具有输入输出约束的无人直升机预设性能安全跟踪控制

针对无人直升机姿态与高度系统存在未知外部干扰、输入饱和、姿态与高度约束等问题, 本文提出一种具 有输入输出约束的预设性能安全跟踪控制方法. 首先, 针对无人直升机的姿态与高度约束, 通过设计一类边界保护 算法, 构建了新的安全期望跟踪信号. 为了保证系统对于安全期望跟踪信号的跟踪性能, 将预设性能函数与边界保 护算法进行结合, 并对跟踪误差进行转换. 针对系统的输入饱和现象, 使用Sigmoid函数进行逼近; 同时, 针对饱和函 数的逼近误差与未知外部干扰构成的复合干扰, 采用参数自适应方法对其上界进行逼近. 然后, 结合反步控制方法 设计了安全跟踪控制器, 并通过Lyapunov稳定性理论证明了闭环系统所有信号的收敛性, 保证了无人直升机的安全 跟踪性能. 最终, 通过数值仿真验证了所提控制方法的有效性.     

Adaptive Neural Tracking Control of Full-state Constrained Nonstrict-feedback Time-delay Systems with Input Saturation

In this study, an adaptive neural backstepping control scheme is proposed for a class of nonstrict-feedback time-delay systems with input saturation, full-state constraints and unknown disturbances. A structural property of radial basis function neural network is presented to deal with the design from the nonstrict-feedback formation. This method does not require the parameter separation technique and its assumption. With the help of the Lyapunov-Krasovskii functionals and Young’s inequalities, the effects of time delays are compensated, and the unknown disturbances are eliminated in the design process. The barrier Lyapunov function (BLF) is applied to arrest the violation of the full-state constraints. To overcome the problem of input saturation nonlinearity, the smooth nonaffme function of the control input signal is adopted to approach the input saturation function. Moreover, an adaptive backstepping neural control strategy is proposed. The proposed adaptive neural controller ensures that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB). Furthermore, the tracking error can converge to a small neighborhood of the origin. The simulation result shows the effectiveness of this method.

     

基于修正闭环子空间辨识--分段线性结构的 环管式丙烯聚合反应过程非线性模型预测控

本文提出一种非奇异终端滑模funnel控制(NTSMFC)方法, 实现带有饱和输入电机伺服系统的指定性能跟踪控制. 根据中值定理, 非光滑饱和函数转化为放射形式, 并且应用一个简单的神经网络进行逼近和补偿. 为保证跟踪误差被限制在指定的界限内, 同时为避免构建复杂的barrier李雅普诺夫函数或逆函数, 本文采用一个新的限制变量. 然后, 构建非奇异终端滑模funnel控制器保证电机伺服系统的指定跟踪性能. 该方法无需事先已知输入饱和函数的界限等先验知识, 且基于李雅普诺夫函数设计可以保证位置跟踪误差的收敛性, 最后给出仿真对比实例证明了该方法的有效性.     

带有饱和的电机伺服系统非奇异终端滑模funnel控制

本文提出一种非奇异终端滑模funnel控制(NTSMFC)方法, 实现带有饱和输入电机伺服系统的指定性能跟踪控制. 根据中值定理, 非光滑饱和函数转化为放射形式, 并且应用一个简单的神经网络进行逼近和补偿. 为保证跟踪误差被限制在指定的界限内, 同时为避免构建复杂的barrier李雅普诺夫函数或逆函数, 本文采用一个新的限制变量. 然后, 构建非奇异终端滑模funnel控制器保证电机伺服系统的指定跟踪性能. 该方法无需事先已知输入饱和函数的界限等先验知识, 且基于李雅普诺夫函数设计可以保证位置跟踪误差的收敛性, 最后给出仿真对比实例证明了该方法的有效性.     

Discrete-time extended state observer-based model-free adaptive constrained sliding mode control with modified prescribed performance via CFDL and PFDL

To tackle the trajectory tracking problem and achieve high control accuracy in many actual nonlinear systems with unknown dynamics and input saturation, a novel discrete-time extended state observer-based model-free adaptive constrained sliding mode control with modified prescribed performance is investigated via compact-form dynamic linearization (CFDL) and partial-form dynamic linearization (PFDL). Firstly, the original non-affine system is turned into an affine one comprising an unknown nonlinear term and a linearly parametric term affine to the input via both PFDL and CFDL. Then, a discrete-time extended state observer (DESO) is used to estimate the lumped disturbance containing the unknown nonlinear time-varying term and the term relevant to the estimation error of pseudo partial derivative (PPD) parameter. Furthermore, a modified prescribed performance function is introduced in the model-free adaptive sliding mode control scheme to keep the output tracking error in the prescribed bound without causing any asymmetric offset error in the steady-state. Meanwhile, to suppress the influence of input saturation on the control system, an anti-windup compensator is used. Finally, rigorous theoretical analyses show the robust convergence of the tracking error via the proposed CFDL and PFDL-based methods under external disturbances. Simulations verify the superiority of the modified prescribed performance function, DESO, and anti-windup compensator in the proposed method. Also, the effects of the PFDL-based method and the CFDL-based one are compared during the simulation.     

不确定分数阶多涡卷混沌系统自适应重复学习同步控制

研究了不确定分数阶多涡卷混沌系统的自适应重复学习同步控制问题.通过利用滞环函数,设计了一类参数可调的分数阶多涡卷混沌系统.针对这类分数阶多涡卷混沌系统,在考虑非参数化不确定性、周期时变参数化不确定性、常参数化不确定性和外部扰动情况下,提出了一种重复学习同步控制方案.利用自适应神经网络技术补偿了系统中的函数型不确定性,通过自适应重复学习控制技术处理了周期时变参数化不确定性,并利用自适应鲁棒学习项处理了神经网络逼近误差和干扰的影响,实现了主系统和从系统的完全同步.综合利用分数阶频率分布模型和类Lyapunov复合能量函数方法证明了同步误差的学习收敛性.数值仿真验证了所提方法的有效性.     

具有输入饱和的近空间飞行器鲁棒控制

针对近空间飞行器这一类存在外部扰动,输入饱和和参数不确定的多输入多输出线性系统,提出了一种基于干扰观测器的抗饱和鲁棒控制方案.将干扰观测器与抗饱和控制技术相结合,从而消除系统存在的未知外部扰动、输入饱和和不确定性对系统控制的影响.首先,设计干扰观测器对线性外部系统产生的未知扰动进行估计.然后根据干扰观测器输出,通过超前抗饱和方法设计抗饱和补偿器,并将其加入到鲁棒控制器的设计中,保证闭环系统存在输入饱和、未知外部扰动和参数不确定情况下的稳定性.为便于设计,干扰观测器、抗饱和补偿器和控制器设计矩阵均通过求解线性矩阵不等式得到.最后,将提出的鲁棒抗饱和控制方法应用于近空间飞行器,仿真结果验证了该控制方案的有效性.     

Adaptive neural network asymptotical tracking control for an uncertain nonlinear system with input quantisation

In this paper, the problem of adaptive neural network asymptotical tracking is investigated for a class of nonlinear system with unknown function, external disturbances and input quantisation. Based on neural network technique, an adaptive asymptotical tracking controller is provided for an uncertain nonlinear system via backstepping method. In order to reduce complexity of the control algorithm in the backstepping design process, a sliding mode differentiator is employed to estimate the virtual control law and only two parameters need to be estimated via adaptive control technique. The stability of the closed-loop system is analysed by using Lyapunov function method and zero-tracking error performance is obtained in the presence of unknown nonlinear function, external disturbances and input quantisation. Finally, an application example is employed to demonstrate the effectiveness of the proposed scheme.     

Adaptive prescribed performance control for switched nonlinear systems with input saturation

In this paper, an adaptive prescribed performance output-feedback control scheme is proposed for a class of switched nonlinear systems with input saturation. The MT-filters are employed to estimate the unmeasured states and the unknown functions are approximated by the radial basis function neural networks in controller design procedure. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error satisfies the prescribed performance. Finally, simulation results are given to illustrate the effectiveness of the proposed approach.     

Synchronization of uncertain chaotic systems based on adaptive type-2 fuzzy sliding mode control

A novel direct adaptive interval type-2 fuzzy neural network (FNN) controller in which linguistic fuzzy control rules can be directly incorporated into the controller is developed to synchronize chaotic systems with training data corrupted by noise or rule uncertainties involving external disturbances, in this paper. By incorporating direct adaptive interval type-2 FNN control scheme and sliding mode approach, two non-identical chaotic systems can be synchronized based on Lyapunov stability criterion. Moreover, the chattering phenomena of the control efforts can be reduced and the external disturbance on the synchronization error can be attenuated. The stability of the proposed overall adaptive control scheme will be guaranteed in the sense that all the states and signals are uniformly bounded. From the simulation example, to synchronize two non-identical Chua’s chaotic circuits, it has been shown that type-2 FNN controllers have the potential to overcome the limitations of tpe-1 FNN controllers when training data is corrupted by high levels of uncertainty.     

Projective synchronization of two different fractional-order chaotic systems via adaptive fuzzy control

In this paper, the projective synchronization problem of two fractional-order different chaotic (or hyperchaotic) systems with both uncertain dynamics and external disturbances is considered. More particularly, a fuzzy adaptive control system is investigated for achieving an appropriate projective synchronization of unknown fractional-order chaotic systems. The adaptive fuzzy logic systems are used to approximate some uncertain nonlinear functions appearing in the system model. These latter are augmented by a robust control term to compensate for the unavoidable fuzzy approximation errors and external disturbances as well as residual error due to the use of the so-called e-modification in the adaptive laws. A Lyapunov approach is adopted for the design of the parameter adaptation laws and the proof of the corresponding stability as well as the asymptotic convergence of the underlying synchronization errors towards zero. The effectiveness of the proposed synchronization system is illustrated through numerical experiment results.     

基于自适应重复学习的不确定多涡卷混沌系统同步控制

基于滞环函数提出一种参数可调的多涡卷混沌系统构造方法. 针对复杂不确定性系统, 综合利用自适应神经网络和重复学习控制方法设计一种自适应重复学习同步控制器; 利用自适应重复学习控制方法对周期时变参数化不确定性进行处理; 对函数型不确定性利用神经网络逼近技术进行补偿; 设计鲁棒学习项对神经网络逼近误差和扰动上界进行估计; 通过构造类Lyapunov 复合能量函数证明了同步误差学习的收敛性. 仿真结果验证了所提出方法的有效性.

     

Adaptive neural prescribed performance control for a class of strict-feedback stochastic nonlinear systems under arbitrary switchings

This paper presents an adaptive neural tracking control scheme for strict-feedback stochastic nonlinear systems with guaranteed transient and steady-state performance under arbitrary switchings. First, by utilising the prescribed performance control, the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed. Second, radial basis function neural networks approximation are used to handle unknown nonlinear functions and stochastic disturbances. At last, by using the common Lyapunov function method and the backstepping technique, a common adaptive neural controller is constructed. The designed controller overcomes the problem of the over-parameterisation, and further alleviates the computational burden. Under the proposed common adaptive controller, all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded, and the prescribed tracking control performance are guaranteed under arbitrary switchings. Three examples are presented to further illustrate the effectiveness of the proposed approach.     

Chaotic synchronization in the presence of disturbances based on an orthogonal function neural network

Chaos synchronization problems are addressed in this paper. For chaotic synchronization systems with uncertainties and external disturbances, an orthogonal function neural network is used to achieve the synchronization of chaotic systems. Legendre orthogonal polynomials are selected as the basis functions of the orthogonal function neural network. An adaptive learning law is derived to guarantee that the tracking errors are bounded using Lyapunov stability theory. Simulation results show the efficiency of the proposed scheme. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

带执行器饱和的N 连杆机械臂输出反馈动态面控制

针对带执行器饱和的多关节刚性机械臂系统,提出一种基于RBF神经网络补偿的输出反馈动态面控制.通过观测器实现角速度的观测,采用RBF网络实现执行器饱和的补偿;通过Lyapunov方法证明闭环系统的稳定性,实现高精度的角度和角速度跟踪.仿真结果表明,所提出的方法能够有效补偿系统存在的执行器饱和,显著减小跟踪误差,并且对于外界干扰具有一定的鲁棒性.     

Adaptive sliding mode observer‐based synchronization for uncertain chaotic systems

This paper investigates the synchronization problem for a class of uncertain chaotic systems. Only partial information of the system states is known. An adaptive sliding mode observer‐based slave system is designed to synchronize a given chaotic master system with unknown parameters and external disturbances. Based on the Lyapunov stability theorem, the global synchronization between the master and slave systems is ensured. Furthermore, the structure of the slave system is simple and the proposed adaptive sliding mode observer‐based synchronization scheme can be implemented without requiring a priori knowledge of upper bounds on the norm of the uncertainties and external disturbances. Simulation results demonstrate the effectiveness and robustness of the proposed scheme. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Transverse function control with prescribed performance guarantees for underactuated marine surface vehicles

This paper studies the problem of stabilizing reference trajectories (also called as the trajectory tracking problem) for underactuated marine vehicles under predefined tracking error constraints. The boundary functions of the predefined constraints are asymmetric and time‐varying. The time‐varying boundary functions allow us to quantify prescribed performance of tracking errors on both transient and steady‐state stages. To overcome difficulties raised by underactuation and nonzero off‐diagonal terms in the system matrices, we develop a novel transverse function control approach to introduce an additional control input in backstepping procedure. This approach provides practical stabilization of any smooth reference trajectory, whether this trajectory is feasible or not. By practical stabilization, we mean that the tracking errors of vehicle position and orientation converge to a small neighborhood of zero. With the introduction of an error transformation function, we construct an inverse‐hyperbolic‐tangent‐like barrier Lyapunov function to show practical stability of the closed‐loop systems with prescribed transient and steady‐state performances. To deal with unmodeled dynamic uncertainties and external disturbances, we employ neural network (NN) approximators to estimate uncertain dynamics and present disturbance observers to estimate unknown disturbances. Subsequently, we develop adaptive control, based on NN approximators and disturbance estimates, that guarantees the prescribed performance of tracking errors during the transient stage of on‐line NN weight adaptations and disturbance estimates. Simulation results show the performance of the proposed tracking control.     

Prescribed performance adaptive fault tolerant control approach for nonlinear large-scale systems with time delay interconnection and dead zone input

In this study, a prescribed performance adaptive fault tolerant tracking control scheme is presented for a class of nonlinear large-scale systems with time delay interconnection, dead zone input, and actuator fault. The radial basis function neural networks are used to approximate unknown nonlinear functions. Different from the barrier Lyapunov functions used to achieve the symmetrical prescribed performance, a new error transformation is introduced in this study to achieve the desired asymmetrical prescribed performance. In addition, Nussbaum function is introduced to solve the difficulties caused by dead zone input and actuator fault. Based on the appropriate Lyapunov–Krasovskii functions, the effect of time delay interconnection could be compensated. By using backstepping procedures, an adaptive fault tolerant tracking control approach is developed for the considered large-scale systems, and the stability of the closed-loop systems is analyzed by Lyapunov theory. Meanwhile, the prescribed performance of the tracking error could be guaranteed. Finally, the effectiveness of the proposed control approach is illustrated by two simulation examples.     

一类输入受限的不确定非仿射非线性系统二阶动态terminal 滑模控制

针对一类输入受限的不确定非仿射非线性系统跟踪控制问题, 提出一种二阶动态terminal 滑模控制策略. 在不损失模型精度, 并考虑系统输入饱和受限的前提下, 给出一种适用于全局的不确定非仿射非线性系统近似方法. 提出小波小脑模型干扰观测器设计方法, 实现复合扰动的有效逼近. 构造辅助系统分析输入饱和对跟踪误差的影响. 通过构造基于PI 滑模面的terminal 二阶滑模面, 给出二阶动态terminal 滑模控制器设计过程, 克服了传统滑模的抖振问题. 仿真结果验证了所提出方法的有效性.

     

Synchronization of A Class of Uncertain Chaotic Systems with Lipschitz Nonlinearities Using State‐Feedback Control Design: A Matrix Inequality Approach

This paper proposes a new state‐feedback stabilization control technique for a class of uncertain chaotic systems with Lipschitz nonlinearity conditions. Based on Lyapunov stabilization theory and the linear matrix inequality (LMI) scheme, a new sufficient condition formulated in the form of LMIs is created for the chaos synchronization of chaotic systems with parametric uncertainties and external disturbances on the slave system. Using Barbalat's lemma, the suggested approach guarantees that the slave system synchronizes to the master system at an asymptotical convergence rate. Meanwhile, a criterion to find the proper feedback gain vector F is also provided. A new continuous‐bounded nonlinear function is introduced to cope with the disturbances and uncertainties and obtain a desired control performance, i.e. small steady‐state error and fast settling time. Several criteria are derived to guarantee the asymptotic and robust stability of the uncertain master–slave systems. Furthermore, the proposed controller is independent of the order of the system's model. Numerical simulation results are displayed with an expected satisfactory performance compared to the available methods.