Design Of Fuzzy Controller Research Articles

Robust stability and fuzzy controller synthesis for a class of hybrid re-entrant manufacturing systems

This paper addresses the problem of stability analysis and state feedback fuzzy control design for multi-line re-entrant manufacturing systems (RMSs) with impulse-type state jumps. Under the impulse effect, the RMS is modeled as a hyperbolic impulsive partial differential equation (IPDE) that possesses hybrid characteristics. Then, a matrix inequality condition depending on the discrete dynamic information is presented to deal with the jump behavior of the IPDE at impulse instants. Moreover, a robust fuzzy controller based on the parallel distributed compensation scheme is derived to render the RMS to a desired producing mode with steady feeding and output rates. Finally, an illustrative example is provided to demonstrate the feasibility and effectiveness of the proposed robust fuzzy controller.

An Enhanced Fractional-order Fuzzy Controller Design for an Integrated Power System using a Counteractive Control Action

An Enhanced Fractional-order Fuzzy Controller Design for an Integrated Power System using a Counteractive Control Action

Computer intelligent device adjustment and fuzzy controller design for embedded ARM

Computer intelligent device adjustment and fuzzy controller design for embedded ARM

A Fuzzy Neural Network Controller Using Compromise Features for Timeliness Problem

When the control rules of traditional fuzzy controller are determined, it comes to be time-consuming and laborious to adjust for different usage conditions. Therefore, the timeliness cannot be guaranteed to solve the timeliness problem, and a fuzzy controller with modifiable factors is designed. While the entire control table is affected by the modifiable factors selection table, all previous control parameters need to be reset. In light of above problems, this paper firstly proposes new fuzzy controller design methods, which retain strengths of traditional controller and controller with modifiable factors. It effectively overcomes the shortcomings of the two controllers mentioned above, and only need to adjust the compromise factor for different working conditions of proposed controller, which is more convenient and efficient. Secondly, the proposed fuzzy controller also adopts a four-layer neural network to optimize the control rules of compromise to improve control precision and system robustness. Finally, the excellent characteristics of proposed controller are verified through simulation research, and the simulation result proves the proposed fuzzy controller has the advantages of higher control precision and smaller transition.

New fuzzy control design for photovoltaic system using LMI technique

The T-S Fuzzy model-based technique (T-S: Takagi-Sugeno) is used in this research to offer a new fuzzy state feedback control for solar systems with output charge. We discuss a DC-DC boost converter to manage the output power of a solar panel array under shifting meteorological circumstances. To reach optimal power point, a T-S reference model is employed to create a fuzzy state feedback control that must be tracked. Solving a series of linear matrix inequalities (LMIs) yields controller gains. Furthermore, the proposed solution reduces the oscillation disadvantage near the highest power point while simultaneously shortening the tracking time. Finally, simulation of the solar system employing the proposed state feedback control shows that the proposed approach is effective even in the face of climate change.

Flight Controller Design for Altitude Control of a Quadcopter using PID and Fuzzy Methods

A quadcopter can be manually controlled or can be autonomous. Therefore, a control system is needed to support the quadcopter's movement. PID and fuzzy control are used in this research to adjust the quadcopter's altitude position. The control system proposed in this research is to keep the quadcopter at a certain altitude. The control system design uses a PID controller, where the control parameters are obtained using the Ziegler-Nichols 2 tuning method. Ziegler-Nichols 2 tuning method is used to obtain the parameters Kp, Ti, and Td. PID control tuning becomes a reference for fuzzy control design in determining input and output membership functions and the rules. Also, in this study, the flight controller board design is proposed. The experiment results show that the PID control has a faster rise time of 0.2 seconds, a better maximum overshoot of 1.56%, a quicker settling time of 1.69 seconds, and a steady-state error of 0% compared to the fuzzy control. The result shows that the use of PID control is more suitable for proposed quadcopter plants because it requires a fast output response.

Metaheuristic Optimization Algorithm for Optimal Design of Type-2 Fuzzy Controller

The utilization of Le`vy flight to create new candidate solutions is one of the most powerful elements of CS. Candidate solutions are modified using this method by making a lot of minor modifications and a few big jumps. As a result, CS will be able to significantly increase the link between exploration and exploitation while also improving its search capabilities. The cuckoo search optimization (CSO) algorithm is applied to interval type-2 fuzzy logic controller (IT2FLC) in this research to determine the optimal parameters of membership functions (MFs) of interval type-2 fuzzy logic systems (IT2FLSs). The study takes into account two forms of MFs: triangular and trapezoidal. When perturbations are applied during the execution of each control issue, the CSO algorithm's performance and efficiency improve significantly. The proposed approach is tested using two benchmark control problems: water tank controller and inverted pendulum controller.

Intelligent power control of wind conversion system based on Takagi–Sugeno fuzzy model

SummaryThis paper comes up with a novel fuzzy control design for a wind conversion system (WCS). To do this, a boost converter is employed to adjust the rectified voltage under different wind speed levels. Initially, the boost converter model is well presented via the Takagi–Sugeno approach. Then, a T‐S fuzzy controller is designed to promptly calculate the duty cycle required for an optimal power operation. Afterward, the optimal DC current is determined and then transferred to the reference model to instantly determine the desired trajectories. These latter should be preserved so as to reach the maximum power. Following that, linear matrix inequalities (LMIs) are also used to guarantee the stability analyses and the performance. Finally, the sound performance and the validity of the suggested control schema are both proved by a numerical and some experimental results.

An AEFA-Based Optimum Design of Fuzzy PID Controller for Attitude Control Flywheel with BLDC Motor

A new method for optimizing the fuzzy PID controller, based on an artificial electric field algorithm (AEFA), is proposed in this paper, aiming at improving the stability indicator of the Brushless DC (BLDC) motor for the small satellite attitude control flywheel. The BLDC motor is the basic part of the small satellite attitude control flywheel. In order to accurately control the attitude of the small satellite, a good motor control system is very important. Firstly, the mathematical model of the BLDC motor is established and the BLDC motor speed control system using traditional PID control is designed. Secondly, considering that the small satellite speed control system is a nonlinear system, a fuzzy PID control is designed to solve the shortcomings of the fixed parameters of the traditional PID control. Finally, we find that the control accuracy of the fuzzy PID control will change with the range of the input. Therefore, we introduce the AEFA to optimize fuzzy PID to achieve high-precision attitude control of small satellites. By simulating the BLDC motor system, the proposed fuzzy PID controller based on AEFA is compared with the traditional PID controller and the fuzzy PID controller. Results from different controllers show that the proposed control method could effectively reduce steady state error. In addition, the proposed fuzzy PID–AEFA controller has the better anti-jamming capability.

High clearance four-wheel independent electric drive sprayer path tracking control based on self-correction controller

Most traditional agricultural machines use traditional mechanical transmission with fuel drive and front wheel steering, and most of them are manned, with low transmission efficiency, poor flexibility and low level of intelligence. This paper proposes a new high ground clearance sprayer with four-wheel independent drive (4WID) with dual front and rear steering axles. This sprayer is an oil-electric hybrid that not only saves energy, but also reduces the pollution to crops. In this study, a path tracking controller and a self-correcting controller are constructed based on the kinematic model of the sprayer, respectively. The model prediction controller is used as the main controller, and it outputs the desired steering angle and speed of the sprayer based on the current state of the sprayer and the desired path to achieve path tracking control. The self-correcting controller, as an auxiliary controller, adopts fuzzy control method and designs fuzzy control rules based on the driver’s experience to correct the wheel turning angle at the current moment, which not only retains the advantages of model prediction control in linear path tracking, but also improves the control effect in curved path tracking and enhances the path tracking accuracy in large curvature roads. The simulation shows that the maximum lateral deviation of 0.03 m can be achieved by the model predictive control alone, and the maximum lateral deviation is reduced to 0.0141 m after adding the self-correcting controller.

Design of Fin Stabilizer Controller Based on Fuzzy Control Principle

As the ship is sailing in the sea, the PID control parameters set may not be the optimal parameters under different sea conditions according to the changes of sea conditions and wind, waves and currents, so the PID control of fin stabilizer cannot achieve the ideal control effect in the actual ship operation process. This paper studies the design of fuzzy controller with better robustness than traditional PID control, and explores the actual control performance of fuzzy controller under different sea conditions.

Fuzzy Control of Nonlinear Strict-Feedback Systems With Full-State Constraints: A New Barrier Function Approach

This article is concerned with adaptive fuzzy control design of nonlinear strict-feedback systems, in which system functions are unknown and system states are subjected to some constant constraints. The main control issue is to design an adaptive fuzzy controller such that the system output follows the reference signal, meanwhile, all the state variables abide by their constrained requirements. Differing from the existing way to build barrier function, the constructed virtual control signal is employed to build the barrier function. Furthermore, a backstepping-based adaptive fuzzy control design process is presented in this article. Compared to the control schemes in the existing literature on full-state constrained systems, the current control scheme has the following advantages: 1) the built virtual control signals in this article are ensured to satisfy corresponding state constraints, rather than assuming that they do, as in the existing articles; 2) the choice of initial values is independent of the norm of the virtual control signal. So, the conservatism of initial value selection is considerably reduced. It is shown that the proposed adaptive fuzzy controller not only ensures perfect tracking performance, but also ensures that all the states abide by the preassigned state constraints during the operation. At last, simulation study further verifies the efficacy of the proposed control strategy.

Optimal fuzzy controller design for industrial gear system by gray wolf algorithm

Optimal fuzzy controller design for industrial gear system by gray wolf algorithm

Adaptive fuzzy asymptotic control design for MIMO nonlinear systems with state constraints

This paper addresses the asymptotic control problem of uncertain multi-input and multi-output (MIMO) nonlinear systems. The considered MIMO systems contain unknown virtual control coefficients (UVCCs) and state constraints. A creative Lyapunov function by associating with the lower bounds of UVCCs is presented to counteract the adverse effect deriving from UVCCs. The state constraints are ensured by utilising the barrier Lyapunov function. Moreover, the asymptotic tracking controller is recursively constructed by combining the backstepping technique with fuzzy logic systems. The remarkable character of the designed controller is that the asymptotic tracking performance can be achieved by introducing some smooth functions into adaptive backstepping procedure. In contrast to the existing results, the conditions on the UVCCs are relaxed. Finally, the new control design is illustrated by a practical example.

Fuzzy subspace-based constrained predictive control design for a greenhouse micro-climate

Fuzzy subspace-based constrained predictive control design for a greenhouse micro-climate

Disturbance observer based adaptive fuzzy synchronization controller design for uncertain fractional-order chaotic systems

This study premeditated the synchronization of two fractional-order chaotic systems (FOCSs) with uncertainties and external disturbances. We utilized fuzzy logic systems (FLSs) to estimate unknown nonlinearities, and implemented disturbance observers to estimate unknown bounded external disturbances. Then, a robust control term was devised to compensate for the unavoidable approximation error of the fuzzy system. In addition, a sliding mode surface was devised to construct an adaptive fuzzy sliding mode controller (AFSMC) that can guarantee that the synchronization error converges to a small neighborhood of zero. Finally, the validity of the proposed control strategy was verified via a numerical simulation.

Fuzzy Controller Design for Discrete-time T-S Fuzzy Systems with Partially Unknown Membership Functions

Fuzzy Controller Design for Discrete-time T-S Fuzzy Systems with Partially Unknown Membership Functions

Fuzzy Q-Learning interaction controller design for collaborative robot

Background: In physical human-robot interaction (pHRI), admittance control is widely used. The most critical thing in admittance control is the configuration of admittance parameters, but a constant admittance value can not meet the needs of interactive indicators smoothness especially. Variable admittance control is a method to overcome this limitation by adjusting the admittance value in real time. This paper proposes a fuzzy Q-learning (FQL) variable admittance control system, which integrates the fuzzy system (FIS) and reinforcement learning method Q-learning. Methods: FIS is used to turn a continuous input state into fuzzy set and Q-learning is used to train the premise strength of fuzzy rules to get the optimal policy of variable admittance value. To verify the performance of this method, an experiment was performed using an AUBO i5 robot. Training trajectory is point-to-point (PTP) trajectory, several interaction variables before and after training by the algorithm are compared to show the validity of algorithm. Results: Experimental results show that the reward converges to a smaller value in about 25 episodes, and the reward of the last five episodes reduces by 68%. The motion trajectory after algorithm training is closer to the ideal min-jerk trajectory and the deviation and mean value of interaction force become smaller. Conclusions: The proposed FQL method can converge in a few episodes and can improve the performance of pHRI by minimizing the jerk based cost function.

Subpredictor-Based Fuzzy Control Design for a Reaction–Diffusion Equation

This article addresses the stabilization of the nonlinear reaction–diffusion equation with an unknown but bounded delay. Based on Takagi–Sugeno (T–S) fuzzy rules, a T–S fuzzy partial differential equation (PDE) model is constructed to describe the nonlinear distributed parameter system. More importantly, we contribute to the large delay compensation and the future state prediction by introducing a chain of subpredictors for the T–S fuzzy PDE model, and the convergence of observation errors is guaranteed as well. Different from the conventional approaches, the proposed observer can compensate a larger delay. To stabilize the system, for the first time, a new subpredictor-based T–S fuzzy control law is suggested by using the proposed observer, which is composed of elementary observers connected in series. The implementation of the subpredictor feedback is simpler than the classical methods that only one of the elementary observers is employed. Via Lyapunov–Krasovskii approach, sufficient exponential stability conditions are established in the framework of linear matrix inequalities. A numerical example is provided to illustrate the effectiveness of theoretical results.

Shadowed Type-2 Fuzzy Sets in Dynamic Parameter Adaption in Cuckoo Search and Flower Pollination Algorithms for Optimal Design of Fuzzy Fault-Tolerant Controllers

In recent, various metaheuristic algorithms have shown significant results in control engineering problems; moreover, fuzzy sets (FSs) and theories were frequently used for dynamic parameter adaption in metaheuristic algorithms. The primary reason for this is that fuzzy inference system (FISs) can be designed using human knowledge, allowing for intelligent dynamic adaptations of metaheuristic parameters. To accomplish these tasks, we proposed shadowed type-2 fuzzy inference systems (ST2FISs) for two metaheuristic algorithms, namely cuckoo search (CS) and flower pollination (FP). Furthermore, with the advent of shadowed type-2 fuzzy logic, the abilities of uncertainty handling offer an appealing improved performance for dynamic parameter adaptation in metaheuristic methods; moreover, the use of ST2FISs has been shown in recent works to provide better results than type-1 fuzzy inference systems (T1FISs). As a result, ST2FISs are proposed for adjusting the Lèvy flight (P) and switching probability (P′) parameters in the original cuckoo search (CS) and flower pollination (FP) algorithms, respectively. Our approach investigated trapezoidal types of membership functions (MFs), such as ST2FSs. The proposed method was used to optimize the precursors and implications of a two-tank non-interacting conical frustum tank level (TTNCFTL) process using an interval type-2 fuzzy controller (IT2FLC). To ensure that the implementation is efficient compared with the original CS and FP algorithms, simulation results were obtained without and then with uncertainty in the main actuator (CV1) and system component (leak) at the bottom of frustum tank two of the TTNCFLT process. In addition, the statistical z-test and non-parametric Friedman test are performed to analyze and deliver the findings for the best metaheuristic algorithm. The reported findings highlight the benefits of employing this approach over traditional general type-2 fuzzy inference systems since we get superior performance in the majority of cases while using minimal computational resources.