DSP-Based Implementation of Fuzzy-PID Controller Using Genetic Optimization for High Performance Motor Drives

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.

This paper compares the performance of four different controllers implemented on two multi agent robots to stabilize its motion from one station to another during delivery tasks. The controllers are; multi-position controller, PID controller, fuzzy logic controller and fuzzy-PID controller. The aim of this paper is to control the mobile robot robustly to arrive its target destination. The robots and station coordinates are recognized using machine vision system and all programming is carried out in LabVIEW. The paper compares the transient response and steady state error of each of controller and experimental results show that the Fuzzy-PID controller produced the best performance and good trajectory of robot from its current position to its target position. It had a better convergence rate when compared with other controllers like PID and Fuzzy logic controllers.

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.

The control performance of a fixed parameters PID controller in a dynamic system will be reduced. To solve this problem, an optimized nonlinear PID controller with fuzzy inference system is proposed. The first order plus dead time (FOPDT) is a high dynamic system. This paper introduces the development and implementation of the optimal fuzzy PID controller for the FOPDT system to improve the control performance. The control process of temperature is a FOPDT model, and the approximate mathematical model is constructed according to the analysis of the step response curve without close-loop control. The design of fuzzy PID controller and the implementation of the control software are carried out by using MATLAB\Simulink. The experimental results show that, compared with the traditional PID controller, the optimized fuzzy PID controller can greatly improve the control performance for the FOPDT system.

The photovoltaic (PV) system’s nonlinear nature, uncertainty in temperature, and light intensity in the environment are responsible for the fluctuation of the maximum power point (MPP). Many maximum power point tracking (MPPT) algorithms are proven effective in the literature, including use of artificial intelligence. Proportional integral derivative controller and fuzzy controllers found effective to track MPP. Using the combination of Fuzzy-PID controller, swiftly adapt to changes in the atmosphere to confirm the solar array is continually operating at its highest efficiency with MPP. Easy and effective oscillation elimination is possible with PID control. To regulate the maximum power of the PV system combination of Fuzzy-PID self-tuning controllers is found effective.The goal is to enhance a PV system’s dynamic response to in presence of irradiation and temperature fluctuations. For tuning Fuzzy logic controller (FLC), gain scheduler values of the proportional, integral, and derivative constants are fundamental. The design processes for non-linear conventional PID controllers, FLC, and series combination of Fuzzy-PID controller are discussed. It compares the traditional PID controller, FLC, and Fuzzy-PID controller. The comparison of outputs produced for PID, FLC, and series Fuzzy-PID controller confirms the effectiveness of the algorithm. Use of Fuzzy controller helps in self-tuning of PID controller constant value. MATLAB/Simulink based environment used to perform the simulations.

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.

Aiming at the microwave-vacuum wood drying controlled object with the characteristics of strong coupling, large delay, large inertia, nonlinear and time-variant, a control system based on fuzzy self-tuning PID control and conventional digital PID is proposed. The system combines the cascade control with the fuzzy self-tuning PID control, taking fully advantage of the cascade control, fuzzy control, self-tuning control and PID control, which makes the system possess not only better track ability but also stronger robust and anti-disturbance The design, analysis, and implementation stages are carried out entirely in a TMS320LF2407A digital signal processor-based real-time data acquisition control system and MATLAB/Simulink environment. The simulation result indicates that this system may enhance the entire microwave-vacuum wood drying system control precision and working efficiency, and it's an effective method to realize automatic control in the wood drying process. What's more, the control algorithm is simple and practical.

This article presents a fuzzy fractional-order PID (FFOPID) controller scheme for a pneumatic pressure regulating system. The industrial pneumatic pressure systems are having strong dynamic and nonlinearity characteristics; further, these systems come across frequent load variations and external disturbances. Hence, for the smooth and trouble-free operation of the industrial pressure system, an effective control mechanism could be adopted. The objective of this work is to design an intelligent fuzzy-based fractional-order PID control scheme to ensure a robust performance with respect to load variation and external disturbances. A novel model of a pilot pressure regulating system is developed to validate the effectiveness of the proposed control scheme. Simulation studies are carried out in a delayed nonlinear pressure regulating system under different operating conditions using fractional-order PID (FOPID) controller with fuzzy online gain tuning mechanism. The results demonstrate the usefulness of the proposed strategy and confirm the performance improvement for the pneumatic pressure system. To highlight the advantages of the proposed scheme a comparative study with conventional PID and FOPID control schemes is made.

In the last two decades, there have been great advances in using Unmanned Aerial Vehicles (UAVs) in several applications. This rapid growth is due to their ability to carry out several types of tasks with high flexibility aligned with reduced risks to human life and cost-effectiveness. The increasing demands for more complex assignments have improved UAVs' capabilities, propelling the rise of platforms with a high degree of autonomy for performing simultaneous tasks with less human intervention. One of the great challenges of maneuvering a UAV for a stabilized flight consists of a highly-coupled nonlinear system with fast dynamics. Thus, attitude control is an essential issue for stabilizing the vehicle or for keeping it in the desired orientation. The PID controller is often used due to its simple structure, good stability properties, and less dependence on the exact system model. However, its tuning process may become complicated in the face of the system nonlinearities. In this scenario, an adjustment error can cause it to lose its flight stability. Therefore, the main contribution of this work is the development of a Fuzzy-PID hybrid controller to control a quadrotor UAV's height stability. The work also considers a classic PID controller for comparison purposes with the proposed hybrid controller. The ROS/Gazebo platform was used to carry out the experiments with both control structures. From the results, it was possible to verify that both PID and Fuzzy-PID controllers could perform the attitude control of the UAV. However, the hybrid control strategy obtained some advantages, such as self-adjustment through system variations.

The research on active control of the pantograph has been intensively studied, however, the control results in practice are not satisfactory as expected, for the existence of time delays coming from the input and output measurement. This paper focuses on the problem of time delays in the designing of the control strategy, including the sensor signal input and the action of the actuator. A multibody dynamic pantograph model is established based on the relative coordinate method, combined with the rigid catenary established by the modal superposition method. Firstly, the performance of the traditional PID controller with signal time delay is studied, including the separation or combination of sensor signal input delay and actuator signal output delay. Then, a novel fuzzy PID controller with an improved rule library is proposed, which further improves the control performance of the PID controller. Finally, to deal with the time delay of sensor and actuator well, a dynamic predictive fuzzy adaptive PID (DMC-Fuzzy-PID) controller is proposed by combining dynamic matrix control (DMC) with the improved fuzzy PID controller, which may support the engineering application of active control of pantograph.KeywordsActive controlTime delayRigid catenaryMultibody dynamicsFuzzy PID controller

In CNC lathe servo feed system, many parameters are uncertain, so it is difficult to establish accurate mathematical model of the feed system, and parameters of conventional PID controller can not change the parameters to adjust with the controlled object. Fuzzy PID controller, which has the advantages of conventional PID control and fuzzy control, can solve these problems very well. So we can make it applied in CNC lathe servo feed system. This paper has made a simulation of fuzzy self-turning PID controller in the MATLAB environment. The simulation shows that the fuzzy self-turning PID controller has a better control capability and a higher stability, and it improved the servo feed system dynamic performance.

In this paper, a self-tuning hierarchical controller in which a Fuzzy logic controller supervises the control actions of a conventional PID has been proposed, implemented and presented. The controller has been applied to a control study of Fluid Catalytic Cracking (FCC) unit riser temperature, and regenerator temperature respectively. Performance comparison of the proposed Fuzzy-PID controller and the conventional PID was made in simulation studies of regulatory and servo performances of the two controller types. Six performance measures: Percent overshoot (OS), settling time (ST), integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE) and integral time square error (ITSE) were employed as the tools for performance comparison between the conventional PID and the Fuzzy-PID controller. For the tracking of riser temperature with a set point at 524oC, the performance indicators under PID control gave the following results overshoot (14.5%); settling time (40 seconds) Integral absolute error (8.246), integral square error (3.3); integral time absolute error(1762);integral time square error (43.77) while for the same indicators under Fuzzy-PID control the following values: overshoot (3.3%); settling time (40 seconds) ;Integral absolute error (8.811); integral square error (14.5); integral time absolute error(280),;integral time square error (31.11) .The results allude to the superiority of the fuzzy-PID scheme over the PID scheme in tracking the optimal set point of riser temperature. More so, for tracking the regenerator set point temperature of 746oC, comparative study of step response under the two schemes gave the following results in six performance indicators: overshoot (PID(12.6%)/Fuzzy-PID(6%)); settling time (PID(80 seconds)/Fuzzy-PID(20seconds));Integral absolute error(PID(14.29)/Fuzzy-PID(8.63)); integral square error(PID(6.713).Fuzzy-PID(4.506)); integral time absolute error(PID(2858)/Fuzzy-PID(305.9)), integral time square error (PID(77.55)/Fuzzy-PID(33.05)).Moreover, the fuzzy-PID controller also showed superior performance over the conventional PID controller in terms disturbance rejection (regulatory response) of both riser and regenerator temperature. The results from this study suggest that the application of fuzzy-PID scheme to temperature control offers good promise of improved fluid catalytic cracking unit (FCCU) operations.

Cascade fuzzy control of a tubular chemical reactor

Brushless doubly-fed induction generator (BDFIG) has drawn significant attention in recent years in variable speed drive applications due to such features as simple and robust construction, favorable operating characteristics and reduced maintenance. The objective of BDFIG control is to achieve better performance compared to the doubly fed induction generator using the well-established vector control method. Control of a BDFIG with back-to-back PWM converters using an artificial intelligence approach, fuzzy PID controller, is proposed for a BDFIG-based variable speed wind energy conversion system. The proposed controller is adaptive in the manner that the controller parameters are modified online by using the fuzzy control rules. Comparative performance of the BDFIG with the proposed fuzzy PID controller and the conventional fixed-parameters PID controller under various operating speeds, stator reactive power references and a 100% voltage dip is investigated. Results of simulation studies using MATLAB® reported in the paper show that the limitations of the conventional PID controller can have negative effects on both quality and quantity of the generated power. Performance of the system can be improved with the proposed adaptive fuzzy PID controller under dynamic conditions.

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.