一种应用于自主车辆的新型自适应模糊控制器

Adaptive control for a class of nonlinear systems is discussed in this paper,We use fuzzy systems to approximate the ideal optimal corntroller by adjusting the parameters of fuzzy systems,In order to ture these parameters,linear relationship between approximation error and parameters is established first.Then we desing the adaptive laws of these parameters based on Lyapunov synthesis approach.The advantage of our method is that we can tune not only the parameters of the consequences of fuzzy rules,but also the parameters of the membership functions.As a result,a stable and more flexible controller is achieved.The performance of the adaptive scheme is demonstrated through the longitudinal vehicle control.     

Covariance control with observed-state feedback gains for continuous nonlinear systems using T-S fuzzy models

The design problem of state variance constrained control for stochastic systems has received rather extensive attention in recent years. This paper solves the state variance constrained controller design problem by using the covariance control theory, with observed-state feedback gains for continuous Takagi-Sugeno (TS) fuzzy models. By incorporating the technique of state estimation into the practical covariance control theory, a variance constrained control methodology is developed for the continuous TS fuzzy models. Finally, a numerical example is shown to demonstrate the efficiency and applicability of the proposed approach.     

A Novel Adaptive Fuzzy Controller for Application in Autonomous Vehicles

１INTRODUCTIONMostofthecurrentresearchonadaptivefuzzycontrolonlytunestheparametersoftheconsequencesoffuzzyrules．Thismaycausetheapproximationpropertyoffuzzysystemsnottobegood熏andaffecttheperformanceofthecon鄄troller．Aimingatthisproblem熏wehopetotuneallparam鄄etersoffuzzyrules．Inordertotunetheseparameters熏lin鄄earrelationshipbetweenapproximationerrorandallparam鄄etersoffuzzyrulesisestablishedfirst．ThenwedesigntheadaptivelawsoftheseparametersbasedonLyapunovsyn鄄thesisapproach．Theadva…     

On position/force tracking control problem of cooperative robot manipulators using adaptive fuzzy backstepping approach

In this paper, the position and force tracking control problem of cooperative robot manipulator system handling a common rigid object with unknown dynamical models and unknown external disturbances is investigated. The universal approximation properties of fuzzy logic systems are employed to estimate the unknown system dynamics. On the other hand, by defining new state variables based on the integral and differential of position and orientation errors of the grasped object, the error system of coordinated robot manipulators is constructed. Subsequently by defining the appropriate change of coordinates and using the backstepping design strategy, an adaptive fuzzy backstepping position tracking control scheme is proposed for multi-robot manipulator systems. By utilizing the properties of internal forces, extra terms are also added to the control signals to consider the force tracking problem. Moreover, it is shown that the proposed adaptive fuzzy backstepping position/force control approach ensures all the signals of the closed loop system uniformly ultimately bounded and tracking errors of both positions and forces can converge to small desired values by proper selection of the design parameters. Finally, the theoretic achievements are tested on the two three-link planar robot manipulators cooperatively handling a common object to illustrate the effectiveness of the proposed approach.     

Robust covariance control for discrete system by Takagi-Sugeno fuzzy controllers

Variances of the system states or outputs often play vital roles in the problem for performance requirements of many stochastic control systems. For linear stochastic systems, the covariance control technique has been applied to deal with the variance constrained design problem. This paper extends this technique to a class of discrete-time nonlinear perturbed stochastic systems, which are modeled by the Takagi-Sugeno (TS) fuzzy systems. By fuzzy IF-THEN rules, which represent local linear input-output relations, the nonlinear systems can be described by TS fuzzy models. According to the parallel distributed compensation (PDC) concept, the discrete-time nonlinear perturbed stochastic systems can be driven by the linear feedback gains. The purpose of this paper is to provide a method to design an output feedback fuzzy controller, which is based on the upper bound state covariance control technique and PDC concept, for the discrete-time perturbed stochastic systems using TS fuzzy models.     

Novel delay-partitioning stabilization approach for networked control system via Wirtinger-based inequalities

This paper studies the problems of stability analysis and state feedback stabilization for networked control system. By developing a novel delay-partitioning approach, the information on both the range of network-induced delay and the maximum number of consecutive data packet dropouts can be taken into full consideration. Various augmented Lyapunov–Krasovskii functionals (LKFs) with triple-integral terms are constructed for the two delay subintervals. Moreover, the Wirtinger-based inequalities in combination with an improved reciprocal convexity are utilized to estimate the derivatives of LKFs more accurately. The proposed approaches have improved the stability conditions without increasing much computational complexity. Based on the obtained stability criterion, a stabilization controller design approach is also given. Finally, four numerical examples are presented to illustrate the effectiveness and outperformance of the proposed approaches.     

Fault tolerant control for modified quadrotor via adaptive type-2 fuzzy backstepping subject to actuator faults

In this paper, a robust attitude and position control of a novel modified quadrotor unmanned aerial vehicles (UAV) which has higher drive capability as well as greater robustness against actuator faults than conventional quad-rotor UAV has been developed. A robust backstepping controller with adaptive interval type-2 fuzzy logic is proposed to control the attitude and position of the modified quadrotor under actuator faults. Besides globally stabilizing the system amid other disturbances, the insensitivity to the model errors and parametric uncertainties are the asset of the backstepping approach. The adaptive interval type-2 fuzzy logic as fault observer can effectively estimate the lumped faults without the knowledge of their bounds for the modified quadrotor UAV. Additionally, the type-2 fuzzy systems are utilized to approximate the local nonlinearities of each subsystem under actuator faults, next and in order to achieve the expected tracking performance, we used Lyapunov theory stability and convergence analysis to online adjust adaptive laws. As a result, the uniformly ultimate stability of the modified quadrotor system is proved. Finally, the performances of the proposed control method are evaluated by simulation and the results demonstrate the effectiveness of the proposed control strategy for the modified quadrotor in vertical flights in presence of actuator faults.     

Actuator fault tolerant control using adaptive RBFNN fuzzy sliding mode controller for coaxial octorotor UAV

In this paper, a robust controller for a Six Degrees of Freedom (6 DOF) coaxial octorotor helicopter control is proposed in presence of actuator faults. Radial Base Function Neural Network (RBFNN), Fuzzy Logic Control approach (FLC) and Sliding Mode Control (SMC) technique are used to design a controller, named Fault Tolerant Control (FTC), for each subsystem of the octorotor helicopter. The proposed FTC scheme allows avoiding difficult modeling, attenuating the chattering effect of the SMC, reducing the rules number of the fuzzy controller, and guaranteeing the stability and the robustness of the system. The simulation results show that the proposed FTC can greatly alleviate the chattering effect, good tracking in presence of actuator faults.