Takagi-Sugeno Fuzzy Logic Controller Research Articles

Comparative Investigation of DSP-Based Speed Control of PMSM Using Proportional Integral and Takagi-Sugeno Fuzzy Logic Controller

This paper presents the simulation and experimental results of speed control of a three-phase Permanent Magnet Synchronous Motor (PMSM). The control techniques employed in this work include a Proportional Integral (PI) controller and a Takagi-Sugeno Fuzzy Logic Controller (TS-FLC). While theory and simulation help in predicting the PI controller settings for real drive implementation, further adjustment of the PI controller coefficients is still needed for optimal performance. However, the complexity of PI controller tuning can be overcome by employing a fuzzy logic controller, which is less affected by variations in model parameters and load torque. Two variants of the proposed TS-FLC are implemented: a simple algorithm with 9 rules and a standard algorithm with 49 rules. The drive system utilizes Field Oriented Control (FOC). The control program is developed using the C/C++ programming language and executed on the Texas Instruments TMS320LF28335 Digital Signal Processor (DSP), known for its intelligent motor control capabilities. The drive system evaluation was performed through simulations and experimentation using the MCK28335 professional development kit provided by Technosoft Company. The simulation and experimental results of the proposed controllers are compared under various conditions, including speed reversal and load torque variations.

Designing a Ship Autopilot System for Operation in a Disturbed Environment Using the Adaptive Neural Fuzzy Inference System

Efficient ship guidance, fuel savings, and reduced human control have long been a key focus in developing intelligent controllers. The integration of neural networks and fuzzy logic control offers numerous advantages, creating a robust and adaptive system capable of handling complex dynamics and uncertainties. This intelligent control system learns from its environment and adjusts behavior, making it effective in challenging situations. Additionally, it improves system efficiency, reduces energy consumption, and minimizes human intervention, enhancing safety and reducing errors. This study presents an intelligent control approach, titled “Designing a Ship Autopilot System for Operation in a Disturbed Environment using the Adaptive Neural Fuzzy Inference System”, combining a neural network and fuzzy logic control to steer ships. A 6DOF dynamic model is constructed, simulating ship operations with noise signals. The ANFIS controller comprises six layers, with a distinct composition rule expressing conclusions as linear equations of input variables. Layer 1 has two input signals, layer 2 represents fuzzy rules with six nodes, and layers 3, 4, and 5 contain nine nodes each. Layer 6 combines output signals from layer 5, following the first-order Takagi–Sugeno fuzzy logic control model. Simulation results using MATLAB/Simulink demonstrate the superiority of the ANFIS controller over the PID controller, significantly improving stability and trajectory accuracy.

Takagi–Sugeno Fuzzy Logic Controller for DFIG Operating in the Stand-Alone Mode: Simulations and Experimental Investigation

Takagi–Sugeno Fuzzy Logic Controller for DFIG Operating in the Stand-Alone Mode: Simulations and Experimental Investigation

Optimization of Fuzzy Controller for Predictive Current Control of Induction Machine

An optimization procedure for type 1 Takagi–Sugeno Fuzzy Logic Controller (FLC) parameter tuning is shown in this paper. Ant colony optimization is used to obtain the optimal controller parameters, and only a small amount of post-optimization parameter adjustment is needed. The choice of controller parameters is explained, along with the methodology behind the criterion for objective function value calculation. The optimized controller is implemented as an outer-loop speed controller for Predictive Current Control (PCC) of an induction machine. The performance of the proposed control method is compared with that of several other model predictive control methods. The results show a 55% decrease in speed tracking error and 74% decrease in torque overshoot.

Parameter Optimization of Hole-Slot-Type Magnetron for Controlling Resonant Frequency of Linear Accelerator 6 MeV by Reverse Engineering Technique

This paper presents the parameter optimization of a twelve-hole-slot-type magnetron based on a reverse engineering technique to improve a 6-MeV linear accelerator (LINAC) operation for fruit sterilization. The magnetron structural dimensions are measured by a coordinate measuring machine (CMM) that has tolerance on a dimension of 0.5 µm to analyze the resonant frequency with a desired operating point of the magnetron in the dominant mode. There are two methods of analysis using a proper parameter for the magnetron operation. The first method is mathematical model analysis of an equivalent resonant parallel circuit. The other is 3D-model drawing of the magnetron based on particle-in-cell (PIC) using computer simulation technology microwave studio (CST). The results are demonstrated by the position of the resonant frequency of each mode of operation, and the radius and tuner slot distance of a cavity within the structures of the magnetron cause a resonant frequency change. The suitable parameters of the voltage and magnetic field supply are desired to control a resonant frequency at 2.9982 GHz by using the Takagi–Sugeno fuzzy logic control (FLC) algorithm to control a resonant frequency at 2.9982 GHz. The results of the FLC algorithm application show that the LINAC can produce X-rays with a constant dose rate for an hour with a disturbance in the range of 38 to 42 °C temperature and 1 × 10−9 to 5 × 10−8 torr vacuum pressure.

Design of Fuzzy TS-PDC Controller for Electrical Power System via Rules Reduction Approach

In this paper, a new Takagi–Sugeno Fuzzy Logic controller (TS-FLC) is presented and applied for modeling and controlling the nonlinear power systems even in the presence of disturbances. Firstly, a nonlinear mathematical model for the electrical power system is presented with consideration of PSS and AVR controller. Then, a Takagi–Sugeno Fuzzy Logic controller is employed to control power system stability. Nevertheless, the study of the stability of Takagi–Sugeno fuzzy models will be difficult in the case where the number of nonlinearities is important. To cope with this problem, this study proposed a methodology to reduce the number of rules and to guarantee the global stability of the power system. The new model included only two rules. All the other nonlinearities were considered as uncertainties. In addition, a Parallel Distributed Compensation controller is designed using the Linear Matrix Inequalities constraints in order to guarantee system stability. Finally, this approach is applied on a Single Machine Infinite Bus affected by fault perturbation. To show the novelty of Takagi Sugeno’s method, we compared our approach to the Taylor linearization method. The numerical simulations prove the feasibility and performance of the proposed method.

Salp swarm algorithm-based TS-FLCs for MPPT and fault ride-through capability enhancement of wind generators

This article presents an optimum design of Takagi–Sugeno fuzzy logic controllers (TS-FLCs) to enhance capability of fault ride-through (FRT) and the maximal power point tracking (MPPT) of the grid-tied wind farms. To obtain the optimum TS-FLCs, salp-swarm-algorithm (SSA) applied to minimize the sum of integral squared-error (ISE) function, where the variables of the cost function are the factors of Gaussian membership functions and the control rules of eight TS-FLCs. After that, the optimized TS-FLCs are applied to control the grid-tied variable-speed wind generator system, which is simulated using PSCAD/EMTDC. Thus, the transient and dynamic responses revealed that the optimum TS-FLCs have better stability margins than the optimum proportional–integral (PI) controllers. Furthermore, a realistic variable wind speed measured data are implemented to test the robustness of the MPPT based on the optimal TS-FLCs to extract more power than the MPPT based on optimal PI controllers.

A new technique with improved control quality of nonlinear systems using an optimized fuzzy logic controller

Fuzzy logic controllers are increasingly applied to control complex systems because they have several advantages. The objective of this work is to propose a new technique to optimize a Takagi-Sugeno fuzzy logic controller with quality using the Non-dominated Sorting Genetic Algorithm-II by optimizing three objectives functions which are a cost function, the number of fuzzy inference rules, and the maximum instantaneous quadratic error. In this technique, the Multi-Criteria Decision-Making approach is used to choose one of the best controllers from the Pareto set of the last generation of the genetic algorithm. The proposed technique ensure: (i) that the output of the controlled system correctly follows the desired reference. (ii) the acceleration of the control process and (iii) avoid the existence of large overshoots; which are usually observed when applying commands to complex processes with variable behavior. At the end of the control process, a robustness test is performed to verify the efficiency of the proposed technique. It is shown here that the optimization of the third objective function, allows the improvement of the control quality. This new technique can be used to improve expert and intelligent systems based on fuzzy rules to control high complex systems with variable behavior which they have disturbing overshoots during their control. This technique; allows to accelerate the calculation of the control law of expert systems based on fuzzy rules; while ensuring that the quality of the control and the output signal are good.

Mobile robot visual navigation based on fuzzy logic and optical flow approaches

This paper presents the design of mobile robot visual navigation system in indoor environment based on fuzzy logic controllers (FLC) and optical flow (OF) approach. The proposed control system contains two Takagi–Sugeno fuzzy logic controllers for obstacle avoidance and goal seeking based on video acquisition and image processing algorithm. The first steering controller uses OF values calculated by Horn–Schunck algorithm to detect and estimate the positions of the obstacles. To extract information about the environment, the image is divided into two parts. The second FLC is used to guide the robot to the direction of the final destination. The efficiency of the proposed approach is verified in simulation using Visual Reality Toolbox. Simulation results demonstrate that the visual based control system allows autonomous navigation without any collision with obstacles.

Enhanced whale optimization algorithm for maximum power point tracking of variable-speed wind generators

This paper proposes an enhancement of the meta-heuristic whale optimization algorithm (WOA) for maximum power point tracking (MPPT) of variable-speed wind generators. First of all, twenty-three benchmark functions tested the enhanced whale optimization algorithm (EWOA). Then the statistical results of EWOA compared with the results of other algorithms (WOA, salp swarm algorithm (SSA), enhanced SSA (ESSA), grey wolf optimizer (GWO), augmented GWO (AGWO), and particle swarm optimization (PSO). Also, the non-parametric statistical test and convergence curves proved the superiority and the speed of the EWOA. After that, the EWOA and WOA are implemented to design optimal Takagi–Sugeno fuzzy logic controllers (FLCs) to enhance the MPPT control of variable-speed wind generators. Moreover, real wind speed data has confirmed the robustness of optimal EWOA-MPPT. In conclusion, the simulation results revealed that the EWOA is a promising algorithm to be applied for solving different engineering problems.

Combination of Data-Driven Active Disturbance Rejection and Takagi-Sugeno Fuzzy Control with Experimental Validation on Tower Crane Systems

In this paper a second-order data-driven Active Disturbance Rejection Control (ADRC) is merged with a proportional-derivative Takagi-Sugeno Fuzzy (PDTSF) logic controller, resulting in two new control structures referred to as second-order data-driven Active Disturbance Rejection Control combined with Proportional-Derivative Takagi-Sugeno Fuzzy Control (ADRC–PDTSFC). The data-driven ADRC–PDTSFC structure was compared with a data-driven ADRC structure and the control system structures were validated by real-time experiments on a nonlinear Multi Input-Multi Output tower crane system (TCS) laboratory equipment, where the cart position and the arm angular position of TCS were controlled using two Single Input-Single Output control system structures running in parallel. The parameters of the data-driven algorithms were tuned in a model-based way using a metaheuristic algorithm in order to improve the efficiency of energy consumption.

A Fuzzy Logic Controller Based on Maximum Power Point Tracking Algorithm for Partially Shaded PV Array-Experimental Validation

In the last two decades, the maximum power point tracking (MPPT) methods for PV system is becoming very interesting subject. Among these methods there is Fuzzy-MPPT, which is mainly based on two inputs and at least 25 rules. This structure need more calculation time and not easy to be implemented in hardware. For these reasons and in order to facilitate the MPPT synthesis, this paper proposes an intelligent MPPT controller based on a single-input Takagi–Sugeno fuzzy logic controller (SI-TS-FLC) with three linguistic variables and three rules. The proposed controller is simulated using MATLAB-SIMULINK for a PV system, which consists of a PV generator, DC-DC converter, and resistive load under varied temperature and irradiance levels. An experimental study is conducted using DSPACE 1104 card real-time board under partial shading condition. Simulation and experimental results show that the proposed controller exhibits less settling time and lower overshoot than the commonly used perturb and observe (P&O) algorithm in the transient state and minimum oscillation around the optimal operating point.DOI: http://dx.doi.org/10.5755/j01.eie.24.4.21476

Second Order Intelligent Proportional-Integral Fuzzy Control of Twin Rotor Aerodynamic Systems

This paper proposes a hybrid controller that consists of a second order data–driven Model–Free Control (MFC), also known as intelligent proportional–integral (iPI), and a Takagi-Sugeno Fuzzy (TSF) logic controller for nonlinear Multi Input–Multi Output (MIMO) twin rotor aerodynamic systems (TRASs). The pitch position control is carried out using a single input–single output control system structure. The hybrid controller (the second order MFC algorithm and the TSF logic controller) is referred to as second order MFC–TSF controller. The performance of the control system structure (CSS) with the new second order MFC–TSF controller is compared with the performance of a CSS with the second order MFC controller, which is optimally tuned in a model-based manner by a Grey Wolf Optimizer algorithm. Experimental validation results on a nonlinear TRAS laboratory equipment are included.

TSFLC based DC link voltage regulation of grid connected DC micro grid

Integration of micro grid with utility power network has major concerns such as active power control and maintaining desired power quality (PQ) standards. Among several power quality issues, voltage dip and harmonics are critical concerns impacting the overall performance and stability of system. In this paper, a grid connected DC micro grid (DCMG) is designed and controlled to ensure requisite power allocation between the micro-grid and utility grid system vis-a-vis DC link voltage regulation, load balancing and harmonic elimination. The utility grid and DCMG system are interfaced via voltage source converter (VSC). The DC link voltage of VSC is regulated using Takagi-Sugeno fuzzy logic controller (TSFLC) based synchronous reference frame technique to compensate for the reactive power demanded by the load. In the proposed architecture, DCMG consists of 8 kW photovoltaic (PV) energy system with maximum power point tracking (MPPT), 6 kW wind energy generation system with MPPT, 3 kW fuel cell (FC) energy systems and a VSC interfaced to three phase utility grid. Further, the TSFLC is compared with conventional PI controller under different operating conditions. It has advantages over classical controller such as better performance under dynamics and can be expressed in the form of the exact linear function. The effectiveness of proposed control method is validated under MALAB/SIMULINK software under different loading conditions.

TSFLC based DC link voltage regulation of grid connected DC micro grid

Integration of micro grid with utility power network has major concerns such as active power control and maintaining desired power quality (PQ) standards. Among several power quality issues, voltage dip and harmonics are critical concerns impacting the overall performance and stability of system. In this paper, a grid connected DC micro grid (DCMG) is designed and controlled to ensure requisite power allocation between the micro-grid and utility grid system vis-à-vis DC link voltage regulation, load balancing and harmonic elimination. The utility grid and DCMG system are interfaced via voltage source converter (VSC). The DC link voltage of VSC is regulated using Takagi-Sugeno fuzzy logic controller (TSFLC) based synchronous reference frame technique to compensate for the reactive power demanded by the load. In the proposed architecture, DCMG consists of 8 kW photovoltaic (PV) energy system with maximum power point tracking (MPPT), 6 kW wind energy generation system with MPPT, 3 kW fuel cell (FC) energy systems and a VSC interfaced to three phase utility grid. Further, the TSFLC is compared with conventional PI controller under different operating conditions. It has advantages over classical controller such as better performance under dynamics and can be expressed in the form of the exact linear function. The effectiveness of proposed control method is validated under MALAB/SIMULINK software under different loading conditions.

Fuzzy logic controller for stabilization of biped robot gait

The article centers round the problem of stabilization of biped robot gait through smoothing out the jumps of first and second order derivatives of a biped robot control vector using the fuzzy logic approach. The structure of a composite Takagi-Sugeno fuzzy logic controller developed by the authors is presented. The simulation study of a robot gait with climbing an obstacle is carried out and the results provided in the article showed that the developed controller performed significantly better than the analytical formula model in terms of smoothing out the derivatives of the control vector.

Autonomous path following by fuzzy adaptive curvature-based point selection algorithm for four-wheel-steering car-like mobile robot

Autonomous path following in mobile robots with nonholonomic constraints can be divided into two problems: first, selecting the tracking point, and then designing an appropriate controller to follow the selected point. When selecting tracking point, considering the kinematics of the robot as well as characteristics of the desired path is of considerable importance. For these purposes, a curvature-based point selection algorithm is first proposed for the car-like mobile robot with independent steering mechanisms. Each instant, the proposed algorithm finds a point which enables the robot to be tangent to the path at that specific point. Afterwards, in order to take into consideration characteristics of the path, a fuzzy adaptive curvature-based point selection algorithm is proposed. In this algorithm, in addition to the kinematic constraints, path characteristics are also considered in selecting the tracking point. This gives the robot the ability to show better performance when the path slope changes suddenly, resulting in less overshoot/undershoot around the desired path. The fuzzy adaptive curvature-based point selection algorithm is combined with a controller based on the Takagi–Sugeno fuzzy logic, such that the fuzzy adaptive curvature-based point selection algorithm selects the tracking point, while the Takagi–Sugeno fuzzy logic controller makes the robot follow the selected point. Finally, the fuzzy adaptive curvature-based point selection–Takagi–Sugeno fuzzy logic tracker is implemented on the robot, and the results are compared with a similar path-following algorithm. Obtained results show that for tracking a piecewise linear path, the steering activity and the following root mean square error decrease from 170.74° and 0.37 m for the conventional fuzzy controller to 63.37° and 0.09 m for the fuzzy adaptive curvature-based point selection–Takagi–Sugeno fuzzy logic controller, respectively.

A nonlinear Takagi-Sugeno fuzzy logic control for single machine power system

This paper presented a nonlinear controller based on fuzzy logic control to enhance and ensure the power system stability. The nonlinear model was transformed on Takagi-Sugeno fuzzy model. Then, a new regulator was constructed using the concept so-called parallel distributed compensation. This controller was used to stabilize and control the system. The convex optimisation problem involving linear matrix inequalities and Lyapunov function was used. The objective of these techniques was to improve the robustness of fuzzy controller and calculate the stable feedback gains. Finally, the proposed approach was applied on single machine infinite bus affected by serious disturbance. Simulation results demonstrate the performance and effectiveness of the developed method.

Takagi-Sugeno fuzzy logic controller for Liu-Chen four-scroll chaotic system

Chaos modelling, chaos control and chaos synchronisation are active areas in chaos theory, while research in these areas has potential applications in many fields like nonlinear optics, oscillations, chemical reactions, biology, neurology, secure data encryption, secure communications, robotics, neural networks, fractals and nonlinear circuits design. Especially, chaos control is an important research area, and it has a wide range of applications like control of turbulence, dynamics of lasers, plasma, chemical reactions such as Belousov-Zhabotinsky reaction, ecological systems and cardiac arrhythmias. Until now, many methods for chaos control such as open-loop control based on periodic system excitation, closed-loop control such as OGY method and time-delayed feedback control such as Pyragas method have been used. In this paper, a fuzzy logic control using fuzzy sets and fuzzy logic for the control of chaotic systems has been proposed. In more detail, this work presents a fuzzy logic controller design of a chaotic system using Takagi-Sugeno fuzzy model. The stability analysis of the fuzzy logic controller system is carried out using Barbashin-Krasovskii theorem. For the proposed fuzzy logic control, the Liu-Chen four-scroll chaotic system was used. Finally, the simulation results of the proposed control method present the effectiveness of this method.

Takagi-Sugeno Fuzzy Perpose as Speed Controller in Indirect Field Oriented Control of Induction Motor Drive

This paper deal with the problem in speed controller for Indirect Field Oriented Control of Induction Motor. The problem cause decrease performance of Induction Motor where it widely used in high-performance applications. In order decrease the fault of speed induction motor, Takagi-Sugeno type Fuzzy logic control is used as the speed controller. For this, a model of indirect field oriented control of induction motor is built and simulating using MATLAB simulink. Secondly, error of speed and derivative error as the input and change of torque command as the output for speed control is applied in simulation. Lastly, from the simulation result overshoot is zero persent, rise time is 0.4s and settling time is 0.4s. The important data is steady state error is 0.01 percent show that the speed can follow reference speed. From that simulation result illustrate the effectiveness of the proposed approach.