Optimal Fuzzy Controller Research Articles

Observer-Based Optimal Fuzzy Control for Networked IT-2 Fuzzy Systems Against Hybrid Cyber-Attacks

Observer-Based Optimal Fuzzy Control for Networked IT-2 Fuzzy Systems Against Hybrid Cyber-Attacks

Fuzzy Control Optimization of Loading Paths for Hydroforming of Variable Diameter Tubes

Fuzzy Control Optimization of Loading Paths for Hydroforming of Variable Diameter Tubes

Prescribed performance event-triggered fuzzy optimal tracking control for strict-feedback nonlinear systems

The prescribed performance event-triggered optimal tracking control problem is considered for a class of uncertain strict-feedback nonlinear systems with external disturbances. First, the disturbance observers are constructed to estimate external disturbances. Then, the controller contains an adaptive fuzzy controller and an optimal compensation term, in which the unknown nonlinearities and cost function are approximated by the fuzzy logic systems. An adaptive fuzzy controller is established by the dynamic surface control (DSC) technique, which is addressed to handle “computation complexity” issue occurred in conventional backstepping approach. Based on adaptive dynamic programming (ADP) method, an optimal compensation term is developed by minimizing the cost function. Furthermore, communication load is reduced via embedding event-triggered mechanism. In addition, the tracking error can be restricted in the prescribed region with the aid of the prescribed performance control. Thus, the whole control scheme can not only ensure that the tracking error converges to a boundary but also achieve optimization, reduce the communication burden and avoid “computation complexity” issue. The boundedness of all signals in the closed-loop is proved. Finally, the simulation examples illustrate the validity of the designed control scheme.

Particle swarm optimized fuzzy proportional-integral-derivative controller-based transverse leaf spring active suspension for vibration control

The transverse leaf spring (TLS) suspensions are a promising option for van vehicles due to their high load-carrying capacity and excellent handling stability. However, its ride comfort remains a major challenge. This paper investigates and compares the effects of semi-active and active control strategies to enhance the ride comfort of TLS suspensions. Firstly, a four-degree-of-freedom (4-DOF) half-car model and a multi-body dynamics (MBD) model of the TLS suspensions are established. The MBD model has higher accuracy and can describe the medium and high frequency characteristics of the TLS suspensions, such as the suspension offset frequency and the frequency response function of the body vertical acceleration (BVA). Therefore, based on the MBD half-car model with TLS suspensions, this paper proposes an optimal fuzzy PID active control strategy considering the left and right suspension coupling. The optimization objectives are the BVA, the left and right suspensions dynamic deflection, and the left and right wheels dynamic displacement. The integral absolute error is used as the evaluation criterion. The left and right fuzzy PID controllers’ parameters are obtained through particle swarm optimization. Simulation results demonstrate that the particle swarm optimization fuzzy PID active control strategy effectively controls the low-frequency vibration of the TLS suspensions and suppresses the medium- and high-frequency vibration characteristics compared with the traditional skyhook semi-active control strategy. This technology provides a reference for improving the ride comfort of the TLS suspensions.

Optimal Fuzzy-FOPID, Fuzzy-PID Control Schemes for Trajectory Tracking of 3DOF Robot Manipulator

The present study explores the guidance of a robotic arm along a predefined path by implementing an optimal fuzzy fractional order PID controller-based control strategy. This method serves as a means to address the nonlinearity and unpredictability of the robotic manipulator, contingent upon the fuzzy logic controller's specifications and the employment of a clonal selection algorithm. The dynamic equation of the manipulator was considered as an initial point, followed by designing a fuzzy controller for this purpose. To validate the effectiveness of this approach, it was compared to other techniques, such as Fuzzy, Fuzzy-PID, and fuzzy-FOPID controllers, with PID and FOPID controller parameters optimized using clonal selection algorithms. Simulation results reveal that the fuzzy-FOPID variant outperformed other methods under varying load conditions and model uncertainties, using SIMULINK/MATLAB 2014a.

Optimized fuzzy fractional-order linear quadratic tracking control for a nonlinear system

In this paper, a novel fuzzy fractional-order Linear Quadratic Tracking (LQT) controller optimized by a Multi-Objective gray Wolf Algorithm (MOGWA) is designed for a 6 Degree-Of-Freedom (DOF) quadcopter flying system. In order to reach this end, the state-space equations would be divided into two parts, one for attitude and altitude control and another for position control. The LQT method is implemented to determine the constant parameters of the designed controller by minimizing a quadratic form related to errors and control efforts. Then, to substantially improve the performance of the linear quadratic tracking scheme, the fractional-order derivatives of the system errors are considered as the control inputs, while the MOGWA is used for optimal determination of the related orders. A fuzzy system that employs triangular and trapezoidal membership functions as well as the center average defuzzifier and singleton fuzzifier, is designed to timely regulate the gains of the controller. The introduced strategy would be applied on the nonlinear quadcopter flying system, and the related diagrams would be revealed to illustrate the efficiency and ability of the introduced fuzzy optimal fractional-order linear quadratic tracking controller.

Refinements the solutions of fuzzy optimal control problems

Refinements the solutions of fuzzy optimal control problems

Optimal Fuzzy Output Feedback Control for Dynamic Positioning of Vessels With Finite-Time Disturbance Rejection Under Thruster Saturations

This paper focuses on the problem of optimal fuzzy output-feedback tracking control for dynamic positioning of marine vessels simultaneously with model uncertainties, unknown disturbances, unavailable velocities and thruster saturations. The control scheme is built by integrating a fuzzy velocity observer, a finite-time disturbance observer, a dynamic auxiliary system, the optimal control strategy with the dynamic surface control. The fuzzy velocity observer is constructed to obtain the unavailable velocities without requiring the certain model dynamics. The finite-time disturbance observer is established to estimate the unknown disturbances. The dynamic auxiliary system is designed to compensate for thruster saturations effects. By incorporating these methods, a disturbance rejection adaptive controller is designed with dynamic surface control. The optimal compensation term is inserted to minimize the cost function of the tracking error system by adaptive dynamic programming. It is shown that the optimal control consisting of the adaptive controller and the optimal compensation term guarantees that all signals in the closed-loop dynamic positioning system are bounded. Finally, simulation results demonstrate the effectiveness of the proposed optimal control scheme.

Event-Triggered Adaptive Fuzzy Optimal Control for a Class of Strict-Feedback Nonlinear Systems With External Disturbances

In this paper, the problem of the event-triggered optimal control for a class of strict-feedback nonlinear systems with external disturbances is investigated. Firstly, by introducing proper low-pass filters to the steps of backstepping design, the problem of “jump of the error surfaces” is avoided. What's more, it facilitates the design of a fuzzy hybrid-mode-triggered feedforward controller, which aims to transform the original controlled nonlinear strict-feedback system into an equivalent nonlinear system in affine form. Secondly, by utilizing the adaptive dynamic programming theory, an event-triggered feedback controller is developed for the equivalent affine nonlinear system. Differing from the existing methods, the real control input consists of two event-triggered terms, which can be updated synchronously under the proposed composite error-based triggering condition. Moreover, it is proven that all the closed-loop signals are bounded. At last, by taking a second-order nonlinear system and missile integrated guidance and control system as examples, simulation results demonstrate the effectiveness of the proposed approach.

Design of self-organized membership functions for an optimal fuzzy controller in enhancing frequency regulation

Design of self-organized membership functions for an optimal fuzzy controller in enhancing frequency regulation

Chimp optimization‐based fuzzy controller for hybrid electric vehicle speed control using electronic throttle plate

AbstractToday's concern about climate change and the country's economic growth have made adopting hybrid electric vehicles (HEV) a worthy solution. Since the gas emissions of HEVs are closely related to their motor performance, developing an effective HEV motor control technique is essential. The performance and acceleration of an HEV motor are determined by the internal combustion engine (ICE) and the electronic throttle plate (ETP), which employ an air‐fuel mixture for vehicle propulsion. In order to deal with this problem, this article presented an optimal fuzzy controller that controls the throttle plate's angular position and motor speed in HEVs. However, the efficiency and reliability of fuzzy controllers depend upon their gains factor. So, a different heuristic algorithm is employed to self‐tuning the fuzzy logic controller (FLC) gain factor. The performance of the suggested ChOA‐fuzzy‐based controller was evaluated utilizing various control error indices and time‐domain stability. A comprehensive controller performance analysis using different metaheuristic techniques has been carried out to validate the proposed scheme. The findings show that the suggested ChOA‐fuzzy‐based controller performs better than other techniques.

Fuzzy optimal tracking control for nonlinear underactuated unmanned surface vehicles

In this paper, a reinforcement Q-learning fuzzy optimal tracking control method is investigated for nonlinear underactuated unmanned surface vehicles (USVs) with external disturbances. Firstly, the motion dynamics of USVs are described by the Takagi–Sugeno (T–S) fuzzy discrete-time systems. Secondly, by applying parallel distributed compensation (PDC) method and constructing cost function, the existence condition of optimal solutions is derived and reduced to the algebraic Riccati equations (AREs). To solve the solutions of the GAREs, a Q-learning value iteration (VI) algorithm is further developed, which can be implemented by eliminating the demand of initial allowable control policy and system information. The developed strategy has the advantage that the optimal control policy can ensure the USV to be stable and the desired reference signal can also be well tracked by the USV output. Finally, the proposed fuzzy optimal control algorithm is applied to control the USV, the simulation results verify the feasibility of the proposed optimal control algorithm.

Adaptive Fuzzy Inverse Optimal Control of Nonlinear Switched Systems

Existing approaches to optimal control for uncertain switched systems require heavy computations for approximating and learning the optimal solution of the Hamilton—Jacobi—Bellman equation. To overcome this problem, a fuzzy adaptive inverse optimal control strategy is first developed for switched systems, which minimizes the cost functional but circumvents solving the Hamilton—Jacobi—Bellman equation. Specifically, an alternative practical inverse approach is developed by two Lyapunov functions method, based on which the inverse optimality regarding a meaningful cost function is achieved. In addition, a new condition of admissible edge-dependent average dwell time is developed by applying the extended multiple Lyapunov functions method. Guided by this weaker condition, the stability of the considered switched system is proved. Finally, simulations are carried out to verify the developed method.

Semi-automatic of steer by wire system using fuzzy logic control and swarm optimization

<span lang="EN-US">The semi-automatic steer by wire system is a vehicle steering system that does not use a mechanical link but uses an electric drive, so that the steer wheel is equipped with an angle sensor as a control system input to adjust the electric drive rotation when determining the direction of the front wheels of the vehicle. The control system uses fuzzy logic control (FLC) is optimized using swarm-based optimization. Performance testing of the optimal control system is carried out using Software in the loop simulations and hardware in the loop simulations which were then applied to the steering system prototype equipped with rack-pinion and wheel steer. The test results show that the optimal control system performance reaches a C-RMS error of 6.712. The prototype that was built has two kinds of ratios between the steering angle and the vehicle wheel angle, namely a ratio of 1:6.25 for speeds below 10 km/h making it suitable for operation when the vehicle is maneuvering in the parking area and a ratio of 1:25 for speeds above 10 km/h which is suitable for operation when the vehicle is traveling at medium or high speed.</span>

Multiobjective Reliability-Based Design Optimization of the Fuzzy Logic Controller for MR Damper-Based Structures

To devise an optimum and robust fuzzy logic controller for MR damper-based structures subjected to earthquake ground motions, the multiobjective reliability-based design optimization (RBDO) using the adaptive Kriging model is performed to determine the parameters of the fuzzy logic controller. The optimization problem is formulated with two objective functions, namely, the minimization of interstory drift and average control force of the concerned structure, and subjected to a probability constraint on structural dynamic responses under the effects of random structural stiffness and stochastic earthquake loadings. To reduce the computational cost of reliability assessment, a global Kriging model is constructed in an augmented space as a surrogate for computational evaluations. Subsequently, the trained metamodel combined with the nondominated sorting genetic algorithm (NSGA-II) is integrated into the framework of RBDO for solving the fuzzy logic control (FLC) optimization problem. The feasibility and effectiveness of the multiobjective RBDO in the FLC design are finally validated by conducting numerical simulations on both linear and nonlinear structures. As demonstrated in the linear case, the fuzzy logic controllers obtained from the multiobjective RBDO show more robustness than those derived from the multiobjective deterministic design optimization (DDO). In the nonlinear case, using the multiobjective DDO to prelocate a coarse safety domain can significantly reduce the number of samples for training the metamodel and facilitate the implementation of the multiobjective RBDO; in addition, the controlled structural performance with a specified fuzzy logic controller can be further improved by considering MR damper distribution optimization.

Fuzzy adaptive optimal backstepping control of the FO MEMS resonator under imprecise target trajectory with disturbance compensation mechanism

Fuzzy adaptive optimal backstepping control of the FO MEMS resonator under imprecise target trajectory with disturbance compensation mechanism

Infinite horizon fuzzy optimal control: optimality does not imply asymptotic stability

In this paper, novel stability result for discrete-time infinite horizon optimal control using fuzzy objective functions is presented. For this class of control, the fuzzy goals and the fuzzy constraints introduced in the fuzzy objective function handle the constraints placed on both the state and the control vectors. We analyze the asymptotic stability of the equilibrium for the infinite horizon fuzzy optimal control law using the minimum aggregation operator. We show that the infinite horizon control with the minimum aggregation operator does not guarantee the asymptotic stability of the equilibrium in general. This is done by deriving an analytical solution of the control law for a simple linear system using a fuzzy dynamic programming approach. An example that shows the novel asymptotic stability result of the equilibrium for discrete-time infinite horizon optimal control with fuzzy objective function problem is given.

Artificial pancreas control using optimized fuzzy logic based genetic algorithm

The aim of this paper is to develop an artificial pancreas that can automate the process of monitoring blood glucose levels and administering insulin to diabetic patients. The device incorporates a fuzzy controller that is optimized through a genetic algorithm and designed using MATLAB. The system comprises three key components: a continuous glucose monitoring (CGM) system, an insulin pump, and the fuzzy controller. The CGM measures blood glucose levels in real-time, and the insulin pump administers insulin doses to maintain blood glucose levels within a specific range. The fuzzy controller adjusts the insulin delivery rate based on the patient's blood glucose levels and their target range. To enhance the system's performance, a genetic algorithm is used to fine-tune the parameters of the fuzzy controller, seeking the optimal set of parameters that minimize the difference between the patient's blood glucose levels and the desired target range. The system is implemented in MATLAB, and simulation results indicate its effectiveness in maintaining blood glucose levels within the desired target range, reducing the risk of hypoglycemia and hyperglycemia. In summary, the proposed artificial pancreas system provides an effective automated solution for monitoring blood glucose levels and administering insulin to diabetic patients, with the fuzzy controller and genetic algorithm optimization enhancing the system's performance.

Fuzzy approximation-based optimal consensus control for nonlinear multiagent systems via adaptive dynamic programming

This paper investigates the fuzzy approximation-based optimal consensus control problem for nonlinear multiagent systems with unknown perturbations. By constructing local error dynamics, the considered optimal consensus problem is reformulated as finding Nash-equilibrium solutions to zero-sum games. Then, by using sliding mode control technology and the concept of hierarchical design, a series of control signals are sequentially designed to regulate the consensus error and minimize the local value function. In addition, an identifier-critic architecture is developed by using generalized fuzzy hyperbolic models, where the identifier is employed to relax the requirement for complete system dynamics information, and the hierarchical sliding mode surface-based critic network is applied to approximate optimal control inputs. Finally, A simulation example is presented to illustrate the validity of the proposed approach.

Fuzzy logic based adaptive vibration control system for structures subjected to seismic and wind loads

In this study, an attempt has been made to develop a Fuzzy Logic Multi Verse Optimal Control (FLMVOC) system as a new adaptive real-time vibration control mechanism for structures subjected to seismic excitation and wind load by utilizing the capability of the stochastic optimization method and fuzzy logic technique.The magnetorheological damper (MR) is deployed as a controllable vibration damping system in this study due to its excellent damping performance and low energy consumption. Therefore, the analytical model for the MR damper is formulated and integrated with the developed fuzzy logic optimal control (FLOC) algorithm. The story drift and absolute acceleration have been defined as the inputs of the fuzzy logic controller (FLC), while the MR commanding voltage is considered as the controller’s output. Then, the membership functions and fuzzy rule base have been formulated. To derive the optimal controller, the FLC with full parameters has been trained with multi objective multi verse algorithm (MOMVO). For this purpose, the MATLAB program and its Simulinks have been integrated and hybridised with finite element package to simulate and evaluate structure response for various input parameters.The developed FLMVOC system has been implemented in three story shear building subjected to seismic load and 60 story wind induced high rise building in order to evaluate its efficiency in diminishing the dynamic response of the structure.The result revealed that FLMVOC system successfully reduced structural drifts by 60%, 53%, and 41% under the effect of El Centro, Kobe, and Northridge earthquakes, respectively, while the floor absolute acceleration was reduced by 38%, 17%, and 10%, respectively. For the wind induced structure, the proposed system showed the ability to maintain the floor acceleration within people’s comfort criterion in addition to the reduction in story drift.