Adaptive neural output feedback finite-time command filtered backstepping control for nonlinear systems with full-state constraints

In this paper, an adaptive neural finite-time control method via barrier Lyapunov function, command filtered backstepping, and output feedback is proposed to solve the tracking problem of uncertain high-order nonlinear systems with full-state constraints and input saturation. By utilizing the neural network (NN) to approximate unknown nonlinear functions, the finite-time command filters are used to filtering the virtual control signals and get the intermediate control signals in a finite time in the backstepping process. Because there are errors between the output of finite-time command filters and the virtual control signals, the error compensation signals are added to eliminate the influence of filtering errors. Based on the proposed control scheme, the states of the system can be constrained in the predetermined region, all signals in the system are bounded in finite time, and the tracking error can converge to the desired region in finite time. At last, a simulation example is given to show the effectiveness of the proposed control method.     

Neural network-based robust finite-time control for robotic manipulators considering actuator dynamics

A novel neural network-based robust finite-time control strategy is proposed for the trajectory tracking of robotic manipulators with structured and unstructured uncertainties, in which the actuator dynamics is fully considered. The controller, which possesses finite-time convergence and strong robustness, consists of two parts, namely a neural network for approximating the nonlinear uncertainty function and a modified variable structure term for eliminating the approximate error and guaranteeing the finite-time convergence. According to the analysis based on the Lyapunov theory and the relative finite-time stability theory, the neural network is asymptotically convergent and the controlled robotic system is finite time stable. The proposed controller is then verified on a two-link robotic manipulator by simulations and experiments, with satisfactory control performance being obtained even in the presence of various uncertainties and external disturbances.     

Predefined-time control for Euler–Lagrange systems with model uncertainties and actuator faults

A novel adaptive predefined-time tracking control algorithm is proposed for the Euler–Lagrange systems (ELSs) with model uncertainties and actuator faults. Compared with traditional finite-time and fixed-time studies, the system output tracking error under the proposed predefined-time controller converges to a small neighborhood of zero in finite time, whose upper bound is exactly a design parameter in the control algorithm. For the uncertain model, radial-based function neural network (RBFNN) is utilized to approximate the continuous uncertain dynamics. To deal with the actuator faults, an adaptive control law is involved in the fault-tolerant controller. In order to achieve the predefined-time bounded, a novel predefined-time sliding mode surface is designed. It is proved that the tracking error vector trajectory of closed-loop system is semi-globally uniformly ultimately predefined-time bounded, and the upper bounds of both the system settling time and the corresponding output tracking error can be adjusted with a simple parameter. Simulation examples finally demonstrate the effectiveness of the proposed control algorithm.     

Control Surface Faults Neural Adaptive Compensation Control for Tailless Flying Wing Aircraft with Uncertainties

A neural adaptive compensation tracking control scheme considering the prescribed tracking performance bound is proposed for a flying wing aircraft with control surface faults, actuator saturation and uncertainties of aerodynamic parameters. Second-order command filters are introduced to avoid the saturation of the actuators, prescribed performance bound strategy is designed to characterize the convergence rate and maximum overshoot of the tracking error, uncertainties of aerodynamic parameters are approximated by online RBF neural networks, and control allocation law is designed to reduce the coupling of the flight dynamics. The closed-loop control law is given based on adaptive backstepping compensation control scheme, and the stability of the closed-loop system is proved by Lyapunov based design. Simulation results are given to illustrate the effectiveness of the proposed neural adaptive compensation control scheme.     

基于自适应神经网络的不确定非线性系统的模糊跟踪控制

提出了一种基于模糊模型和自适应神经网络的跟踪控制方法.在系统具有未知不确定非线性特性的情况下,首先利用T＿S模糊模型对系统的已知特性进行近似建模,对基于模糊模型的模糊H∞跟踪控制律进行输出跟踪控制.并在此基础上,进一步采用RBF神经网络完全自适应控制,通过在线自适应调整RBF神经网络的权重、函数中心和宽度,从而有效地消除系统的未知不确定性和模糊建模误差的影响,保证了非线性闭环系统的稳定性和系统的H∞跟踪性能,而不要求系统的不确定项和模糊建模误差满足任何匹配条件或约束.最后,将所提出的方法应用到一非线性混沌系统,仿真结果表明了所提出的方案不仅能够有效地稳定该混沌系统,而且能使系统输出跟踪期望输出.     

Direct adaptive type-2 fuzzy neural network control for a generic hypersonic flight vehicle

A direct adaptive interval type-2 fuzzy neural network (IT2-FNN) controller is designed for the first time in hypersonic flight control. The generic hypersonic flight vehicle is a multi-input multi-output system whose longitudinal model is high-order, highly nonlinear, tight coupling and most of all includes big uncertainties. Interval type-2 fuzzy sets with Gaussian membership functions are used in antecedent and consequent parts of fuzzy rules. The IT2-FNN directly outputs elevator deflection and throttle setting which make the GHFV track the altitude command signal and meanwhile maintain its velocity. The parameter adaptive law of IT2-FNN is derived using backpropagation method. The deviation of the control signal from the nominal dynamic inversion control signal is used as the reference output signal of IT2-FNN. The tracking errors of velocity and altitude are used as inputs of IT2-FNN. Tracking differentiator is designed to form an arranged transition process (ATP) of the command signal as well as ATP’s high-order derivatives. Nonlinear state observer is designed to get the approximations of velocity, altitude as well as their high-order derivatives. Simulation results validate the effectiveness and robustness of the proposed controller especially under big uncertainties.     

基于改进的FNNs移动机械臂输出反馈跟踪控制

针对包含电机动态模型的移动机械臂系统,提出一种鲁棒自适应输出反馈控制方法.将误差符号函数鲁棒积分反馈与神经网络前馈结构相结合用于控制器的设计,然后利用神经网络去逼近机器人和电机系统的不确定项,设计鲁棒项实时补偿网络误差.通过Lyapunov稳定性分析证明闭环系统所有信号半全局一致有界.最后仿真实验表明,控制方法对系统动态不确定性和外界干扰有很好的鲁棒性,可实现移动机械臂的输出反馈跟踪控制.     

Finite-time adaptive neural control for uncertain nonlinear time-delay systems with actuator delay and full-state constraints

This paper investigates finite-time adaptive neural tracking control for a class of nonlinear time-delay systems subject to the actuator delay and full-state constraints. The difficulty is to consider full-state time delays and full-state constraints in finite-time control design. First, finite-time control method is used to achieve fast transient performances, and new Lyapunov–Krasovskii functionals are appropriately constructed to compensate time delays, in which a predictor-like term is utilized to transform input delayed systems into delay-free systems. Second, neural networks are utilized to deal with the unknown functions, the Gaussian error function is used to express the continuously differentiable asymmetric saturation nonlinearity, and barrier Lyapunov functions are employed to guarantee that full-state signals are restricted within certain fixed bounds. At last, based on finite-time stability theory and Lyapunov stability theory, the finite-time tracking control question involved in full-state constraints is solved, and the designed control scheme reduces learning parameters. It is shown that the presented neural controller ensures that all closed-loop signals are bounded and the tracking error converges to a small neighbourhood of the origin in a finite time. The simulation studies are provided to further illustrate the effectiveness of the proposed approach.     

基于干扰观测器的一类不确定仿射非线性系统有限时间收敛backstepping控制

针对一类不确定仿射非线性系统的跟踪控制问题,提出一种基于干扰观测器的有限时间收敛backstepping控制方法.为增强小脑模型(CMAC)泛化和学习能力,将非对称高斯函数和模糊理论相结合,给出非对称模糊CMAC结构,设计干扰观测器实现系统未知复合干扰在线准确逼近;基于非对称模糊CMAC干扰观测器,给出有限时间收敛backstepping控制器设计步骤,利用Lyapunov稳定理论证明闭环系统稳定性,其中采用非线性微分器获取虚拟控制量滤波和微分信息以避免backstepping设计中的微分“膨胀问题”,设计辅助系统修正因微分器带来的误差对系统跟踪性能影响,引入基于障碍型函数的自适应滑模鲁棒项抑制复合干扰估计偏差对跟踪误差的影响;将所提方法应用于无人机飞行控制仿真实验,结果表明所提方法的有效性.     

Finite-time command filter-based adaptive tracking control for nonstrict feedback nonlinear systems with full-state restrictions and unmodeled dynamics

The finite-time command filter tracking control for a class of nonstrictly feedback nonlinear systems with unmodeled dynamics and full-state constraints is investigated in this paper. The hyperbolic tangent function is used as a nonlinear mapping technique to solve the obstacle of the full-state constraints. A new adaptive finite time control method is proposed through command filtering reverse engineering, and the shortcomings of the dynamic surface control (DSC) method are overcome by the error compensation mechanism. Dynamic signal is designed to handle dynamical uncertain terms. Normalization signal is designed to handle input unmodeled dynamics. Unknown nonlinear functions are approximated by radial basis function neural networks. Based on the Lyapunov stability theory, it is proved that all signals in the closed-loop system are semi-globally consistent and finally bounded and the output tracking error converges in finite time. Two numerical examples are utilized to verify the effectiveness of the proposed control approach.     

电动舵机的复合自适应非奇异终端滑模控制

非奇异终端滑模控制（NTSMc）能够实现误差的有限时间收敛,但一般NTSMC的控制律采用高增益项来消除系统不确定性的影响,具有较强的保守性．为了降低这种保守性,本文采用复合白适应律对系统的不确定参数进行估计,按照估计值设计复合自适应非奇异终端滑模控制（CANTSMC）,对不确定参数引起的系统动态进行补偿．本方案曾经应用于电动舵机,以补偿不确定参数对模型动态性能的影响．本文证明了闭环稳定性,以及输出跟踪误差在有限时间内的收敛性．通过仿真验证了该方法的有效性．     

Adaptive robust fuzzy control for path tracking of a wheeled mobile robot

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Dynamic structure adaptive neural fuzzy control for MIMO uncertain nonlinear systems

This paper proposes a novel dynamic structure neural fuzzy network (DSNFN) to address the adaptive tracking problems of multiple-input-multiple-output (MIMO) uncertain nonlinear systems. The proposed control scheme uses a four-layer neural fuzzy network (NFN) to estimate system uncertainties online. The main feature of this DSNFN is that it can either increase or decrease the number of fuzzy rules over time based on tracking errors. Projection-type adaptation laws for the network parameters are derived from the Lyapunov synthesis approach to ensure network convergence and stable control. A hybrid control scheme that combines the sliding-mode control and the adaptive bound estimation control with different weights improves system performance by suppressing the influence of external disturbances and approximation errors. As the employment of the DSNFN, high-quality tracking performance could be achieved in the system. Furthermore, the trained network avoids the problems of overfitting and underfitting. Simulations performed on a two-link robot manipulator demonstrate the effectiveness of the proposed control scheme.     

不确定非线性离散系统指令滤波事件触发控制

本文提出了考虑输入饱和的一类不确定非线性离散系统的事件触发指令滤波控制方法. 采用指令滤波控制技术解决了传统反步法存在的“因果矛盾”问题, 引入补偿机制提高了系统的控制精度; 利用事件触发机制能够避免自适应律和控制律的频繁更新, 降低了计算负担, 提高了资源利用率; 运用模糊逻辑系统逼近系统中未知的非线性函数; 结合李雅普诺夫稳定性理论, 验证了提出的控制方案能够保证跟踪误差收敛到原点小的邻域内以及闭环系统的所有信号有界. 仿真结果表明, 本文提出的控制方法具有较强的鲁棒性及较好的跟踪性能.     

Finite-time fault-tolerant control for flutter of wing

In this paper, we use the radial basis function neural network and the finite-time H_∞ adaptive fault-tolerant control technique to deal with the flutter problem of wings with propulsion system, which is affected by input saturation, time delay, time-varying parameter uncertainties and external disturbances. Then sensor and actuator faults are both considered in the control design. The theory content of this article includes the trajectory optimization, modeling of wing flutter and fault-tolerant controller design. The stability of the finite-time H_∞ adaptive fault-tolerant controller is theoretically proved. Finally, simulation results are given to demonstrate the effectiveness of the scheme.     

带有输出约束的水面船舶实际有限时间控制

考虑带有输出约束的水面船舶系统,提出一种自适应神经网络航迹跟踪实际有限时间控制算法.基于反步法设计有限时间控制律,构造障碍李雅普诺夫函数处理输出约束问题,采用神经网络逼近船舶模型中的不确定信息.在控制算法递推过程中,通过设计一个关于跟踪误差的可微幂函数来避免控制器中的奇异问题.借助李雅普诺夫稳定性分析理论,证明了航迹跟踪误差在有限时间内收敛到有界的邻域内.最后,以一艘1:70的比例模型船作为仿真对象,来验证所提出的航迹跟踪实际有限时间控制算法的有效性.     

Nonsingular terminal sliding mode based finite-time control for spacecraft attitude tracking

This paper studies finite-time attitude tracking control problem of a rigid spacecraft system with external disturbances and inertia uncertainties. Firstly, a new finite-time attitude tracking control law is designed using nonsingular terminal sliding mode concepts. In the absence and presence of external disturbances and inertia uncertainties, this controller can drive the attitude and angular velocity tracking errors to reach zero in finite time. Secondly, a finite-time disturbance observer is introduced to estimate the disturbance, and a composite controller is developed which consists of a feedback control based on nonsingular terminal sliding mode method and compensation term based on finite-time disturbance observer. Finite-time convergence of attitude tracking errors and the stability of the closed-loop system is ensured by the Lyapunov approach. Numerical simulations on attitude control of spacecraft are also given to demonstrate the performance of the proposed controllers.     

一类机械臂系统自适应有限时间有界H∞跟踪控制

考虑一种电机驱动的单连杆机械臂系统在受到输出约束时的自适应有限时间H∞跟踪控制问题.一个有限时间有界H∞性能的新概念被提出,并结合障碍Lyapunov函数(BLF)、神经网络自适应技术、有限时间控制理论和H∞控制理论,提出了一种该系统在输出受限条件下的自适应神经有限时间有界H∞跟踪控制器设计方法,避免了许多有限时间控制...     

Direct adaptive neural flight control system for an unstable unmanned aircraft

A direct adaptive controller design using neural network is proposed for an unstable unmanned research aircraft similar in configuration to combat aircraft. The control law to track the pitch rate command is developed based on system theory. Neural network with linear filters and back propagation through time learning algorithm is used to approximate the control law. The bounded signal requirement to develop the neural controller is circumvented using an off-line finite time training scheme, which provides the necessary stability and tracking performances. On-line learning scheme is implemented to compensate for uncertainties due to variation in aerodynamic coefficients, control surface failures and also variations in center of gravity position. The performance of the proposed control scheme is validated at different flight conditions. The disturbance rejection capability of the neural controller is analyzed in the presence of the realistic gust and sensor noises. Hardware-in-loop simulation is also carried out to study the behavior of control surface deflections in real-time.     

串级连续搅拌反应釜的有限时间命令滤波控制

针对串级连续搅拌反应釜系统的快速精准跟踪控制问题,利用自适应反步控制方法、模糊逻辑系统、命令滤波器以及有限时间控制技术设计串级连续搅拌反应釜系统的有限时间命令滤波控制器.其中,自适应反步方法使系统控制器的设计更简单;模糊逻辑系统通过逼近系统模型中的复杂非线性函数使控制器的在线计算量更小;命令滤波器解决了经典反步法带来的“计算爆炸”的问题;有限时间控制方法能够使系统被控量更迅速地跟踪其参考值; Lyapunov稳定性分析证明了系统的稳定性.通过Matlab实例仿真验证所设计控制器的有效性和可行性,为有限时间命令滤波控制技术在实际串级连续搅拌反应釜过程中的应用提供指导.与现有控制方法相比,所提出的控制策略具有控制器结构简单、在线计算复杂度小、跟踪速度快以及无静差的优点.