Output Of Fuzzy Controller Research Articles

Self-Tuning PID Controller for Quadcopter using Fuzzy Logic

Tracking has become a necessary feature of a drone. This is due to the demand for drones, especially quadcopters, to be used for activities such as surveillance, monitoring, and filming. It is crucial to ensure the quadcopters perform the tracking with stable flight. Despite the advantages of having VTOL ability and great maneuverability, quadcopters require an effective controller to overcome their under-actuation and instability behavior. Even though a PID controller is commonly used and promising with its simple mechanism, it requires very proper tuning to ensure the stability of the system is not affected. In this paper, a simple Fuzzy algorithm is proposed to be incorporated into a PID controller to form a self-tuning Fuzzy PID controller. The Fuzzy logic controller works as the self-adjuster to the PID parameters. A mathematical model of the DJI Tello quadcopter is derived with position and attitude control loops that are designed to track a variety of trajectories with stable flight. The proposed method uses a simple architecture where the ranges of PID parameters are used as scaling factors for Fuzzy controller outputs. The results of the simulations show the tracking error performance metrics, which are IAE, ISE, and RMSE, are smaller compared to the values of the PID controller. Beyond its impact on quadcopter control, the proposed self-tuning approach holds promise for broader applications in nonlinear systems.

Optimized fuzzy PI controller for variable speed wind turbine using DE algorithm

To design an optimal fuzzy proportional-integral (PI) controller for the variable speed wind turbine systems, a new differential evolution (DE) algorithm approach is developed in this paper. We have investigated a fuzzy PI controller, in which fuzzy rules are applied to adapt the parameters of the PI controller founded on the error and its first-time derivative. The fuzzy PI controller's inputs and outputs are tuned using the DE optimization method. The superiority of the suggested (DE fuzzy PI) controller has been proved by comparing the results with (fuzzy PI) and only the PI controller applied to the wind turbine system. In comparison to a fuzzy controller with parameters selected by a human operator, the numerical validation results of the suggested approach (DE fuzzy PI) have shown good performance in terms of robustness, pursuit, and response time.

A Fuzzy Controller for PFN Trigger Time Sequence in Electromagnetic Railgun System

For the electromagnetic launch system, how to realize the wide pulse, fast rise, and small fluctuation of pulse forming network output current is a core problem. A trigger time sequence control method based on fuzzy theory for the pulse forming network is proposed in this paper. First, circuit models of pulse forming unit, pulse forming network, and electromagnetic rail launcher is established. Second, a fuzzy trigger time sequence controller model for pulse forming network is designed by using Simulink. Third, fixed time sequence discharge tests consisting of 20 pulsed forming units on the linear simulation load are carried out to verify the accuracy of the simulation model. Finally, discharge tests on the linear simulation load and launch tests on the electromagnetic railgun are carried out to verify the precision of the fuzzy controller output respectively. The results show that the designed fuzzy trigger time sequence controller can obtain the ideal trigger time sequence, which demonstrates the pragmatic and reliability of fuzzy control algorithm.

Boundary Fuzzy Output Tracking Control of Nonlinear Parabolic Infinite-Dimensional Dynamic Systems: Application to Cooling Process in Hot Strip Mills

This paper utilizes a combination of integral control, fuzzy control, and observer-based output feedback control to deal with the issue of nonlinear output tracking control (OTC) design for nonlinear infinite-dimensional dynamic systems. The system dynamics model is represented by a semi-linear parabolic partial differential equation (PDE) with boundary control and non-collocated boundary measurement. Initially, a Takagi-Sugeno (T-S) fuzzy parabolic PDE model is constructed to surmount the OTC design difficulty from the infinite-dimensional nonlinear system dynamics. Subsequently, a fuzzy-observer-based OTC law is proposed via the T-S fuzzy PDE model and the integral control approach. Here, the integral control ensures asymptotic output regulation, and the observer-based output feedback control is employed to conquer the stabilizing control design difficulty caused by the non-collocation between control actuation and measurement. It is shown via the Lyapunov technique with variants of vector-valued Poincaré-Wirtinger's inequality that the suggested fuzzy OTC law drives the measurement output to asymptotically track the desired reference signal and ensures the boundedness of the resulting closed-loop system, provided that a sufficient condition given in the form of linear matrix inequalities is fulfilled. Moreover, the proposed fuzzy-model-based OTC design is also revised for the exponential stabilization case. Finally, extensive simulation results for a numerical example and a cooling process in hot strip mills are provided to examine the effectiveness and merit of the proposed fuzzy OTC scheme.

Structure Analysis of General Type-2 Fuzzy Controller and Its Application

In this paper, a general type-2 fuzzy controller is proposed. The proposed general type-2 fuzzy controller is based on α-plane representation, which converts the centroid of general type-2 fuzzy sets to centroids of several interval type-2 fuzzy sets. Also, the mathematical expression of general type-2 fuzzy controller is derived by fuzzy product operate and NT type reduction. By the mathematical expression, the relation between general type-2 fuzzy controller output with parameter of triangular secondary membership function is discussed. Finally, the control performances of proposed general type-2 fuzzy controller are compared with conventional PID controller, tradition type-1 fuzzy controller, and interval type-2 fuzzy controller. Furthermore, to show the effective and practical of the proposed controller, a nonlinear inverted pendulum system is tested. The simulation results show that general type-2 fuzzy controller achieves better control efforts than other compared controllers.

A Fuzzy Full Velocity Difference Model Based on Driver’s Perception Characteristics

The perception characteristics of drivers greatly vary with the status of traffic flow. To designate the vehicle trajectory more accurately, a driver’s perception headway coefficient is introduced, and a fuzzy full velocity difference (FVD) model is proposed in this paper. Consistent with the control theory, the stability conditions of the improved model are derived. Through the fuzzy control method, the input and output items of the control rule are constructed, respectively. The genetic algorithm is operated to calibrate the model parameters of timid and aggressive drivers. Eventually, the effectiveness of the model is verified by simulations. The research results show that, with the decrease of driver’s perception headway coefficient, the stability range of the traffic flow gradually increases, which is beneficial. Additionally, the average root means square percentage errors (RMSPE) values of the full velocity difference (FVD) model, the intelligent driver model (IDM), and the proposed model are, respectively, 0.312, 0.308, and 0.213. Compared with IDM and FVD models, the proposed model can accurately describe the local velocity variations and determine the car-following behavior of the human driving vehicle better.

GAPSO-Optimized Fuzzy PID Controller for Electric-Driven Seeding.

To improve the seeding motor control performance of electric-driven seeding (EDS), a genetic particle swarm optimization (GAPSO)-optimized fuzzy PID control strategy for electric-driven seeding was designed. Since the parameters of the fuzzy controller were difficult to determine, two quantization factors were applied to the input of the fuzzy controller, and three scaling factors were introduced into the output of fuzzy controller. Genetic algorithm (GA) and particle swarm optimization (PSO) were combined into GAPSO by a genetic screening method. GAPSO was introduced to optimize the initial values of the two quantization factors, three scaling factors, and three characteristic functions before updating. The simulation results showed that the maximum overshoot of the GAPSO-based fuzzy PID controller system was 0.071%, settling time was 0.408 s, and steady-state error was 3.0693 × 10−5, which indicated the excellent control performance of the proposed strategy. Results of the field experiment showed that the EDS had better performance than the ground wheel chain sprocket seeding (GCSS). With a seeder operating speed of 6km/h, the average qualified index (Iq) was 95.83%, the average multiple index (Imult) was 1.11%, the average missing index (Imiss) was 3.23%, and the average precision index (Ip) was 14.64%. The research results provide a reference for the parameter tuning mode of the fuzzy PID controller for EDS.

Novel Design and Simulation of Fuzzy Controller for Turn-On & Turn-Off Angle in Coordination with SRM Speed Control for Electric Vehicles

In current scenario the Switch Reluctance Motor (SRM) are powerful alternative for Electric vehicles applications, due to its simple and rugged structure, high speed, its fault tolerance ability and Magnet free design these attributes make SRM superior to other conventional machines. This motor is a reluctance torque-driven stepper motor that can be used for bi-directional control and self-starting applications. In This paper novel control strategy proposed is to minimizing the Multiobjective function for accurate speed control of SRM by using Mamdani based two input two output fuzzy controller for optimal evaluation of α and β angle by designing closed loop system for accurate speed control of SRM and the corresponding error indices ITAE, IAE, ISE for with and without controller is analysed and compared modelling and simulation is done using MATLAB 2020a.

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

This paper is dedicated to the memory of Prof. Ioan Dzitac, one of the fathers of this journal and its founding Editor-in-Chief till 2021. The paper addresses the performance improvement of three Single Input-Single Output (SISO) fuzzy control systems that control separately the positions of interest of tower crane systems, namely the cart position, the arm angular position and the payload position. Three separate low-cost SISO fuzzy controllers are employed in terms of first order discrete-time intelligent Proportional-Integral (PI) controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. Iterative Learning Control (ILC) system structures with PD learning functions are involved in the current iteration SISO ILC structures. Optimization problems are defined in order to tune the parameters of the learning functions. The objective functions are defined as the sums of squared control errors, and they are solved in the iteration domain using the recent metaheuristic Slime Mould Algorithm (SMA). The experimental results prove the performance improvement of the SISO control systems after ten iterations of SMA.

Optimization of DC motor speed control based on fuzzy logic-PID controller

In this paper the PID controller and the Fuzzy Logic Controller (FLC) are used to control the speed of separately excited DC motors. The proportional, integral and derivate (KP, KI, KD) gains of the PID controller are adjusted according to Fuzzy Logic rules. The FLC cotroller is designed according to fuzzy rules so that the system is fundamentally robust. Twenty-five fuzzy rules for self-tuning of each parameter of the PID controller are considered. The FLC has two inputs; the first one is the motor speed error (the difference between the reference and actual speed) and the second one is a change in the speed error (speed error derivative). The output of the FLC, i.e. the parameters of the PID controller, are used to control the speed of the separately excited DC Motor. This study shows that the precisiom feature of the PID controllers and the flexibllity feature of the fuzzy controller are presented in the fuzzy self-tuning PID controller. The fuzzy self – tuning approach implemented on the conventional PID structure improved the dynamic and static response of the system. The salient features of both conventional and fuzzy self-tuning controller outputs are explored by simulation using MATLAB. The simulation results demonstrate that the proposed self-tuned PID controller i.plementd a good dynamic behavior of the DC motor i.e. perfect speed tracking with a settling time, minimum overshoot and minimum steady state errorws.

Obstacle avoidance and path planning of humanoid robot using fuzzy logic controller aided owl search algorithm in complicated workspaces

PurposeThis paper aims to outline and implement a novel hybrid controller in humanoid robots to map an optimal path. The hybrid controller is designed using the Owl search algorithm (OSA) and Fuzzy logic.Design/methodology/approachThe optimum steering angle (OS) is used to deal with the obstacle located in the workspace, which is the output of the hybrid OSA Fuzzy controller. It is obtained by feeding OSA's output, i.e. intermediate steering angle (IS), in fuzzy logic. It is obtained by supplying the distance of obstacles from all directions and target distance from the robot's present location.FindingsThe present research is based on the navigation of humanoid NAO in complicated workspaces. Therefore, various simulations are performed in a 3D simulator in different complicated workspaces. The validation of their outcomes is done using the various experiments in similar workspaces using the proposed controller. The comparison between their outcomes demonstrates an acceptable correlation. Ultimately, evaluating the proposed controller with another existing navigation approach indicates a significant improvement in performance.Originality/valueA new framework is developed to guide humanoid NAO in complicated workspaces, which is hardly seen in the available literature. Inspection in simulation and experimental workspaces verifies the robustness of the designed navigational controller. Considering minimum error ranges and near collaboration, the findings from both frameworks are evaluated against each other in respect of specified navigational variables. Finally, concerning other present approaches, the designed controller is also examined, and major modifications in efficiency have been reported.

Research on Torque Control System Based on Fuzzy Logic

Aiming at the needs of intelligent coal mining and construction of intelligent mines, based on fuzzy control language, the torque parameter control system of intelligent drilling rigs is analyzed and researched. The structure of the torque output fuzzy control system and the fuzzy adaptive PID controller are designed, and the system is simulated. The simulation research provides new ideas for the development of actual hardware controllers in the future, and lays the foundation for realizing unmanned operations in coal mines.

Performance Analysis of a High Gain LUO Converter-Based Hybrid PV-Wind System

This project is aimed at the implementation of a fuzzy logic algorithm based maximum power point tracking in transformer less grid connected PV system along with reactive power compensation. A single diode model is used for PV array and its simulation is performed using MATLAB. In fuzzy logic controller, voltage and current are taken as inputs and the effective value of A.C current corresponding to the maximum power point is obtained as output. Thus, in addition to supplying voltage by the inverter without transformer for compensating the reactive power not exceeding its power rating. This results in utilization of PV system at night and at periods of low irradiation. Rules relating the input and output of fuzzy logic controller are written and simulation is performed. A LUO Converter is used for maintaining DC input to the inverter at various conditions of irradiation and temperature. Gating pulses to the inverter are generated by proportional-integral controller. Hardware model of a10W solar panel is developed and results are obtained with fuzzy logic controller for different irradiation and temperature conditions. Results show the effectiveness of the proposed method in utilizing the PV system. This project is implemented using DSPIC30F2010 controller.

Fuzzy Static Output Control of T–S Fuzzy Stochastic Systems via Line Integral Lyapunov Function

Considering some unmeasurable states, a fuzzy static output control problem of nonlinear stochastic systems is discussed in this paper. Based on a modelling approach, a Takagi–Sugeno (T–S) fuzzy system, constructed by a family of stochastic differential equations and membership functions, is applied to represent nonlinear stochastic systems. Parallel distributed compensation (PDC) technology is used to construct the static output controller. A line-integral Lyapunov function (LILF) is used to derive some sufficient conditions for guaranteeing the asymptotical stability in the mean square. From the LILF, a potential conservatism produced by the derivative of the membership function is eliminated to increase the relaxation of sufficient conditions. Furthermore, those conditions are transferred into linear matrix inequality (LMI) form via projection lemma. According to the convex optimization algorithm, the feasible solutions are directly obtained to establish the static output fuzzy controller. Finally, a numerical example is applied to demonstrate the effectiveness and usefulness of the proposed design method.

DC Motor Speed Control Using Mamdani Fuzzy Logic Based on Microcontroller

DC motors are included in the category of motor types that are most widely used both in industrial environments, household appliances to children's toys. The development of control technology has also made many advances from conventional control to automatic control to intelligent control. Fuzzy logic is used as a control system, because this control process is relatively easy and flexible to design without involving complex mathematical models of the system to be controlled. The purpose of this research is to study and apply the fuzzy mamdani logic method to the Arduino uno microcontroller, to control the speed of a DC motor and to control the speed of the fan. The research method used is an experimental method. Global testing is divided into three, namely sensor testing, Pulse Width Modulation (PWM) testing and Mamdani fuzzy logic control testing. The fuzzy controller output is a control command given to the DC motor. In this DC motor control system using the Mamdani method and the control system is designed using two inputs in the form of Error and Delta Error. The two inputs will be processed by the fuzzy logic controller (FLC) to get the output value in the form of a PWM signal to control the DC motor. The results of this study indicate that the fuzzy logic control system with the Arduino uno microcontroller can control the rotational speed of the DC motor as desired.

Использование геометрической прогрессии для определения параметров функций принадлежности при настройке нечеткого регулятора ПИ-типа

In the issue we consider the possibility of geometric series application for fuzzy logic controller tuning. Triangular and trapezoidal membership functions are often used for fuzzy controller parameters description, and determination of those functions bases is a common way to tune the controller. Our related publications review revealed that there is no universal fuzzy controllers tuning method, and that approaches to the problem are usually quite difficult and not sufficiently described to understand and reiterate. Aim. So for the purpose of our research we assumed that bases of fuzzy controller input and output parameters membership functions could be described with a numerical sequence. In that case the entire tuning procedure may be reduced to mere selection of such numerical sequence parameters. Materials and methods. Geometric series with unit as a first member were chosen to avoid ambiguity, different ratios from 1.5 to 3.5 with an increment of 0.2 were chosen for second and third series members calculations. Calculated values were used as membership functions term bases for eleven subsidiary linguistic variables. Then we obtained ranges of control, control error and its time integral values variations for a specific control object using automatically tuned PI-controller. Using MATLAB FIS-editor we constructed PI-type fuzzy controller assuming that both of its input and an output parameters has five terms. By projecting the subsidiary linguistic variables gained earlier on each parameter range we obtain eleven different types of PI-type fuzzy controller tunings. They then were applied for time delay technological object controlling, seven of which proved to be functioning. Results. Comparison of functioning fuzzy controllers’ transient processes with corresponding ordinary PI-controller transient process allowed us to determine that all seven PI-type fuzzy controllers’ quality criteria values are much better that those of ordinary PI-controller. Conclusion. Our research showed that usage of geometric series can be an effective and easy way to obtain fuzzy controllers tunings of satisfactory quality.

Energy-efficient single-machine scheduling problem with controllable job processing times under differential electricity pricing

The framework of Industry 3.5 has stimulated the development of smart manufacturing systems that efficiently utilize available resources, including energy; this has led to disruptive innovations in the paradigm of manufacturing systems. The key aim of daily planning and scheduling systems is to maximize productivity by optimizing resource allocation in an organization. This study addresses the energy-efficient single-machine scheduling problem in which the job processing time depends on the quantity of allocated resources and the resource allocation cost is uncertain due to differential electricity pricing. The described problem must respond to the three prominent aspects of Industry 3.5: total resource management, digital decision-making, and smart manufacturing. To meet the total resource management and smart manufacturing requirements, a mixed-integer programming model is developed to determine the operation status of the single machine (i.e., “on” or “idle”) with variable electricity costs, assigned job processing times for time periods, and resource allocation quantities to reduce job processing times. The objective is to minimize the total energy consumption cost by considering both financial (i.e., budget for resource allocation) and environmental constraints (i.e., carbon footprint threshold). This study proposes a type-2 fuzzy control approach integrated with a genetic algorithm (GA), which could be implemented in a decision-support system, thus responding to the digital decision-making requirements of Industry 3.5. The GA is utilized to re-optimize the output of the type-2 fuzzy controller to provide an effective scheduling solution. Our experimental results indicate a 4.20% reduction in the total energy consumption cost compared to the GA approach without controllable processing times. The proposed algorithm can efficiently address the studied problem on a large scale (approximately 200 jobs), where the average computational time required is less than 1 h.

Design of adaptive intuitionistic fuzzy controller for synchronisation of uncertain chaotic systems

This study presents the synchronisation between two non-linear Lorenz chaotic systems using an adaptive intuitionistic fuzzy controller (AIFC). This problem is solved in the presence of external disturbances and uncertainties. Two classes of Lorenz systems have been formulated one as the master and the other as the slave. The master and slave systems have different initial conditions. Under different initial conditions, both systems diverge. This is called the butterfly effect that rules the behaviour of chaotic systems. AIFC is chosen as a different method that has not been used before to synchronise the chaotic systems. The scaling factors of the inputs and the output of the intuitionistic fuzzy controller (IFC) are tuned using the artificial bee colony (ABC) algorithm depending on a specified objective function. Then IFC stability is studied when it is applied to synchronise the Lorenz system. The simulation results achieved to assess the adequacy of the proposed control and to exhibit its dependability to synchronise the Lorenz system.

Finite-time dynamic output-feedback dissipative control for singular uncertainty T–S fuzzy systems with actuator saturation and output constraints

This paper studies the problem of finite-time dynamic output-feedback control for singular uncertainty T–S time-varying delay fuzzy systems with actuator saturation and output constraints. Through a dynamic parallel distributed compensator, the (U,V,R)−α− dissipative output-feedback fuzzy controller is designed. Furthermore, by employing reciprocally convex approach and a new lemma, the sufficient conditions are obtained to guarantee that the systems are finite-time bounded subject to (U,V,R)−α− dissipative performance level. Then, the desired fuzzy state-feedback controller and dynamic output feedback controller are derived by solving the linear matrix inequalities (LMIs). Finally, the feasibility and effectiveness of the proposed method can be validated by some simulations.

Interval Type-2 Fuzzy Sliding-Mode Control of Three-Axis Stabilization Gimbal

Aiming at the attitude control problem of three-axis stabilization gimbal, an algorithm combining interval type-2 fuzzy control and sliding-mode control was designed. Firstly, the mathematical model of the three-axis stabilization gimbal actuator was established after analyzing the mechanical structure of the three-axis stabilization gimbal. Then, a sliding-mode controller was designed based on the position ring of the actuator of three-axis stabilization gimbal. An interval type-2 control was used to reduce chattering in sliding-mode control. The switching function and its derivative are taken as the input of fuzzy control, and the rate of change of the approach law is taken as the output of fuzzy control. Fuzzy control can adjust the gain of sliding-mode surface dynamically, enhance the adaptability on external random disturbance, improve the speed of convergence and weaken chattering. The simulation results show that the output of the controller designed in this paper is more stable, and the tracking of the three-axis stabilization gimbal is more rapid and accurate.