controller variable. Therefore, a hybrid fuzzy and fuzzy self-tuning PID control was put forward. With this control scheme was divided into two parts, and the fuzzy controller and fuzzy self-tuning PID controller. Fuzzy controller is used to control the output of the system of the values of the system away from target value. We proved that the performance of the control scheme through the experiment of the motor speed control SEHS. The experimental results show that the proposed a hybrid fuzzy PID controller and fuzzy self-tuning effect is better than that of a hybrid fuzzy and PID controller.