Traditional Fuzzy Control Research Articles

Fuzzy Logic Controller Optimized by BBO for Decentralized Source Based on a SOFC

This paper will investigate the efficiency of an optimal Fuzzy Logic Controller (FLC) for a decentralized source which is established on the basis of a Solid Oxide Fuel Cell (SOFC) that is linked to the electrical network through a voltage inverter and a boost converter. To serve the purpose of this research, a Biogeography Based Optimization (BBO) is applied in order to adjust the parameters of the membership functions (the centers and the widths of the gaussian membership functions in inputs and output) for the purpose of improving the efficacy of traditional fuzzy logic controller. The given control methods have proved to be effective drawing from simulation results, and show that fuzzy logic controller tuned by biogeography based optimization is better and more robust than the traditional fuzzy logic controller for decentralized source based on a SOFC.

Permanent Magnet Synchronous Motor Control Method Based on Fuzzy PI and Sliding Mode Observer

In order to solve the many shortcomings of the current servo system using mechanical position sensors, this article combines the traditional fuzzy PI control and sliding mode observer, and proposes a control method of PMSM based on fuzzy PI and sliding mode observer. Simulink is used for simulation analysis, and the results are compared. It can be seen from the simulation results that the designed control method can efficiently control the permanent magnet synchronous speed control system, so that the system has excellent starting characteristics and dynamic stability.

Hyper-Spherical Search Optimized Fuzzy Logic Control Considering Operating Conditions for Hybrid Tram

Rule-based control strategy has significant advantages of strong robustness and high flexibility, and has gradually become a classical implementation method to optimize the energy management performance of modern energy-storage trams. However, it also faces the problems of over-reliance on expert experience and poor adaptability to operating conditions. Therefore, an improved dynamic power distribution method for tram lithium battery/supercapacitor energy storage system is proposed, which introduces road slope and running speed into the decision variable layer of traditional fuzzy logic control input. Firstly, the membership functions and domain are formulated according to operating conditions, the response time of high power density of supercapacitor is adjusted to improve the dynamic performance of the tram. Secondly, the hyper-spherical search algorithm is used to optimize the weight of fuzzy control rules, the peak current of the lithium battery is minimized and the life span of the energy storage system is increased. Finally, the fuzzy control strategies combined with different decision variables are compared and analyzed by using the data of modern tram western suburban line in Beijing. The results show that the proposed strategy achieves multiple superior to traditional fuzzy control strategy in power distribution, charge state offset ranges of lithium battery and supercapacitor, and energy storage system overall efficiency. Compared with the traditional fixed weight scheme, the peak current of the lithium battery is reduced by 31.02% and the continuous tram mileage is increased by 22.45% in the rule weight dynamic optimization scheme.

Planning the Yaw Correction Trajectory Based on Current Motor Detection

This paper describes distributed adaptive methods for solving the trajectory distortion control problem of intelligent lawnmowers. The strategy we have chosen is to use the association of motor current changes with changes in the movement patterns of the trajectory to try to modify the relationship between the fuzzy controller and the Proportional Integral Derivative (PID) controller based on the traditional fuzzy PID control theory to design a new low-cost control scheme. The scheme uses the differences between the current detection and the linearised models as data feedback and interpolates these linear models with the Takagi-Sugeno (T-S) fuzzy method to approximate the entire non- linear model. Then, the concept of parallel distributed compensation (PDC) synthesizes a state feedback controller. A Linear Quadratic Regulator (LQR) is used to stabilize the system and achieve the desired response. A PID fuzzy control method is then used to form the control of the intelligent lawnmower motor. This strategy uses data feedback obtained by monitoring the left and the right motor current fluctuations, and it can be seen from the practical results that the proposed method is effective in obtaining an ideal linear path with good tracking behaviour in various situations. This paper uses the current monitoring change control method to determine whether the bias voltage is tested and confirms that the method can achieve stable path control.

Deep Reinforcement Learning Based Dynamic Proportional-Integral (PI) Gain Auto-Tuning Method for a Robot Driver System

To meet the growing trend of stringent fuel economy regulations, automakers around the world are designing modules such as engines, motors, transmissions and batteries to be as efficient as possible. In order to verify the effect of these designs on the overall fuel efficiency of the vehicle, the vehicle equipped with each module is placed on the chassis dynamometer, driven to follow the target vehicle speed, and actual fuel efficiency is measured. These tests are traditionally performed by human operators, but are now being replaced by robots (physical or software) to ensure the accuracy and reliability of test results. Although the conventionally proposed proportional integral (PI)-based controller has a simple structure and is easy to implement, it requires the process of finding the optimal gain whenever the test conditions such as vehicle or drive cycle change, which is difficult and time consuming. In this study, we propose a proportional integral controller gain adjustment algorithm using deep reinforcement learning. The reinforcement learning agent learns to dynamically modify the PI gain value of the acceleration/deceleration pedal to better follow the target vehicle in a simulation environment. The perturbation is used in each training episode to reduce the difference between the simulation and real testing environment. Upon completion of the training process, the trained agent performs an adjustment process that generates a reference gain table. We then use this reference gain table to perform a real test. The performance of the proposed system was evaluated using Hyundai Tucson HEV (NX4) on an AVL chassis dynamometer. We also compared the performance of our proposed algorithm to traditional fuzzy logic-based PI controllers. The obtained experimental results show that the proposed control system achieved a performance improvement of aounrd 46.8% compared to the conventional PI control system in terms of root mean square error.

Design of and Research into a Multiple-Fuzzy PID Suspension Control System Based on Road Recognition

By analyzing the shortcomings of the traditional fuzzy PID(Abbreviation for Proportional, Integral and Differential) control system (FPID), a multiple fuzzy PID suspension control system based on road recognition (MFRR) is proposed. Compared with the traditional fuzzy PID control system, the multiple fuzzy control system can identify the road grade and take changes in road conditions into account. Based on changes in road conditions and the variable universe and secondary adjustment of the control parameters of the PID controller were carried out, which makes up for the disadvantage of having too many single input parameters in the traditional fuzzy PID control system. A two degree of freedom 1/4 vehicle model was established. Based on the suspension dynamic parameters, a road elevation algorithm was designed. Road grade recognition was carried out based on a BP neural network algorithm. The experimental results showed that the sprung mass acceleration (SMA) of the MFRR was much smaller than that of the passive suspension system (PS) and the FPID on single-bump and sinusoidal roads. The SMA, suspension dynamic deflection (SDD) and tire dynamic load (TDL) of the MFRR were significantly less than those of the other two systems on roads of each grade. Taking grade B road as an example, compared with the PS, the reductions in the SMA, SDD and TDL of the MFRR were 40.01%, 34.28% and 32.64%, respectively. The control system showed a good control performance.

Application of adaptive fuzzy control to suppression vibration response of floating offshore wind turbine

Floating offshore wind turbine is a complex rigid-flexible coupling nonlinear system, and the accurate dynamic model is difficultly established. Therefore, the wind-wave interference cannot be improved by adopting the conventional control strategy. In order to solve this problem, an adaptive fuzzy controller (AFC) is used to suppress the dynamic response of floating wind turbine. Two correction factors are introduced to optimize the fuzzy rule, and the traditional fuzzy controller (FC) is firstly obtained. Since the balance positions change and structural parameter perturbation of the wind turbine, an AFC is designed and validated. Finally, the suppression vibration responses ability of floating offshore wind turbine by using the different control strategies is studied under the random wind-wave disturbance and blade pitch control system coupling effect. The simulation results show that the tracking ability of the AFC to the target value is obviously higher than that of the FC; Comparing with the passive control strategy, the suppression vibration effect on the power spectral density (PSD) of the platform pitch (PFPI) motion peak can increase by 39.06% by adopting the AFC.

Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme

This paper proposes a signal timing scheme through a two-stage fuzzy logic controller. The controller first determines the signal phase and then adjusts the green time. At the first stage, the adaptive membership function of vehicle arrival rate is improved to adapt to the changing traffic flow. In addition to arrival rate and queue vehicles, a specific phase order rule is considered to avoid disordered phase selection in fuzzy control. At the second stage, the green time detection module decides whether to extend the current green time or switch phases every few seconds and the vehicle arrival rate is not required as the input to controller in real-time detection. Differential evolution algorithm with low space complexity and fast convergence is applied to optimize the fuzzy rules for avoiding artificial uncertainty. Simulation experiments are designed to compare traditional fuzzy controller, fixed-time controller, and fuzzy controller without flow prediction. Results show that the current proposed method in this paper can reduce vehicle delay significantly.

A Fuzzy Adaptive PID Control Algorithm with Improved Quantification Factor

The phase-shifting full bridge digital power supply based on Fuzzy PID control is difficult to realize the full mapping from the fuzzy universe to the actual output domain, resulting in the loss of fuzzy control quantity, resulting in the dynamic performance problems of large amplitude and long time output fluctuation. In this paper, a fuzzy adaptive PID control algorithm with improved quantification factor is proposed. The online adjustment function of quantification factor in fuzzy universe is introduced in the quantification link. The function can adjust the fuzzy quantification factor in real time according to the current sampling error and error change rate of the system. In order to verify the effectiveness of the improved algorithm, the traditional PID control, the traditional fuzzy PID control and the quantification factor improved fuzzy PID control are applied to the phase-shifting full bridge small signal disturbance model respectively. The results show that the fuzzy adaptive PID control algorithm with the quantification factor online adjustment function has higher dynamic performance.

Fuzzy control mathematical modelling method based on dynamic particle swarm optimisation training

Aiming at the false correlation problems under set number limit condition in the fuzzy control mathematic modelling process, combined with dynamic particle swarm optimisation training algorithm, the traditional fuzzy control mathematical model based on the Nash equilibrium solution method is difficult to converge to the optimal solution of the state space, leading to bad control performance. This paper proposes the fuzzy control mathematical modelling method based on dynamic particle swarm optimisation training, constructs the general structure model of fuzzy control, describes the standard particle algorithm under the constraint of learning samples of random functional, obtains the global optimal solution of control domain of fuzzy control parameters, and conducts particle swarm optimisation training by adopting the position vector fitness updating method. The research results show that the new method can make every step state update get more effective observation information, reduce the error caused by the difficult use of observation data, reduce the computation cost and improve the accuracy of fuzzy control.

Analysis and Design of a Double Fuzzy PI Controller of a Voltage Outer Loop in a Reversible Three-Phase PWM Converter

Aiming at the application of a reversible three-phase pulse width modulation (PWM) converter with a wide range of AC side voltage and DC side voltage, a double fuzzy proportional integral (PI) controller for voltage outer loop was proposed. The structures of the proposed controller were motivated by the problems that either the traditional PI controller or single fuzzy PI controller cannot achieve high performance in a wide range of AC and DC voltage conditions. The presented double fuzzy controller studied in this paper is a sub fuzzy controller in addition to the traditional fuzzy PI controller; in particular, the sub fuzzy controller can get the auxiliary correction of PI control parameters according to the AC side voltage and the DC side given voltage variation of PWM converter after the reasoning of the sub fuzzy controller, while the traditional fuzzy PI controller outputs the correction of PI control parameters according to the DC voltage error and its error change rate. In this paper, the traditional fuzzy PI controller can be called the main fuzzy controller, and the adaptive adjustment of PI control parameters of the voltage outer loop is the sum of the PI parameter correction output by the main fuzzy controller and the auxiliary PI parameter correction output by the sub fuzzy controller. Finally, the experimental results show that the reversible three-phase PWM converter can achieve excellent dynamic and static performance in a wide range of AC voltage and DC voltage applications by using the proposed double fuzzy PI controller.

Optimal Design of Adaptive Robust Control for the Delta Robot with Uncertainty: Fuzzy Set-Based Approach

An optimal control design for the uncertain Delta robot is proposed in the paper. The uncertain factors of the Delta robot include the unknown dynamic parameters, the residual vibration disturbances and the nonlinear joints friction, which are (possibly fast) time-varying and bounded. A fuzzy set theoretic approach is creatively used to describe the system uncertainty. With the fuzzily depicted uncertainty, an adaptive robust control, based on the fuzzy dynamic model, is established. It designs an adaptation mechanism, consisting of the leakage term and the dead-zone, to estimate the uncertainty information. An optimal design is constructed for the Delta robot and solved by minimizing a fuzzy set-based performance index. Unlike the traditional fuzzy control methods (if-then rules-based), the proposed control scheme is deterministic and fuzzily optimized. It is proven that the global solution in the closed form for this optimal design always exists and is unique. This research provides the Delta parallel robot a novel optimal control to guarantee the system performance regardless of the uncertainty. The effectiveness of the proposed control is illustrated by a series of simulation experiments. The results reveal that the further applications in other robots are feasible.

Energy Control Strategy of Fuel Cell Hybrid Electric Vehicle Based on Working Conditions Identification by Least Square Support Vector Machine

Aimed at the limitation of traditional fuzzy control strategy in distributing power and improving the economy of a fuel cell hybrid electric vehicle (FCHEV), an energy management strategy combined with working conditions identification is proposed. Feature parameters extraction and sample divisions were carried out for typical working conditions, and working conditions were identified by the least square support vector machine (LSSVM) optimized by grid search and cross validation (CV). The corresponding fuzzy control strategies were formulated under different typical working conditions, in addition, the fuzzy control strategy was optimized with total equivalent energy consumption as the goal by particle swarm optimization (PSO). The adaptive switching of fuzzy control strategies under different working conditions were realized through the identification of driving conditions. Results showed that the fuzzy control strategy with the function of driving conditions identification had a more efficient power distribution and better economy.

Robust control design of electric helicopter tail reduction system: Fuzzy and optimal view

The optimal robust control with a fuzzy approach is applied to design the electric helicopter tail reduction system in this article. Firstly, the fuzzy dynamical model of the electric helicopter tail reduction system with uncertainties and external disturbances is established, which may be time varying. Then, we propose a deterministic robust controller (differing from IF-THEN rules in traditional fuzzy control) to solve the uncertain problem in electric helicopter tail reduction systems. The electromechanical system with the proposed controller is proved to be stable via the Lyapunov function. The control gain with an optimal design is considered, which minimizes a fuzzy performance index associated with both the control error and the control cost. Furthermore, simulations compared with linear-matrix-inequality control are made to validate the effectiveness and stability of the optimal robust controller. All results show that the performance of the fuzzy electric helicopter tail reduction system can be always guaranteed by this optimal robust control approach.

Self-Tuning fuzzy controller for sun-tracker system using Gray Wolf Optimization (GWO) technique

The demand of electric power consumption is increasing very rapidly worldwide and to fulfill this requirement, solar energy is one of the most viable solution as renewable energy source. Photovoltaic (PV) cell based sun-tracker system (STS) produces maximum current when sunlight vertically incident on its surface. Hence, there is a need of optimized continuous axis position control of STS to achieve maximum output current. This task can be done on the basis of the fuzzy control system. Usually, in the traditional fuzzy control system (FCS), tuning of designed fuzzy parameter is done by trial and error method. However, this type of FCS parameter tuning approach may or may not give optimal solution. Thus, in presented work, an optimal tuning technique with Takagi, Sugeno and Kang (TSK) fuzzy controller (TFC) using Gray Wolf Optimization (GWO) for STS has been proposed. In order to validate the proposed work, different objective functions have been employed to carry out fuzzy controller parameter optimization. A comparative analysis has been performed on the basis of three parameters: settling time, maximum-overshoot and optimal fuzzy parameter on different constrain set. The results obtained with the GWO optimization algorithm were also compared with other popular population algorithms, i.e. Whale Optimization Technique (WOT) and Particle Swarm Optimization (PSO) algorithms.

An Improved Fuzzy Logic Control Method for Path Tracking of an Autonomous Vehicle

Highlights This study focused on the path tracking accuracy of a two-wheel steering, two-wheel drive autonomous vehicle. An improved fuzzy logic control method (IFM) was designed to control the steering angle of the front wheels. The IFM was better than traditional pure pursuit and fuzzy logic control methods for automatic path tracking. Abstract. This article presents a path-tracking algorithm based on an improved fuzzy logic control method (IFM) that generates optimal steering angles for an autonomous wheeled vehicle to track a sequence of waypoints. The path-tracking algorithm was designed to focus on the vehicle performance, including tracking accuracy, stability, and convergence speed. A prototype vehicle with two-wheel steering and two-wheel drive (2WS-2WD) was developed as a test platform and equipped with a series of sensors for evaluating the IFM. In this study, we considered two variables, i.e., lateral deviation (d) and heading deviation (θ), as inputs for the control system. The output (δ) was the steering angle of the front wheels as controlled by an electric linear actuator. A maximum steering angle strategy was adopted for rapid convergence when the vehicle was far from the desired path or when the heading deviation was large. An update-line distance algorithm was used for finding the best turning time as the vehicle moved closer to the next waypoint. To validate the proposed algorithm, testing was performed to compare traditional pure pursuit (TPP), traditional fuzzy logic control method (TFM), and the IFM in tracking two types of paths (straight and rectangular). Traveling at 0.5 m s-1 and with a signal sampling period of 0.5 s, the prototype converged to the desired straight path from different initial states. When the vehicle reached a stable state, the steady-state error was between 5.0 and 6.5 cm with a mean of ≤6.0 cm. The settling distance in the straight path test was between 125.0 and 422.3 cm, and the convergence time was in the range of 11.5 to 16.0 s; the greater the initial deviation of the vehicle was, the greater the settling distance and settling time were. In the 15 m x 14 m rectangular path test, the overall mean error of the IFM was 14.4 cm when the initial lateral and heading deviations were 0, and the maximum deviation was 67.4 cm, which occurred at the corners. The test results show that the proposed IFM had better accuracy, stability, and convergence speed for path tracking when compared to the TPP and TFM algorithms.

Robust Adaptive Fuzzy Control Using Genetic Algorithm for Dynamic Positioning System

This paper aims to develop a genetic algorithm to adjust an fuzzy controller for Vessels’ Dynamic Positioning System (Vessels’ DPs). It is well-known that nonlinearities affecting the control accuracy of the DPs are related to the arrangement of different types of thrusters in the vessels, such as azimuth thrusters (electric, L-drive, and Z-drive), bow thrusters, stern thrusters, water jets, and propulsion propellers with rudders. Compared with the traditional fuzzy control methods, the proposed Robust Adaptive Fuzzy Control using Genetic Algorithm (RAFC-GA) not only overcomes the influence of nonlinearities in the DPs, but also eliminates the impact of parameter uncertainties. Therefore, the tracking performance is excellent and robustness is maintained. The RAFC-GA control method is superior to the conventional fuzzy control methods in the two following aspects: 1) to find the optimal values for the fuzzy structure parameters to satisfy the robust condition under the effect of disturbances and nonlinearities in the DPs without weakening the output tracking performance and robustness, 2) to improve the quality of the system by optimizing values for the fuzzy structure using genetic algorithm which dynamically adjusted the coverage domain width and the overlap degree of membership functions. Simulation results of the RAFC-GA are evaluated in comparison with other methods. The RAFC-GA performs the desired transient response of DPs better than others in three case studies, which proves the effectiveness of the proposed solution.

T–S Fuzzy Control of Travelling-Wave Ultrasonic Motor

The operation of ultrasonic motor is characterized by obvious nonlinearity. Appropriate motor control strategy is an important prerequisite for promoting the wider application of ultrasonic motors. PID controller is widely used in motor motion control products. Due to the nonlinearity of the ultrasonic motor, the PID control parameter values that can achieve good performance are very different at different speeds. In this paper, aiming at the application requirements of ultrasonic motor speed control, a fuzzy controller with simple structure is proposed to realize nonlinear variable-parameter PID control by using nonlinear input–output mapping ability of fuzzy logic. The proposed fuzzy controller has only one input variable with three linguistic values. In order to match the nonlinear operating characteristics of the ultrasonic motor and improve the control precision, the fuzzy rule is designed as the Takagi–Sugeno form. Takagi–Sugeno defuzzification method is adopted. The calculation formula of control quantity given by the fuzzy controller is similar to that of the PI controller, but the control parameters are nonlinearly changed. Compared with the traditional fuzzy controller, the structure of the proposed controller is simple and the amount of online computations is greatly reduced. Experimental results under various operating conditions show that the proposed controller has good control effect and it has robustness to disturbances such as drive voltage amplitude and load torque variation.

Time delay analysis and constant time-delay compensation control for MRE vibration control system with multiple-frequency excitation

Magnetorheological elastomer (MRE) vibration isolation method has attracted more attention in vibration engineering fields. Time delay in real time control system has an important influence on the control effect, especially for the vibration system with periodic excitations. For the phase difference between the response signal and controlled signal caused by time delay will reduce the vibration control effect or even make the vibration more intense. Therefore, the main purpose in this paper is to improve the vibration attenuation of the MRE vibration control system by analyzing the cause of the time delay and designing the time-delay compensator (TDC) based on the traditional fuzzy controller. The time delay in MRE vibration control system, may be generated by controller, actuator, filter and current driver, was analyzed in theory, and experimental test was carried out to estimate the value. Then a constant TDC was designed for the various vibration amplitude and frequency (single frequency and multiple frequencies). Finally, the real-time control experiments were conducted to verify the effectiveness of the proposed TDC, and the results illustrated that the acceleration RMS with TDC has been attenuated more greatly than that without TDC. Meanwhile, the lower energy consumption is another benefit.

From fuzzy center average defuzzifier (CAD) to fuzzy lookup table controller (FLTC) with an efficient Heaviside search algorithm (HSA)

This paper proposes an alternate interpretation of the traditional lookup table controller via the center average defuzzifier (CAD) in fuzzy set theory with a set of disjoint block pulse membership functions (DBPMFs). The DBPMFs with CAD can be proved to be equivalent to the traditional fuzzy lookup table controller (FLTC). Further, a new Heaviside search algorithm (HSA) is also proposed to implement the FLTC in a more efficient way. The HSA is to find the indices for every fuzzy input variable independently, in order to speed up the table-searching process. The computational complexity of our new HSA is far less than the complexity of the traditional linear search algorithm (LSA), where HSA is the sum of Ri, where Ri is the number of membership functions for ith input variable in FLTC. The average computational complexity in LSA is the product of Ri divided by 2. The HSA also reduces the complexity of coding significantly in the same order as the comparison of computation complexities between HSA and LSA. The balancing of the inverted pendulum system is adopted as the benchmark to show the feasibility and efficiency of this new FLTC and HSA.