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.     

考虑执行器性能约束的刚体航天器鲁棒姿态跟踪控制

针对存在模型不确定性、外界干扰力矩和执行器性能受限等约束条件下的刚体航天器姿态跟踪控制问题进行研究,并基于滑模控制、反步控制、自适应控制、辅助系统和动态面控制等方法设计相应的鲁棒姿态跟踪控制算法.利用自适应控制实现了对具有多项式形式上界函数的系统未知不确定性进行在线估计和补偿;通过建立描述执行器动态特性的低通滤波模型,并结合辅助系统方法,以确保执行器输出控制力矩的幅值及其变化率均满足一定的饱和约束;通过引入动态面控制法,避免期望虚拟控制信号的一阶导数项直接出现在控制器中,简化了闭环姿态跟踪控制器的设计形式.最后,通过数值仿真验证了所提出控制算法的有效性和可行性.     

机器人系统的加权快速终端滑模主动容错控制

针对受执行器故障的非线性机器人系统,提出一种加权快速终端滑模主动容错控制方法。首先利用观测器估计机器人系统中的执行器故障信息,并通过自适应律对故障未知的界进行估计。然后根据机器人各关节的加权位置误差进一步设计快速终端滑模控制器对获得的故障信息做出补偿,从而实现有限时间主动容错控制。通过李雅普诺夫函数法证明了闭环系统的稳定性,并采用两关节机器人验证了方案的有效性。该方案可以通过对不同关节位置误差权重值的分配来相应地补偿故障的影响,能够在有限时间内使得机器人位置跟踪误差快速收敛且跟踪精度得到提高。     

力矩输入有界的柔性关节机器人轨迹跟踪控制

为解决柔性关节机器人在关节驱动力矩输出受限情况下的轨迹跟踪控制问题,提出一种基于奇异摄动理论的有界控制器.首先,利用奇异摄动理论将柔性关节机器人动力学模型解耦成快、慢两个子系统.然后,引入一类平滑饱和函数和径向基函数神经网络非线性逼近手段,依据反步策略设计了针对慢子系统的有界控制器.在快子系统的有界控制器设计中,通过关节弹性力矩跟踪误差的滤波处理加速系统的收敛.同时,在快、慢子系统控制器中均采用模糊逻辑实现控制参数的在线动态自调整.此外,结合李雅普诺夫稳定理论给出了严格的系统稳定性证明.最后,通过仿真对比实验验证了所提出控制方法的有效性和优越性.     

飞翼飞行器的操纵面故障自适应补偿控制

本文针对具有操纵面卡死、失效故障以及执行器饱和的飞翼飞行器纵向运动,考虑系统的预定动态性能,提出了一种自适应反步补偿跟踪控制方案.设计预定动态性能(prescribed performance bound,PPB)边界以保证系统的跟踪误差,采用二阶指令滤波器限制执行器的饱和,通过控制分配避免执行器故障后对横侧向运动的影响.所设计的自适应反步补偿跟踪控制律能够保证系统对参考信号的渐近跟踪.仿真结果表明了本文方法的有效性.     

液压伺服关节自适应模糊神经网络控制补偿方法

三自由度液压伺服关节在实现位置跟踪时存在跟踪误差,原因在于液压伺服关节是一个具有饱和、结构死区和强耦合的动力学系统.为了解决这一问题,建立了该关节的动力学模型.通过比较几种控制方法在该关节位置跟踪问题上存在的不足,提出了一种自适应模糊神经网络控制补偿方法.该方法采用样本训练自学习,自适应调整变增益系数.该方法不但消除了饱和、结构死区和强耦合引起的位置跟踪误差,而且解决了控制向量在大范围内变化实现准确位置跟踪.最后,通过仿真试验验证了该动力学系统是稳定的,提出的方法是可行的.     

永磁同步电动机的自适应神经网络动态面控制

研究永磁同步电动机的位置跟踪控制问题.针对参数不确定的永磁同步电动机系统,提出自适应神经网络动态面位置跟踪控制方法.根据Stone Weierstrass逼近定理,利用神经网络逼近电动机系统中的复杂非线性函数.采用动态面技术的自适应反步方法设计电动机的位置跟踪控制器实现电动机的位置跟踪控制.提出的控制策略不仅能够克服电机参数的不确定性和负载扰动,而且避免了传统反步设计方法存在的“复杂性爆炸”问题.根据Lyapunov稳定性理论,证明闭环系统具有半全局稳定性,位置跟踪误差收敛于原点的小邻域内.仿真结果表明了所提控制方法能够使电动机快速、准确地跟踪给定的位置信号;神经网络能够很好地逼近系统中的复杂非线性函数.     

考虑状态约束与执行器饱和的水下机器人轨迹跟踪控制

针对执行器饱和、模型参数不确定以及海流干扰等因素影响下的水下机器人,提出一种考虑状态约束以及执行器饱和的轨迹跟踪控制器.首先,构建水下机器人水平面轨迹跟踪误差方程;然后,对载体模型参数不确定性产生的模型误差以及海流干扰,设计一个非线性观测器进行估计并用于对控制器进行扰动补偿;接着,引入执行器饱和补偿系统、二阶滤波器以及滤波器误差补偿系统,设计命令滤波反步滑模控制器来控制水下机器人的水平面轨迹跟踪;最后,严格验证命令滤波反步滑模控制器的稳定性并进行数值仿真,验证所提出控制器的有效性.     

侧滑打滑时的WMR抗饱和模糊超螺旋滑模控制

针对侧滑打滑干扰和执行器饱和约束导致轮式移动机器人(WMR)轨迹跟踪困难的问题,提出一种抗饱和模糊超螺旋滑模控制策略.根据WMR的轨迹跟踪误差模型,设计运动学控制器,得到辅助速度控制律;采用反步法思想,设计抗饱和模糊超螺旋滑模动力学控制器,通过饱和约束辅助系统实现抗饱和控制,并引入模糊自适应律,动态调整超螺旋控制律参数...     

输入受限四旋翼飞行器的模糊自适应动态面轨迹跟踪控制

针对输入受限条件下四旋翼飞行器的轨迹跟踪控制问题,考虑系统存在模型动态不确定和未知外界干扰的情况,提出一种模糊自适应动态面轨迹跟踪控制方法.该方法设计干扰观测器估计位置模型中复合扰动项,利用模糊系统逼近姿态模型中不确定项和外界干扰,并引入双曲正切函数和辅助系统处理输入受限问题,结合反演法和动态面技术设计轨迹跟踪控制器,以降低控制算法的复杂性,最后选取李雅普诺夫函数证明闭环系统所有信号一致最终有界.应用大疆M100飞行器模型进行仿真验证,结果表明所设计的控制器能够有效处理模型动态不确定和未知外界干扰问题,避免飞行器工作过程中因输入饱和导致执行器失效现象,精确地完成轨迹跟踪控制任务.     

A robust constrained control approach for flexible air‐breathing hypersonic vehicles

This article investigates the robust adaptive control system design for the longitudinal dynamics of a flexible air‐breathing hypersonic vehicle (FAHV) subject to parametric uncertainties and control input constraints. A combination of back‐stepping and nonlinear disturbance observer (NDO) is utilized for exploiting an adaptive output‐feedback controller to provide robust tracking of velocity and altitude reference trajectories in the presence of flexible effects and system uncertainties. The dynamic surface control is introduced to solve the problem of “explosion of terms.” A new NDO is developed to guarantee the proposed controller's disturbance attenuation ability and to performance robustness against uncertain aerodynamic coefficients. To deal with the problem of actuator saturation, a novel auxiliary system is exploited to compensate the desired control laws. The stability of the presented NDO and controller is analyzed. Simulation results are given to demonstrate the effectiveness of the presented control strategy.     

Output Constrained Adaptive Controller Design for Nonlinear Saturation Systems

This paper considers the adaptive neuro-fuzzy control scheme to solve the output tracking problem for a class of strict-feedback nonlinear systems.Both asymmetric output constraints and input saturation are considered.An asymmetric barrier Lyapunov function with time-varying prescribed performance is presented to tackle the output-tracking error constraints.A high-gain observer is employed to relax the requirement of the Lipschitz continuity about the nonlinear dynamics.To avoid the"explosion of complexity",the dynamic surface control(DSC)technique is employed to filter the virtual control signal of each subsystem.To deal with the actuator saturation,an additional auxiliary dynamical system is designed.It is theoretically investigated that the parameter estimation and output tracking error are semi-global uniformly ultimately bounded.Two simulation examples are conducted to verify the presented adaptive fuzzy controller design.     

Decentralized adaptive neural control of nonlinear interconnected large-scale systems with unknown time delays and input saturation

In this paper, a novel decentralized adaptive neural control scheme is proposed for a class of interconnected large-scale uncertain nonlinear time-delay systems with input saturation. RBF neural networks (NNs) are used to tackle unknown nonlinear functions, then the decentralized adaptive NN tracking controller is constructed by combining Lyapunov–Krasovskii functions and the dynamic surface control (DSC) technique along with the minimal-learning-parameters (MLP) algorithm. The stability analysis subject to the effect of input saturation constrains are conducted with the help of an auxiliary design system based on the Lyapunov–Krasovskii method. The proposed controller guarantees uniform ultimate boundedness (UUB) of all the signals in the closed-loop large-scale system, while the tracking errors converge to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters for each subsystem is reduced to one, and three problems of “computational explosion”, “dimension curse” and “controller singularity” are solved, respectively. Finally, a numerical simulation is presented to demonstrate the effectiveness and performance of the proposed scheme.     

A new approach to robust position/force control of flexible-joint robot manipulators

In this paper a new approach employing smooth robust compensators is proposed for the control of uncertain elastic-joint robot manipulators during contact tasks. It is assumed that the flexible-joint manipulators consist of two subsystems: the rigid subsystem and the flexible subsystem. The output of the flexible subsystem is assumed to be the input of the rigid subsystem. The control design is carried out in two steps. First, a desired input is designed for the rigid subsystem, which can robustly stabilize it. Second, a robust controller is designed to stabilize the flexible subsystem so that it generates the necessary torque designed for the rigid subsystem. By using this approach, the robot manipulator can exert a preset amount of force on the environment while tracking a desired trajectory with global asymptotic stability. Lyapunov's direct method is used here to prove the global asymptotic stability of the closed-loop system. The assumption of weak joint elasticity is relaxed and exact knowledge of joint stiffness is not required for the control design. Also, exact knowledge of robot kinematic and dynamic parameters and actuator parameters are not required. Unlike other approaches, this approach takes the environmental stick-slip friction as well as its dependency on normal contact force into consideration. It compensates for the adverse effects of the stick-slip friction. The proposed controller produces a smooth control action, and ensures smooth motion on the contact surface. The efficacy of the proposed controller is illustrated with the help of a numerical example of a two-link flexible-joint robot. © 1996 John Wiley & Sons, Inc.     

动力定位船轨迹跟踪鲁棒自适应容错控制

本文研究了输入饱和状态下的动力定位船故障容错鲁棒自适应控制问题.该问题以动力定位船轨迹跟踪任务为目标,提出了一种新颖的鲁棒自适应控制器的设计,并且引入了二阶快速非奇异终端滑模和神经网络控制算法保证了控制器在实际任务中的执行效果.首先,介绍了三自由度动力定位船的运动模型包括了运动学模型和动力学模型以及推进器故障模型.然后,设计了二阶快速非奇异终端滑模面,提出了一种针对时变扰动和模型不确定性的鲁棒控制方案,保证系统无抖振现象的前提下实现了系统更快的收敛速度.同时运用被动容错控制思想,确保动力定位船在推进器故障发生时依然能够实现预计的跟踪性能.此外,通过Lyapunov稳定性判据分析,证明了提出的改进自适应滑模控制方法可确保系统在初始状态未知前提下,跟踪误差渐近收敛于零.最后,通过数值仿真实验结果验证了控制律的有效性.     

Active vibration control of large space flexible slewing truss using cable actuator with input saturation

This paper proposes a composite approach to implementing attitude tracking and active vibration control of a large space flexible truss system. The system dynamic model is based on Hamilton's principle and discretized using the finite difference method. A nonlinear attitude controller for position tracking is developed based on the input‐output linearization of the discretized system, which can effectively improve system performance compared with a traditional proportional‐differential feedback controller. A taut cable actuator scheme is presented to suppress tip vibration because the mechanical model is a large large‐span spatial structure; furthermore, because the cable has the feature of unilateral input saturation constraint, which can provide only a pulling force, a nonlinear quadratic regulator controller is developed by introducing a piecewise nonquadratic cost function to suppress the vibration of the flexible structure. To investigate the factors that influence the damping effects of the cable, the parametrically excited instability of a cable under 2 supports is analyzed. Simulation results illustrate that the proposed attitude controller can implement the task of position tracking, and the vibration suppression control law is shown to be optimal for functional performance with input saturation.     

Adaptive Decentralized NN Control of Nonlinear Interconnected Time‐Delay Systems with Input Saturation

In this paper, a novel decentralized adaptive neural control scheme is proposed for a class of interconnected large‐scale uncertain nonlinear time‐delay systems with input saturation. Radial basis function (RBF) neural networks (NNs) are used to tackle unknown nonlinear functions. Then, the decentralized adaptive NN tracking controller is constructed by combining Lyapunov–Krasovskii functions and the dynamic surface control (DSC) technique, along with the minimal‐learning‐parameters (MLP) algorithm. The stability analysis subject to the effect of input saturation constraints are conducted with the help of an auxiliary design system based on the Lyapunov–Krasovskii method. The proposed controller guarantees uniform ultimate boundedness (UUB) of all of the signals in the closed‐loop large‐scale system, while the tracking errors converge to a small neighborhood around the origin. An advantage of the proposed control scheme lies in the number of adaptive parameters of the whole system being reduced to one and in the solution of the three problems of “computational explosion,” “dimension curse,” and “controller singularity”. Finally, simulation results along with comparisons are presented to demonstrate the advantages, effectiveness, and performance of the proposed scheme.     

Robust Finite‐Time Stability Control of a Class of High‐Order Uncertain Nonlinear Systems

In this study, a novel robust finite‐time stability controller is proposed for a class of high‐order uncertain nonlinear systems. It uses the dynamic surface control (DSC) approach to simplify the traditional backstepping design for high‐order nonlinear systems, thus avoiding the “explosion of terms”. The finite‐time stability of the closed‐loop system is guaranteed to have high performance, such as fast transient and strong robustness to dynamic uncertainties, and the tracking error is made arbitrarily small. Simulation results of two examples indicate that the proposed controller is effective.     

高速列车非线性系统的分数阶有限时间控制器设计

针对具有输入非线性, 不确定的气动阻力, 未知的车间力, 外部扰动以及未知的执行器故障等特征的高速列车非线性系统, 结合分数阶稳定性原理以及有限时间控制理论, 本文设计了一种分数阶有限时间控制器以实现高速列车更快速且更高精度的跟踪控制. 该控制器能够直接补偿高速列车的不确定性和非线性以及执行器故障而不需任何“试错”过程, 且稳定时间可由控制参数的不同选择来调整. 仿真研究验证了所设计控制器的有效性和优越性.