Optimal PID and fuzzy logic based position controller design of an overhead crane using the Bees 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.

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 studies the use of Genetic Algorithms (GA) in the design of Fuzzy Logic Controllers (FLC) and show how population size, probability of crossover and rate of mutation can effect the performance of the GA. The comparison of various parameters shows that GA is helpful in improving the performance of FLC. A fuzzy logic is fully defined by its membership function. What is the best to determine the membership function is the first question that has be tackled. Thus it is important to select the accurate membership functions but these methods possess one common weakness where conventional FLC use membership function generated by human operators. The membership function selection process is done with trial and error and it runs step by step which is too long in completing the problem. This research develops a system that may help users to determine the membership function of FLC using the technique of GA optimization for the fastest processing in completing the problems. The data collection is based on the simulation results and the results refer to the transient response specification is maximum overshoot. From the results presented, the system which we developed is very helpful to determine membership function and it is clear that the GA is very promising in improving the performance of the FLC to get more accurate in order to find the optimum result.

Base on the Fuzzy Logic(FL) control theorem, the Self-tune FL PID controller is designed in which the base rules of operator experience were combined. In MATLAB simulation, when system uses the same parameters, the Self-tune FL PID controller’s over-adjust decreases by 17% compared to the conventional PID controller. That applying Self-tune FL PID controller to control the damper’s position in VAV system is able to achieve the steady and less energy consumption system.

This study investigates the use of Genetic Algorithms (GA) to the design and implementation of Fuzzy Logic Controllers (FLC). A fuzzy logic is fully defined by its membership function. What is the best to determine the membership function is the first question that has been tackled. Thus it is important to select the accurate membership functions but these methods possess one common weakness where conventional FLC use membership function generated by human operators. The membership function selection process is done with trial and error and it runs step by step which is too long in completing the problem. This research develops a system that may help users to determine the membership function of FLC using the technique of GA optimization for the fastest processing in completing the problems. The data collection is based on the simulation results and the results refer to the transient response specification is maximum overshoot. From the results presented, the system which we developed is very helpful to determine membership function and it is clear that GA is very promising in improving the performance of the FLC to get more accurate in order to find the optimum result.

In this paper, control scheme for Load Frequency Control (LFC) problem in a three-area interconnected power system with Adaptive Neuro Fuzzy Inference System (ANFIS) controller is presented. Its performance is compared with that of conventional PID Controller and Fuzzy Logic Controllers. This power system consisting of a single reheat thermal unit, a double reheat nuclear unit and a hydro unit. The controllers are designed, trained and tuned. The robustness test and performance of these control schemes against step load change is examine methodically, simulated and compared using Matlab-Simulink.

Braking system plays a major safety feature in any vehicle; it is considered as one of the key points in safe motoring. The main objective of a good braking system is to provide safe stopping, in addition to the fact that it provides repeatable stopping. Anti-lock Braking System (ABS) is one of the recently developed safety feature used in a vehicle in order to prevent wheels from locking. This article proposes an intelligent adaptive fuzzy logic (FL) genetic algorithm (GA) controller for the Anti-Lock Braking System (ABS) to control the braking of a vehicle by controlling the slip ratio based on quarter car model. The main objective of the proposed controller is to prevent wheels from locking, which is a major safety issue in any braking system for different road conditions. The proposed controller consists of a Fuzzy Logic (FL) Genetic Algorithm (GA) based controller. The Genetic Algorithm is utilized in tuning the membership functions of the Fuzzy logic controller, which is developed to calculate the amount of reduction of the applied torque due to applying brake based on the error of the measured slip ratio signal compared with the desired one. Then a Genetic Algorithm (GA) is utilized in tuning the Fuzzy Logic parameters, which are the center of the Gaussian membership function, and the spread (б). The simulation results of the proposed fuzzy logic genetic algorithm controller are compared with both the results of the Fuzzy logic controller and the PD controller developed in previous work of the authors. The simulation results present many advantages of using Fuzzy Logic Genetic Algorithm based controller over both the Fuzzy Logic and the PD controllers, and the simulation is carried out at different road conditions, namely dry, wet and icy road surface.

A ratio is identified as a heuristic to assist in designing fuzzy logic system (FLS) controllers with a low number of membership functions (MFs), high tracking precision, and fast execution time for the control of a two-link flexible space robot. Comparing simulation results for an FLS with three, five, seven, and nine triangular and Gaussian membership functions provides a combination of type and number of MF for optimal tracking control and execution time. Optimal control occurs when the ratio of the FLS output scaling gain to the number of MFs is equal to the output variable universe of discourse. The optimal FLS design is chosen with three triangular membership functions at an output scaling gain of 15, giving a design ratio of five and execution time of 1 min 40 s while tracking a square trajectory

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.

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.

The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. A MATLAB-R2023a/Simulink-R2023a model was developed to simulate the charging process using real-time adaptive control. The fuzzy logic controller (FLC) automatically updates the PID gains by evaluating the error and how quickly the error is changing. This adaptive approach enables efficient voltage regulation and improved system stability. Simulation results demonstrate that the proposed fuzzy–PID controller effectively maintains a steady charging voltage and minimizes power losses by modulating switching frequency. Additionally, the system shows resilience to rapid changes in irradiance and load, improving energy efficiency and extending battery life. This hybrid approach outperforms conventional PID and static control methods, offering enhanced adaptability for renewable-integrated EV infrastructure. The study contributes to sustainable mobility solutions by optimizing the interaction between solar energy and EV charging, paving the way for smarter, grid-friendly, and environmentally responsible charging networks. These findings support the potential for the real-world deployment of intelligent controllers in EV charging systems powered by renewable energy sources This study is purely simulation-based; experimental validation via hardware-in-the-loop (HIL) or prototype development is reserved for future work.

An optimal PID controller with incomplete derivation is proposed based on fuzzy inference and the genetic algorithm, which is called the fuzzy-GA PID controller with incomplete derivation. It consists of the off-line part and the on-line part. In the off-line part, by taking the overshoot, rise time, and settling time of system unit step response as the performance indexes and by using the genetic algorithm, a group of optimal PID parameters K*p, T*i, and T*i are obtained, which are used as the inital values for the on-line tuning of PID parameters. In a on-line part, based on K*p, T*i, and T*d and according to the current system error e and its time derivative, a dedicated program is written, which is used to optimize and adjust the PID parameters on line through a fuzzy inference mechanism to ensure that the system response has optimal dynamic and steady-state performance. The controller has been used to control the D. C. motor of the intelligent bionic artificial leg designed by the authors. The result of computer simulation shows that this kind of optimal PID controller has excellent control performance and robust performance.

In power system any unpredictable variations of consumption load will cause fluctuation in frequency and the transferred power between the lines. Load frequency control is one of the main problems in the power system which synchronizes with automatic generation control. Checking the different factors that cause unbalance in power system is necessary and one of the important factors of them is the load that randomly varies. In this paper, a three area power system in deregulated environments with participation of synchronized generation of wind with Doubly Fed Induction Generator (DFIG), hydro and thermal power plants are studied. The load in this power system is considered as a random variable which periodically and randomly has frequency of 0.25 around nominal amount. Fluctuations are caused due to considering load as a random variable and using wind-powered turbine with DFIG in power system. To improve the response of power system, the optimal fuzzy logic, PID controller and FACTS devices will be used. Imperialist Competitive Algorithm (ICA) has been used for optimizing the parameters of a fuzzy logic controller. PID Controller parameters have been optimized with three intelligent algorithms, ICA, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Algorithm and the results will be compared with each other. With comparison the result it is observed that in the case of using optimal fuzzy logic controller (OFLC) we have the fastest response with the minimum over shoot and under shoot, also in this case the transferred power between the areas is settled in the lowest value. The results show a significant reduction in losses in transmission lines.

Monoethylene glycol (MEG) or Ethylene Oxide is an important chemical in plastic and automotive industry as mixed ingredients or cooling liquid. It is produced from ethylene oxide via hydrolysis at 200ºC and 22 atm. The ratio of the ethylene oxide with water should be maintain at 1:20 to reduce the formation of diethylene glycol and higher homologs. Objective of this study is to predict a production of MEG using fuzzy logic. Others parameters such as level, temperature, composition and pressure are consider constant in this research as this study focusing on single input, single (SISO) output strategy. For fuzzy logic prediction, the type of model chosen is Mamdani with triangular membership function, input 1, input 2, and output which refer to error, feedback, and production of ethylene glycol respectively. 11 rules has been construct in this research. The rules may contain “AND” or “OR” conjunctions. The “error” represents the difference between the value feedback and the output. The results for fuzzy rules give highest product of MEG (6.91) at error of 0.102 and 0.8 of feedback. The gain of proportional, integral, and derivative are 0.2, 0.2, and 0.1 respectively.

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