Fuzzy Based MPPT Controller For Solar Photovoltaic Systems

This paper presents an intelligent maximum power point tracking (MPPT) method for a stand-alone photovoltaic (PV) system using artificial neural networks (ANN) modelling and a fuzzy logic controller (FLC). The ANN is trained for various conditions of solar irradiance and temperature to estimate the MPP voltage. This voltage is then used by the FLC as a reference voltage to generate the appropriate control signal for the DC-DC converter. The proposed technique is implemented in Matlab/Simulink and compared with the conventional method of incremental conductance (IncCond). Simulation results show a good performance of the ANN based fuzzy MPPT controller.

Fuzzy logic controllers are designed for spacecraft hovering flight in a binary asteroid system. The binary asteroid system is modeled as an ellipsoid-sphere system where the spherical harmonics method is adopted to represent the gravitational field of the asteroids. The method to design the fuzzy logic controllers is introduced in detail. Hovering flight about the inner collinear equilibrium point L 1 of binary asteroid Didymos is numerically presented. Detailed steps on roughly estimating the scaling gains of fuzzy logic controllers are also summarized. Numerical simulations are performed to show the effectiveness of the proposed method. Moreover, the aforementioned fuzzy logic controllers can be used for both continuous and impulsive thrusters. The fuzzy controllers with continuous thrust, the fuzzy controllers with fixed thrust, and the other two kinds of sliding-mode controllers are optimized in the same way and compared in the same simulation environment together, to show tremendous advantages of fuzzy logic controllers and continuous thrusters on both control performance and propellant consumption.

Attempts are being made to enhance the drive performance by intelligent control using fuzzy logic (FL) and neural network techniques. One of the frequently discussed applications of artificial intelligence in control is the replacement of a standard proportional plus integral (PI) speed controller with an FL or artificial neural network (ANN) speed controller. Regardless of all the work, it appears that a thorough comparison of the drive behavior under PI, FL, and ANN speed control is necessary. This article attempts to compare PI, fuzzy, and ANN controllers that are implemented in an embedded system for closed-loop speed control of DC drive fed by a buck-type DC–DC power converter. The PI controller is designed based on the small signal modeling of the system. The PI-like fuzzy controller structure is considered for comparison. Two ANN controllers are designed. One controller uses training data obtained from the simulation of a fuzzy controller and the other uses training data from the simulation of a PI controller. The performance of the controllers is studied for a variety of operating conditions, such as step change in speed command and step change in load torque. The parameters selected for the comparison are the steady-state error and the rise time of the response. It is shown that ANN speed controllers provide a superior speed response in terms of rise time and the steady-state error compared to PI and FL controllers. This advantage arises from the fact that the neural network has the property of generalization and the control surface of the neural controller is smooth. The designed neural network controller is simple, with three neurons only, and so it is best suited for embedded system implementation. It is also found that the ANN controller trained with the training data from a PI controller has a better response compared to the ANN controller trained with data from a fuzzy controller.

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 paper proposes a modified particle swarm optimization algorithm (MPSO) for online tuning to the fuzzy controller of power system stabilizer (PSS). The hybrid fuzzy controller combines concepts from fuzzy systems and particle swarm optimization to produce powerful system controllers that are used for adaptive control. Two synchronous generators fitted with a PSS and are connected via double transmission lines will be the system under study. Three types of PSS controllers such as Conventional lead-lag PSS (CPSS), Static Fuzzy Logic Controller (SFLC) and online tuning Fuzzy Logic Controller based on PSO algorithm will be considered. The modified PSO algorithm used to adjust the fuzzy set parameters of each Fuzzy Logic Controller (FLC-PSO) online. The speed error and the change of error are the input vector, and the stabilizing signal is the output of both fuzzy logic controller (FLC). The FLC controllers will be tested under different disturbances for a wide-range of operating points. The computation time and the complexity of the fuzzy controller are reduced. The simulation results ensure the effectiveness of the proposed FLC-PSO controller for achieving the good dynamic response of the system under study.

The increase use of variable-speed drive motors to reduce energy consumption will require a shift from PID controllers to system based on fuzzy logic algorithms to simplify design, reduce development time, and eliminate complex math formulas. This paper presents an approach for minimizing torque ripple in brushless DC motor (BLDCM) due to phase commutation using fuzzy control. The BLDCM drive system based on fuzzy logic control uses double close loops with current hysteresis and speed fuzzy logic control. The traditional speed PID controller is replaced by fuzzy PI-like controller to improve the performance of the BLDCM drive system. The duty ratio is controlled by a ¿ deadbeat¿ controller to equalize the current slopes of the incoming and the outgoing phases of BLDCM during the commutation interval. The simulation results with MATLAB show the accomplishment of suppression of the spikes on the current and torque responses during the commutation intervals.

The authors propose a reinforcement neural-network-based fuzzy logic control system (RNN-FLCS) for solving various reinforcement learning problems. RNN-FLCS is best applied to learning environments where obtaining exact training data is expensive. It is constructed by integrating two neural-network-based fuzzy logic controllers (NN-FLCs), each of which is a connectionist model with a feedforward multilayered network developed for the realization of a fuzzy logic controller. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. Using the temporal difference prediction method, the fuzzy predictor can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the fuzzy controller. The fuzzy controller implements a stochastic exploratory algorithm to adapt itself according to the internal reinforcement signal. During the learning process, the RNN-FLCs can construct a fuzzy logic control system automatically and dynamically through a reward-penalty signal or through very simple fuzzy information feedback. Structure learning and parameter learning are performed simultaneously in the two NN-FLCs. Simulation results are presented. >

Since the BP neural network has poor performance and unstable learning rate in the maximum power point tracking (MPPT) algorithm of photovoltaic (PV) system, an adaptive particle swarm optimization BP neural network-fuzzy control PV MPPT algorithm (APSO-BP-FLC) is proposed in this paper. First, the inertia weight, learning factor and acceleration factor of particle swarm optimization (PSO) are self-updating, and the mutation operator is adopted to initialize the position of each particle. Second, the APSO algorithm is used to update the optimal weight threshold of BP neural network, where the input layer is irradiation and temperature, and the output layer is the maximum power point (MPP) voltage. Third, the fuzzy logical control (FLC) is employed to adjust the duty cycle of Boost converter. The inputs of FLC are voltage difference and duty ratio D(n-1) at the previous time, and the output is duty ratio D(n). Moreover, D(n-1) is optimized by |dP/dU| to improve the search range of FLC. The irradiation, temperature and MPP voltage of PV cell are adopted as the datasets for simulation in a city in Shaanxi province, China. Simulation results show that the proposed MPPT algorithm is superior to the APSO-BP, FLC and perturbation and observation (P&O) algorithm with tracking performance, steady state oscillation rate and efficiency. In addition, the efficiency of proposed MPPT algorithm is improved by 0.37%, 6.2%, and 6.8% as compared to APSO-BP, FLC and P&O algorithm.

This paper presents the design of a novel fuzzy logic based automatic caution order system for Indian Railways. The Artificial Intelligent Fuzzy Logic Controller (FLC) is used. Simulation is done in Matlab-Simulink to control speed of the train as per the caution order. A `caution data processor' subsystem is introduced to switch from manual to fuzzy control of the traction motor at caution regions of the track & sets the speed limitation as per caution order. FLC gets activated at Caution point by the Caution data Processor and gets deactivated at the end of caution thereby passing the control of the train to its loco pilot. This system of imposing speed limitations on route sections is made automatic by the introduction of Caution Data Processor and FLC; improving on conventional speed control aspects of the train manually by the loco pilot. FLC Inputs are Speed error and acceleration. An emergency switch is also included to override the fuzzy control in the caution region, if any emergency occurs. Simulation studies were done to check the working of FLC design & the speed control of traction motor is achieved as per Caution Order. Thus FLC & Caution Data Processor provides intelligent & effective speed control of traction motor.

This paper includes the design and implementation of a fuzzy logic controller for level control system to overcome the limitations of the conventional controllers. Mostly preferred controller is a Proportional Integral Derivative (PID) controller which has fixed control parameters. Selection of wrong parameters in a PID design results into uncontrollable process. But fuzzy logic controller is a rule based. These rules are designed on the basis of heuristic information provided by an operator. Process model is identified and used for the design of Proportional Integral (PI) and fuzzy controllers by using MATLAB. To evaluate the performance of both the controllers, it is tested on a real time system with the help of Real Time Workspace (RTW) module of a MATLAB. From real time results it is understood that, fuzzy controller provides more stable and improved output for level control. Even fuzzy controller has ability to remove overshoot which is present in PI controller. Also it requires less settling time than PI controller. Hence fuzzy control system is more relevant control strategy to control the level.

This paper proposes a strategy to optimize parameters in a fuzzy logic control of a Permanent Magnet Brushless Motor. The proposed system uses a neural network optimization to determine all the static parameters of the fuzzy logic control strategy. Analysis of the vector control scheme and the fuzzy controller are illustrated from the simulation with the MATLAB Environment to compare their performance. The main objective behind this optimization strategy is to improve the performance of the fuzzy logic control scheme. This approach has been experimentally evaluated on a 1.5 kW permanent magnet brushless motor drive.

The aim of this paper is to show how to build a fuzzy controller and its membership functions automatically. In a fuzzy logic controller (FLC), the proposed method allows one easily to construct a set of membership functions, called shrinking-span membership functions (SSMFs). The FLC uses Mamdani-type fuzzy controllers for the defuzzification strategy and inference operators. The FLC hardware implementation is performed on an 8-bit microcontroller. Simulation results and experimental results demonstrate that the converter can be regulated with good performance even when subjected to input disturbance and load variation. The presented approach is generally valid for the design of an FLC, and can be applied to any dc–dc converter topologies.

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.

The expansion of the spread of solar photovoltaic power generation systems is fraught with challenges related to the low efficiency and performance of these systems in light of the change in the surrounding weather conditions and the disturbances associated with the loads, which weakens the electrical energy productivity of these photovoltaic systems due to energy loss. To overcome and reduce these challenges as much as possible, it is considered the use of controllers that track the maximum power point of photovoltaic systems is the ideal solution to increase their efficiency and improve the quality of their electrical power, in order to obtain the maximum productive capacity from these systems, and this will be reflected in achieving an optimum benefit from the available solar energy and thus reducing the loss of unused energy in the absence of the control system.This paper presents a study, analysis and simulation of the actual behaviour of the grid-connected solar photovoltaic system model by means of the (MATLAB/Simulink_R2018a) program. The performance of Fuzzy Logic Controller is then evaluated to track the path of the maximum power point, and its work is based on tracking techniques that use the direct control method to control the operating cycle of the DC commutator, which in turn performs by feeding the inverter with the maximum output power from the solar photovoltaic system.A tracking technology was selected that relies on the method of artificial intelligence, which is the technique (fuzzy logic controller), and a fuzzy logic controller of the type (Mamdani) was used. The most suitable for tracking the maximum power point at the steady state of the Grid and under constant and changing weather conditions, as well as choosing the most appropriate controller when transient disturbances occur in the Grid under constant weather conditions only. The results proved the effectiveness of the fuzzy logic controller, and it had good performance in tracking the maximum power point in terms of achieving more stability, reducing error and settling time.

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.