一类纯反馈非线性系统的简化自适应神经网络动态面控制

针对一类完全非仿射纯反馈非线性系统,提出一种简化的自适应神经网络动态面控制方法.基于隐函数定理和中值定理将未知非仿射输入函数进行分解,使其含有显式的控制输入;利用简化的神经网络逼近未知非线性函数,对于阶SISO纯反馈系统,仅一个参数需要更新;动态面控制可消除反推设计中由于对虚拟控制反复求导而导致的复杂性问题.通过Lyapunov稳定性定理证明了闭环系统的半全局稳定性,数值仿真验证了方法的有效性.     

一类状态/输入受限的不确定非仿射非线性系统 鲁棒自适应backstepping控制

针对一类状态/输入受限的不确定严格反馈非仿射非线性系统跟踪控制问题,提出一种鲁棒自适应backstepping控制策略.在保证系统精度的前提下,对状态/输入受限的非仿射系统进行Taylor级数在线展开,得到其仿射形式;为保证系统复合扰动在线准确逼近,提出基于投影算子的递归扰动模糊神经网络干扰观测器(RPFNNDO);在考虑不确定系统存在状态受限和输入饱和等因素下,结合障碍Lyapunov函数、tanh函数及Nussbaum函数,利用backstepping方法设计控制器,并采用Lyapunov稳定理论分析闭环系统稳定性.应用于无人机航迹控制的仿真结果验证了所提方法的有效性.     

输入饱和及输出受限的纯反馈非线性系统控制

针对具有输入饱和和输出受限的纯反馈非线性系统,设计了神经网络自适应控制器.首先利用隐函数定理和中值定理将非仿射形式的纯反馈非线性系统转换成有显式输入的非线性系统,基于李雅普诺夫第二方法以及反推法并采用障碍型李雅普诺夫函数进行控制器的设计,最后通过稳定性分析证明了闭环控制系统是半全局一致最终有界的,利用仿真例子验证了控制...     

不确定非仿射系统的引入动态逆的自抗扰控制器设计

针对一类不确定非仿射严反馈非线性系统, 提出一种引入动态逆的线性自抗扰控制器设计方法. 首先, 利 用微分同胚映射将严反馈非线性系统变换为积分串联型系统, 然后针对积分串联型系统设计线性自抗扰控制器. 提出的线性自抗扰控制器将闭环系统划分为3个时间尺度, 其中线性扩张状态观测器位于最快的时间尺度上, 用来 估计系统的状态和总和扰动, 动态逆位于次快的时间尺度上用以求解非仿射情况下的控制律, 系统动态位于最慢的 时间尺度上. 利用奇异摄动理论分析了闭环系统的稳定性和性能. 提出的自抗扰控制设计方法同样适用于控制增 益不确定的仿射非线性系统. 仿真和实验结果验证了提出的线性自抗扰控制器的可行性.     

非仿射纯反馈系统的间接自适应神经网络控制

针对非仿射纯反馈系统,提出了一种新的设计方案.与现有文献中方法不同,该方案不是直接利用逼近技巧构建理想的反馈控制器.首先通过自抗扰思想将非仿射纯反馈系统转化成含有未知控制系数以及未知非线性的仿射系统,并且证明了可行性.然后结合微分器和全调节径向基函数神经网络,利用自适应反演技巧设计了自抗扰控制器,微分器的引入避免了传统反演的计算复杂性.最后,从理论上证明了所设计的控制器能够保证闭环系统所有信号半全局一致有界,并且证明了系统状态渐进收敛到零点的残集内.仿真例子验证了算法的有效性.     

一类输入饱和非仿射非线性系统的非线性动态逆控制

输入饱和是实际系统中经常遇到的问题,很多已有的控制方法要求被控系统具有仿射结构.本文针对一类具有输入饱和的非仿射纯反馈非线性系统提出了一种基于奇异值摄动理论的非线性动态逆控制方法.首先构建一个快变子系统,在慢时间尺度下将非仿射非线性系统转换为具有仿射结构的线性系统,从而应用已有的控制算法实现控制目的.为了消除输入饱和带...     

基于观测器的一类非仿射非线性系统的自适应神经网络H∞跟踪控制

针对一类带有外部干扰、状态不可测的非仿射非线性系统,提出了基于观测器的自适应神经网络H∞跟踪控制结构.利用隐函数定理和泰勒公式及中值定理,将非仿射非线性系统转变为仿射型非线性系统.控制器由等效控制器和H∞控制器组成,H∞控制器用于减弱外部干扰及神经网络逼近误差对跟踪的影响.总体控制方案及基于李亚普诺荇夫稳定性理论的权值更新律保证了系统的稳定性及跟踪误差渐近收敛于零,并使干扰对系统的影响衰减到指定的性能指标.理论分析及仿真结果均证明了本文方法的有效性.     

一类非线性耗散系统的逆最优控制

首先研究一类单输入非仿射非线性系统的逆最优控制问题, 其代价泛函为非线性-非二次型, 设计出一族参数化的状态反馈逆最优控制器;然后讨论当该系统为耗散系统时, 在供给率为二次型的耗散性理论框架下,给出使系统渐近稳定的李雅普诺夫函数和镇定控制律, 并通过适当选取代价泛函中的参数,使得李雅普诺夫函数也是最优值函数,进而揭示出耗散系统在线性输出反馈意义下稳定性与最优性之间的等价关系.     

基于神经网络的迟滞非线性补偿控制

提出了一种基于神经网络的迟滞非线性的补偿方法.首先构造一个Duhem逆算子来描述迟滞逆状态.然后利用神经网络来逼近此状态和输出之间的关系来得到神经网络迟滞逆模型,神经网络权值采用反馈误差学习方法来进行在线调整.系统的前馈控制器和反馈控制器分别为逆模型和PID控制器.该方法不需要建立迟滞的正模型,能够在线构造逆模型来实现迟滞补偿.最后通过仿真验证了该方法的有效性.     

带有输入饱和及输出受限非仿射系统固定时间跟踪控制

针对具有量化输入饱和及输出受限的非线性非仿射系统,提出固定时间自适应神经网络跟踪控制方法.引入中值定理解决系统具有非仿射结构的问题;基于反步法,使用Barrier Lyapunov函数约束系统输出,并利用RBF神经网络逼近未知函数;根据固定时间控制理论设计输入信号,该输入信号由滞后量化器量化,以降低控制信号的通信速率,并保证该系统在满足量化输入饱和及输出受限的条件下,系统可以在固定时间内跟踪上期望信号,且该系统收敛时间与初始状态无关.最后通过Matlab仿真软件验证所设计控制器的有效性.     

Neural-network control of nonaffine nonlinear system with zero dynamics by state and output feedback

This paper focuses on adaptive control of nonaffine nonlinear systems with zero dynamics using multilayer neural networks. Through neural network approximation, state feedback control is firstly investigated for nonaffine single-input-single-output (SISO) systems. By using a high gain observer to reconstruct the system states, an extension is made to output feedback neural-network control of nonaffine systems, whose states and time derivatives of the output are unavailable. It is shown that output tracking errors converge to adjustable neighborhoods of the origin for both state feedback and output feedback control.     

Globally Stabilizing a Class of Underactuated Mechanical Systems on the Basis of Finite-Time Stabilizing Observer

Globally exponentially stabilizing a class of underactuated mechanical systems (UMS) with nonaffine nonlinear dynamics is investigated in this paper. The considered UMS has a nonaffine nonlinear subsystem that can be globally asymptotically stabilized by saturated feedbacks, but the saturated feedback cannot be analytically expressed in closed-form. This obstacle limits the real-time applications of most controllers presented in literatures. In this paper, a hybrid feedback strategy is presented to globally exponentially stabilize the UMS with nonaffine and strict-feedback canonical forms. The hybrid feedback strategy is characterized by the composition of partial states feedback and partial virtual outputs feedback based on a higher-order finite-time stabilizing observer. The presented hybrid feedback controller can be synthesized by applying Lyapunov stability theory. Some numerical simulations associated with two underactuated nonlinear systems, the Acrobot system and the Inertia-Wheel-Pendulum (IWP) system, are employed to demonstrate the effectiveness of the proposed controller. The presented control strategy can be applied in real time, thus providing a new feasible dynamic model other than the differential flatness systems for synthesizing the mechanical systems of general underactuated legged robots.     

Feedback-Linearization-Based Neural Adaptive Control for Unknown Nonaffine Nonlinear Discrete-Time Systems

A new feedback-linearization-based neural network (NN) adaptive control is proposed for unknown nonaffine nonlinear discrete-time systems. An equivalent model in affine-like form is first derived for the original nonaffine discrete-time systems as feedback linearization methods cannot be implemented for such systems. Then, feedback linearization adaptive control is implemented based on the affine-like equivalent model identified with neural networks. Pretraining is not required and the weights of the neural networks used in adaptive control are directly updated online based on the input–output measurement. The dead-zone technique is used to remove the requirement of persistence excitation during the adaptation. With the proposed neural network adaptive control, stability and performance of the closed-loop system are rigorously established. Illustrated examples are provided to validate the theoretical findings.      

Tracking control of electro-hydraulic servo multi-closed-chain mechanisms with the use of an approximate nonlinear internal model

This paper studied the trajectory-tracking problem of a hydraulic servo multi-closed-chain mechanism. The nonaffine nonlinear characteristic of the electro-hydraulic actuator and its time-varying uncertainty load resulting from the multi-closed-chain mechanism was taken into consideration in the proposed novel nonlinear control algorithm, that is, the approximate internal model control (AIMC) integrated with a position feedback control in cascade control design. This algorithm improves the trajectory-tracking performance of the hydraulic servomechanism (HSM). To reduce the difficulty in directly utilizing the AIMC for the HSM position trajectory, the complex electro-hydraulic mechanical system was divided into two subsystems: nonaffine nonlinear, and linear. The AIMC controller was designed for the nonaffine nonlinear subsystem to realize velocity trajectory tracking control, whereas a position feedback control was derived for the linear subsystem. The position trajectory tracking control was achieved by congruently combining the AIMC, and the position feedback control based on a recursive design idea. In addition, a complete state-space mathematical model for the HSM was developed and illustrated through simulations and experiments. Based on the proposed approach and the AIMC, the desired position and velocity trajectory tracking was examined on a hydraulic forging manipulator. The stability of the proposed method was analytically derived. Results of the simulations and experiments performed with the hydraulic manipulator demonstrated the effectiveness of the proposed approach.     

Generalized PI control design for a class of unknown nonaffine systems with sensor and actuator faults

This work deals with the tracking control problem of a class of unknown nonaffine dynamic systems that involve unpredictable sensor and actuation failures. As the control inputs enter into and influence the dynamic behavior of the nonaffine system through a nonlinear and implicit way, control design for such system becomes quite challenging. The underlying problem becomes even more complex if the system dynamics are unavailable for control design yet involving unanticipated sensor and/or actuator faults. In this work, a structurally simple and computationally inexpensive control scheme is proposed to achieve uniformly ultimately bounded (UUB) stable tracking control of a class of nonaffine systems. The proposed control is of a generalized PI form and is able to accommodate both sensor and actuator faults. The effectiveness of the proposed control strategy is confirmed by theoretical analysis and numerical simulations.     

一类非仿射非线性时滞系统的动态状态反馈镇定

本文讨论了一类非仿射非线性时滞系统的全局镇定问题.通过引入辅助积分系统和构造合适的LyapunovKrosovskii泛函,提出了一种基于反推法的时滞无关动态状态反馈控制器,所提控制方法无需时滞的任何先验知识.利用Lyapunov稳定性理论证明了该控制策略能够保证非仿射时滞系统状态渐近收敛于原点,且所有闭环信号全局有界.一个仿真实例进一步验证了所得控制方案的可行性与有效性.     

A novel error observer-based adaptive output feedback approach for control of uncertain systems

We develop an adaptive output feedback control methodology for nonaffine in control of uncertain systems having full relative degree. Given a smooth reference trajectory, the objective is to design a controller that forces the system measurement to track it with bounded errors. A neural network with linear parameters is introduced as an adaptive signal. A simple linear observer is proposed to generate an error signal for the adaptive laws. Ultimate boundedness is shown through Lyapunov's direct method. Simulations of a nonlinear second-order system illustrate the theoretical results.     

Improved prescribed performance control for nonaffine pure‐feedback systems with input saturation

This work considers an input and output constraint control problem for pure‐feedback systems with nonaffine functions possibly being in‐differentiable. A locally semibounded and continuous condition for nonaffine functions is presented to guarantee the controllability, and the nonaffine system is transformed to an equivalent pseudoaffine one based on the mild condition. Combined with backstepping technique, a novel prescribed performance controller with new performance functions is constructed to circumvent high frequency chattering in control input. An auxiliary system with bounded compensation term is utilized in this paper, successfully avoiding the overrun of control input. The methodology achieves the desired transient and steady‐state performance and presents excellent robustness against the system uncertainty. Finally, two numerical simulations are performed to demonstrate the effectiveness of the proposed approach.     

Adaptive dynamic surface control for uncertain nonaffine nonlinear systems

This paper focuses on the problem of adaptive control for uncertain nonaffine nonlinear systems. The original nonaffine systems are transformed into the augmented affine systems via adding an auxiliary integrator, which makes the explicit control design possible. By introducing a modified sliding mode filter in each step, a novel adaptive dynamic surface controller is proposed, where the ‘explosion of complexity’ problem inherent in the backstepping design is avoided. It is proven rigorously that for any initial control condition, the proposed adaptive scheme is able to ensure the semiglobal uniformly ultimately boundedness of all signals in the closed loop. An illustrative example is carried out to verify the effectiveness of the proposed approach. Copyright © 2016 John Wiley & Sons, Ltd.     

基于Backstepping 的非仿射非线性系统鲁棒控制

针对一类不确定非仿射非线性系统的跟踪控制问题, 提出一种鲁棒Backstepping 控制策略. 首先, 为利用仿 射非线性方法设计控制器, 给出一种适用于全局的非仿射非线性近似方法; 然后, 设计快速收敛非线性微分器以估计复合干扰和获取虚拟信号的微分, 进而给出不确定非仿射非线性系统的复合控制器, 其中鲁棒项和阻尼项分别用于减少逼近误差和近似方法中动态误差对系统跟踪的影响; 最后, 通过仿真实验验证了所提出方法的有效性.