Design and Implementation of Fuzzy PID Controller into Multi Agent Smart Library System Prototype

This paper presents a real-time implementation of a genetic-based hybrid fuzzy-PID controller for industrial motor drives. Both the design of fuzzy-PID controller and its integration with the conventional PID in global control system to produce a hybrid design is demonstrated. A genetic optimization technique is used to determine the optimal values of the scaling factors of the output variables of the fuzzy-PID controller. The objective is to utilize the best attributes of the PID and fuzzy-PID controllers to provide a controller, which will produce better response than either the PID or the fuzzy-PID controller. The principle of the hybrid controller is to use a PID controller, which performs satisfactorily in most cases, while keeping in the background, a fuzzy-PID controller, which, is ready to take over the PID controller when severe disturbs occur. The hybrid controller is formulated and implemented in real-time, using the speed control of a brushless drive system as a testbed. The design, analysis, and implementation stages are carried out entirely using a dSPACE DS1104 digital signal processor (DSP)-based real-time data acquisition control (DAC) system, and MATLAB/Simulink environment. Experimental results show that the proposed fuzzy- PID controller-based genetic optimization produces better control performance than the conventional PID controllers, particularly in handling nonlinearities and external disturbances.

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.

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.

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.

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.

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.

Abstract- This paper introduces a linear structure of wind turbine, operator and the pitch angle controller. Then, a new method of adaptive fuzzy on the variable speed wind turbines was provided and it was compared with PID and Fuzzy Logic Control ler and the simulation results were analyzed. So when the results of each simulation using PID and Fuzzy controllers and adaptive fuzzy PID controllers provided, the observed advantages and disadvantages of the rotor speed and wind turbine power can be introduced. It can be seen that in PID method the high overshoot is discussed as a disadvantage while it is overcome by using fuzzy controller and overshoot will decrease. The time of reaching to a sustained speed increases slightly and in adaptive fuzzy PID controller, the less overshoot has provided a good and effective performance for system response. The use of adaptive fuzzy PID controller causes the system does not have any steady-state error and at all moments of time, the response rate is better than PID and fuzzy controller. The importance of the amount of overshoot and rate fluctuations is that by reducing these parameters in addition to reducing the cost of preventive care, maintenance and depreciation, the fluctuations in electricity generated by induction generator also greatly reduced.

A GBMO-based PI λ D μ controller for vibration mitigation of seismic-excited structures

Both linear PID controllers and fuzzy controllers are designed and implemented for a boost converter. Comparison between the two controllers is made in the aspect of design, implementation and experimental results. Design of fuzzy controllers is based on heuristic knowledge of the converter and tuned using trial and error, while the design of linear PID and PI controllers is based on the frequency response of the boost converter. Implementation of linear controllers is quite straightforward, while implementation of fuzzy controllers has its unique issues. A comparison of experimental results indicates that the performance of the fuzzy controller is superior to that of the linear PID and PI controllers. The fuzzy controller is able to achieve faster transient response, has more stable steady-state response, and is more robust under different operating points.

High efficiency due to reduced losses, low maintenance and low rotor inertia of the brushless dc (BLDC) motor have increased the demand of BLDC motors in high power servo and robotic applications. Since fuzzy logic with human like but systematic property can convert the linguistic control rules based on expert knowledge into automatic control strategy, it can be well applied to control the systems with uncertain or unmodelled dynamics. In view of fuzzy logic merits, this paper proposes three types of fuzzy logic controllers for BLDC motor drive using advanced simulation model and presents a comparative study of performance specifications of classical PI and PID controllers and three fuzzy logic controllers. The three fuzzy logic controllers considered are PI-like fuzzy logic controller (PI-Like FLC), Hybrid fuzzy logic controller (HFLC) and integrated fuzzy logic controller (IFLC). The steady state and dynamic characteristics of speed and torque are effectively monitored and analyzed using the proposed model. The aim of fuzzy logic controllers is to obtain improved performance in terms of disturbance rejection or parameter variation than obtained using classical controllers. In the HFLC, the proportional term of the traditional PID controller is replaced with an incremental fuzzy logic controller. For the PI-Like FLC, the output of the controller is modified by a rule base with the error and change of error of the controlled variable as the inputs. The IFLC is constructed by using Fuzzy logic controller and PID controller. A performance comparison of classical and fuzzy logic controllers has been carried out by several simulations at different speeds and different load conditions.

Aiming at the deficiencies of traditional PID control algorithm, the application of Fuzzy PID control algorithm is proposed in variable frequency speed regulation system of glass edge grinding machine. The design of fuzzy PID controller is presented in the paper. The paper not only introduces the system principle and models for inverter and AC asynchronous motor but also tells the principle of Fuzzy PID Controller and its design method. A simulation model including traditional PID Controller and Fuzzy PID Controller is established in MATLAB/Simulink. Through the analysis of the difference between the two controlling outputs, the better performance of Fuzzy PID Controller in response speed, accuracy and stability is proved effectively. The result can be used as important criterion to the practical application of Fuzzy PID control algorithm.

Regenerative braking proven to be effective and it can be used for increasing the driving distance of electric vehicles (EVs). Author in this paper developed regenerative braking system (RBS) and it is applied to brushless DC motor, popularly known as BLDC motor. The objective of this paper is to design a unique concept, which will distribute the braking force. In this paper two different techniques are used, for equal distribution force fuzzy logic control and conventional PID control for BLDC motor. The fuzzy logic control is a bit slow than PID control braking torque can be real time, and this is achieved by using PID control. If this method is compared with other methods in literature, the performance and effectiveness is much higher and same is shown in results. While implementing this paper author has developed a model on MATLAB Simulink and hardware results are compared. Various different parameters (e.g. DC bus current, battery state of charge and braking force) are analyzed for checking effectiveness of the developed technique. Results obtained are very positive, which interprets as, usage of fuzzy logic and PID controller in combination is increasing driving distance of EVs.

Robots represent a vital base in modern and future industry. Hence, robot motion control is an important area for research. In this work, inverse kinematics analysis and real-time control of a 5-DOF robot arm are developed and simulated to reach specified positions with minimal error. Rules for analyzing inverse kinematics are developed for the robot arm using the geometric approach, along with analyzing DC motor models, and friction torque equations are introduced. Two control techniques were discussed: a PID controller is initially used, followed by the implementation of a fuzzy logic controller (FL). A FL controller is designed based on Mamadani pro-Max inference. Four defuzzification methods (BOA, MOM, SOM, and COG) are compared. Simulations were conducted using Matlab and Simulink. The methods BOA, MOM, and SOM are similar in giving optimum results. The results of both controllers were compared. FL outperforms PID, with lower rise time, settling time, steady-state error, and less overshoot. Real-time control has been performed. The system performance was good as the output tracks the reference input signal in a good way, and the FL gave better results also.

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.

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