Design and Simulation of Self-tuning PID controller with MPPT for Solar Array using MATLAB/Simulink

The solar energy is directly converted into electrical energy by solar PV module. Each type of PV module has its own specific characteristic corresponding to the surrounding condition such as irradiation, and temperature and this makes the tracking of maximum power point (MPP) a complicated problem. To overcome this problem, many maximum power point tracking (MPPT) control algorithms have been presented. Fuzzy logic (FL) has been used for tracking the MPP of PV modules because it has the advantages of being robust, relatively simple to design and does not require the knowledge of an exact model where a mathematical model of the PV module, DC-DC converter, are used in the study of FL based MPPT algorithm. It is suggested to present this problem in the form of two-folds; first to identify the deviation of the power to maximum power point, and secondly, to control the voltage of the DC-DC converter corresponding to maximum power. In this paper, the first discussion approach will stress out the integration of model predictive control in maximum power point tracking MPPT and as progressing a second approach is identified as fuzzy logic controller FLC and perturb & Observe P&O algorithms are analyzed. All are interrelated to MPPT model for a photovoltaic module, PVM, to search for and generate the maximum power; in this case what’s called P-max. As per the first technique the focus is on the optimal duty ratio, D, for a series of multi diverse types of converters and load matching. The design of the MPPT for a stand-alone photovoltaic power generation system is applied where the system will consist of a solar array with nonlinear time varying characteristics, and a converter with appropriate filters. The integration of model predictive control will be addressed first in this paper. The second fold will implement an MPPT system that use the FLC and compare it with a classical MPPT P&O algorithm through the utilization of Simulink. The novel design in the FLC will be based on the use of asymmetrical membership functions to compensate for the asymmetrical P-V curve of solar panel.

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.

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.

Fuzzy PID control is a process control technique combining the advantages of fuzzy logic and PID control. It maintains precise control over processes by combining fuzzy logic and proportional-integral-derivative (PID) controllers. With this method, it is possible to achieve higher levels of accuracy and stability in controlling process variables such as temperature, pressure, flow rate, and position. This makes Fuzzy PID control an ideal cordial application requiring precise process control. Fuzzy PID control uses the PID parameter tuning experience to make the fuzzy controller automatically tune its parameters, giving PID control a better control effect. In this paper, a fuzzy PID controller is designed to control the temperature of an electric boiler. The control effects of PID control, fuzzy control, and fuzzy PID control are compared by simulation analysis. The simulation results show that the unclear PID control effect is ideal and has a good application prospect.

Microgrids are the major component of the future smart grid, as these open up new possibilities for aggregating loads and sources. As a result, it is considered to be the next-generation power system arrangement, capable of operating in both standalone and grid-connected modes. This work describes a novel fuzzy logic controller for controlling the charging and discharging of a PV battery storage system. The battery is charged and discharged based on the power difference between generation and demand. There are numerous strategies for controlling the performance of battery, however these controlling techniques have issues in terms of limiting overcharging, complexity in control and slow charging. The paper compares the performance of battery, simulated in MATLAB Simulink using PID controller and fuzzy logic controller. A fuzzy logic controller is less complex to design and provides fast charging.

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.

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

This paper focuses on the intelligent techniques used for tracking the maximum power point of the solar photovoltaic (SPV) system for varying environmental conditions. The most widely used perturb and observe (P&O) maximum power point tracking (MPPT) technique is discussed briefly for the comparison with the intelligent techniques. The paper proposes control technique for the SPV system by using fuzzy logic controller (FLC)-based MPPT algorithm and the optimisation of its various parameters by genetic algorithm (GA). The performance of the FLC optimised with GA is compared with the P&O and the fuzzy-based MPPT technique. MATLAB/simulink software is used to design the different stages of the MPPT controllers. Simulation results reported that GA optimised FLC perform much better than the P&O and fuzzy logic-based MPPT controllers.

In order to use photovoltaic cell effectively and improve its photoelectric conversion efficiency, the maximum power point of photovoltaic generation system should be tracked rapidly and stably. In this paper after comparison and analysis common methods used in controller of photovoltaic systems such as Fuzzy and P&O, proposed an approach combined from FLC and particle swarm optimization algorithm (PSO) as an appropriate method to achieve maximum power point tracking (MPPT). Indeed Fuzzy logic control can cope with photovoltaic system using heuristic knowledge rules, but tuning the control parameters is not straightforward. PSO performs an on-line haphazard global search for input and output scaling factors of a PD-type fuzzy controller. The objective function of the PSO algorithm has been defined to minimize slope of P-V curve. The simulation results in SIMULINK of MATLAB indicate that proposed method can effectively eliminate the power oscillation around MPP and raise stability and reach steady state of the system.

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.

Experimental analysis of impact of MPPT methods on energy efficiency for photovoltaic power systems

Photovoltaic (PV) energy production has become more and more important in the latest years. The need of energy efficiency pushed the research in this field both on material innovation and system management. As well known, an essential task in a PV system is performed by the tracking algorithm that has to assure the maximum power generation of the system. Many Maximum Power Point Tracking (MPPT) techniques have been defined. Among them a very promising category is that based on the modeling of the panel (model-based MPPTs). This technique allows to achieve performance comparable to that of traditional algorithms in steady state conditions. On the other side it features much higher performance from a dynamic point of view. An issue of MB-MPPTs is related to the model parameter identification and in particular to the stability of the model fitting over time. In this paper this stability problem will be analyzed for a MPPT previously proposed by the authors.

It is difficult for greenhouse to control temperature since it is usually of such characteristics as big inertia and big lag.Therefore,Fuzzy PID control was introduced into the system.Fuzzy PID control algorithm was used to design temperature control system according to the season of a year and the section of a day.By simulation and comparison with Fuzzy PID and general control,this paper concludes that the use of the method in the system can greatly improve the robustness and self-adaptability of the system.It can save energy and improve economic benefit.

Simulation and experimental design of adaptive-based maximum power point tracking methods for photovoltaic systems

Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC