一类受限机器人的学习控制

本文给出了一类受限机器人的一个学习控制方案，学习控制器的设计是基于受限机器人的奇异模型，在存在未知的有界干扰的情况下，对末端操纵器受线性、无摩擦约束面约束的机器人，本文给出的控制方案实现了机器人运动的完全跟踪，保证了力跟踪误差是有界的，并且界的大小是可调节的。     

受限柔性机器人臂的鲁棒变结构混合位置/力控制

针对一类平面双连杆受限柔性机器人臂提出一种混合位置/力控制方案,采用鲁棒变 结构控制策略对控制方案进行修正,以改善该柔性机器人系统的鲁棒性,控制机器人终端执行 器的位置和接触力.通过引入变结构鲁棒控制器,可确保输出跟踪误差在有限时间内收敛到零, 或一致终结有界.计算机仿真结果证明了这种控制方案的可行性和有效性.     

存在环境约束的机器人自适应迭代学习控制

针对存在不确定性和外界干扰的受限机器人系统提出一种自适应迭代学习控制律.不确定性参数被估计在时间域内,同时重复性外界干扰在迭代域内得到补偿.通过引入饱和学习函数,保证了闭环系统所有信号有界.借助Lyapunov复合能量函数法,证明了系统渐进收敛到期望轨迹的同时,能够保证力跟踪误差有界可调.     

基于观测器的机器人鲁棒跟踪控制

基于滑模控制原理设计稳定的状态观测器，在此基础上。研究了仅有关节角位移可测量的机器人轨迹跟踪控制问题。运用反推设计方法提出一种机器人鲁棒跟踪控制新方案。通过引入用于抑制观测误差的阻尼项和调制参数。加快了输出跟踪误差的收敛速度。利用李亚普诺夫方法分析了闭环控制系统的全局稳定性，证明了系统所有状态信号有界，跟踪误差收敛到零。仿真结果进一步表明，提出的控制方案具有较强的鲁棒性和良好的跟踪性能。     

受扰不确定机器人手臂的终结有界稳定控制

本文研究了在扰动和不确定因素共存的情况下,机器人手臂系统的稳定控制问题,给出了保证其终结有界稳定的控制方案。该方案由一个力矩发生器和终结有界镇定器组成,它不依赖精确的机器人手臂模型,且能容忍一定程度的计算误差,这对于简化运算,提高控制计算机实时处理速度十分有益。     

水下机器人的神经网络自适应控制

研究了水下机器人神经网络直接自适应控制方法,采用Lyapunov稳定性理论,证明了存在有界外界干扰和有界神经网络逼近误差条件下,水下机器人控制系统的跟踪误差一致稳定有界.为了进一步验证该水控制方法的正确性和稳定性,利用水下机器人实验平台进行了动力定位实验、单自由度跟踪实验和水平面跟踪实验等验证实验.     

不依赖模型的机器人鲁棒自适应跟踪控制

提出了一种新颖的鲁棒自适应控制策略，用于不确定性机器人的轨迹跟踪。它不需要任何模型知识，唯一需了解的是系统的阶数和输出的位置及速度状态。理论和仿真均证明，系统的不确定性诸如摩擦力、外部扰动及未建模动力学带来的不确定性影响，均可被设计的控制律补偿，最后可保证全局指数收敛或全局一致最后有界的结果。另外，本文还给出了跟踪误差的暂态测量。     

执行器受限机器人的鲁棒输出调节

针对实际的控制系统中执行器受限的问题，研究了机器人存在不确定结构参数及外界干扰时的输出调节问题。基于李雅普诺夫稳定性理论，通过在构造存储函数的过程中引入双曲正切函数和适当的辅助函数，设计了一种有界的鲁棒控制器。所提出的控制器不保证了闭环系统的鲁棒稳定性，同时也达到了从干扰信号到跟踪误差评价信号所定义的L2增益性能指标γ，即保证了干扰抑制的有效性。最后，由两连杆机器人进行的仿真结果验证了该控制器的可行性。     

不依赖模型的机器人鲁棒自适应跟踪控制(英)

提出了一种新颖的鲁棒自适应控制策略，用于不确定性机器人的轨迹跟踪．它不需要任何模型知识，唯一需了解的是系统的阶数和输出的位置及速度状态。理论和仿真均证明，系统的不确定性诸如摩擦力、外部扰动及未建模动力学带来的不确定性影响，均可被设计的控制律补偿，最后可保证全局指数收敛或全局一致最后有界的结果．另外，本文还给出了跟踪误差的暂态测量．     

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.     

Cooperative control for consensus of multi-agent systems with actuator faults

Consensus tracking problem in multi-agent systems with both outage and partial loss of effectiveness types of actuator faults is investigated in this paper. By adopting the virtual actuator technique based on the obtained fault estimates, the effects of possible actuator faults can be effectively compensated in each individual agent. Based on this, a distributed control strategy is developed by using locally available information. It is shown that the tracking error of each fault-free agent can converge to zero and the tracking errors of the faulty agents can remain bounded if the fault estimates are accurate. Moreover, a sufficient condition for achieving bounded tracking errors for all the followers is derived in terms of the fault estimation error. The sliding mode observer is utilized to estimate the faults. Simulation results are given to show the effectiveness of the proposed approach.     

Adaptive λ-tracking for a class of infinite-dimensional systems

For a class of high-gain stabilizable multivariable linear infinite-dimensional systems we present an adaptive control law which achieves approximate asymptotic tracking in the sense that the tracking error tends asymptotically to a ball centred at 0 and of arbitrary prescribed radius λ>0. This control strategy, called λ-tracking, combines proportional error feedback with a simple nonlinear adaptation of the feedback gain. It does not involve any parameter estimation algorithms, nor is it based on the internal model principle. The class of reference signals is W^1,∞, the Sobolev space of absolutely continuous functions which are bounded and have essentially bounded derivative. The control strategy is robust with respect to output measurement noise in W^1,∞ and bounded input disturbances. We apply our results to retarded systems and integrodifferential systems.     

Adaptive Iterative Learning Control of Non-uniform Trajectory Tracking for Strict Feedback Nonlinear Time-varying Systems

In this paper, an iterative learning control strategy is presented for a class of nonlinear time-varying systems, the timevarying parameters are expanded into Fourier series with bounded remainder term. The backstepping design technique is used to deal with system dynamics with non-global Lipschitz nonlinearities and the approach proposed in this paper solves the non-uniform trajectory tracking problem. Based on the Lyapunov-like synthesis, the proposed method shows that all signals in the closed-loop system remain bounded over a pre-specified time interval [0, T ]. And perfect non-uniform trajectory tracking of the system output is completed. A typical series is introduced in order to deal with the unknown bound of remainder term. Finally, a simulation example shows the feasibility and effectiveness of the approach.     

Flat trajectory design and tracking with saturation guarantees: a nano-drone application

Abstract

This paper deals with the problem of trajectory planning and tracking of a quadcopter system based on the property of differential flatness. First, B-spline characterisations of the flat output allow for optimal trajectory generation subject to waypoint constraints, thrust and angle constraints while minimising the trajectory length. Second, the proposed tracking control strategy combines feedback linearisation and nested saturation control via flatness. The control strategy provides bounded inputs (thrust, roll and pitch angles) while ensuring the overall stability of the tracking error dynamics. The control parameters are chosen based on the information of the a priori given reference trajectory. Moreover, conditions for the existence of these parameters are presented. The effectiveness of the trajectory planning and the tracking control design is analysed and validated through simulation and experimental results over a real nano-quadcopter platform, the Crazyflie 2.0.     

Robust output tracking for non-linear systems

The problem of output tracking for a single-input single-output non-linear system in the presence of uncertainties is studied. The notions relative degree and minimum-phase for non-linear systems are reviewed. Given a bounded desired tracking signal with bounded derivatives, a control law is designed for minimum-phase non-linear systems which results in tracking of this signal by the output. This control law is modified in the presence of uncertainties associated with the model vector fields to reduce the effects of these uncertainties on the tracking errors. Two types of uncertainties are considered: those satisfying a generalized matching condition but otherwise unstructured, and linear parametric uncertainties. It is shown that for systems with the first type of uncertainty, high-gain control laws can result in small tracking errors of O(?), where e is a small design parameter. An alternative scheme based on variable structure control strategy is shown to yield zero tracking errors. Adaptive control techniques are used for systems with linear parametric uncertainties. For systems with relative degree larger than one, a new adaptive control scheme is presented which is considerably simpler than the augmented error scheme suggested previously by Narendra et al. (1978) for linear systems and by Sastry and Isidori (1987) for non-linear systems. Contrary to the augmented error scheme, however, this scheme results in small rather than zero tracking errors.     

Fixed-time coordinated tracking for second-order multi-agent systems with bounded input uncertainties

In this paper, we study the fixed-time coordinated tracking problem for second-order integrator systems with bounded input uncertainties. Two novel distributed controllers are proposed with which the convergence time of the tracking errors is globally bounded for any initial condition of the agents. When relative state measurements are available for each follower, an observer-based distributed control strategy is proposed which achieves fixed-time coordinated tracking for the perturbed second-order multi-agent systems. When only relative output measurements are available, uniform robust exact differentiators are employed together with the observer-based controller which is able to achieve fixed-time coordinated tracking with reduced measurements. Simulation examples are provided to demonstrate the performance of the proposed controllers.     

Active fault tolerant control for nonlinear systems with simultaneous actuator and sensor faults

The goal of this paper is to describe a novel fault tolerant tracking control (FTTC) strategy based on robust fault estimation and compensation of simultaneous actuator and sensor faults. Within the framework of fault tolerant control (FTC) the challenge is to develop an FTTC design strategy for nonlinear systems to tolerate simultaneous actuator and sensor faults that have bounded first time derivatives. The main contribution of this paper is the proposal of a new architecture based on a combination of actuator and sensor Takagi-Sugeno (T-S) proportional state estimators augmented with proportional and integral feedback (PPI) fault estimators together with a T-S dynamic output feedback control (TSDOFC) capable of time-varying reference tracking. Within this architecture the design freedom for each of the T-S estimators and the control system are available separately with an important consequence on robust L ₂ norm fault estimation and robust L ₂ norm closed-loop tracking performance. The FTTC strategy is illustrated using a nonlinear inverted pendulum example with time-varying tracking of a moving linear position reference.     

Distributed adaptive output feedback tracking control for a class of uncertain nonlinear multi-agent systems

This paper addresses the distributed output feedback tracking control problem for multi-agent systems with higher order nonlinear non-strict-feedback dynamics and directed communication graphs. The existing works usually design a distributed consensus controller using all the states of each agent, which are often immeasurable, especially in nonlinear systems. In this paper, based only on the relative output between itself and its neighbours, a distributed adaptive consensus control law is proposed for each agent using the backstepping technique and approximation technique of Fourier series (FS) to solve the output feedback tracking control problem of multi-agent systems. The FS structure is taken not only for tracking the unknown nonlinear dynamics but also the unknown derivatives of virtual controllers in the controller design procedure, which can therefore prevent virtual controllers from containing uncertain terms. The projection algorithm is applied to ensure that the estimated parameters remain in some known bounded sets. Lyapunov stability analysis shows that the proposed control law can guarantee that the output of each agent synchronises to the leader with bounded residual errors and that all the signals in the closed-loop system are uniformly ultimately bounded. Simulation results have verified the performance and feasibility of the proposed distributed adaptive control strategy.     

Iterative Learning Control for Nonlinear Systems: A Bounded‐Error Algorithm

This paper presents a nonlinear iterative learning control (NILC) for nonlinear time‐varying systems. An algorithm of a new strategy for the NILC implementation is proposed. This algorithm ensures that trajectory‐tracking errors of the proposed NILC, when implemented, are bounded by a given error norm bound. A special feature of the algorithm is that the trial‐time interval is finite but not fixed as it is for the other iterative learning algorithms. A sufficient condition for convergence and robustness of the bounded‐error learning procedure is derived. With respect to the bounded‐error and standard learning processes applied to a virtual robot, simulation results are presented in order to verify maximal tracking errors, convergence and applicability of the proposed learning control.     

Design and Implementation of an Inverse Dynamics Controller for Uncertain Nonholonomic Robotic Systems

This paper addresses the trajectory tracking control problem of nonholonomic robotic systems in the presence of modeling uncertainties. A tracking controller is proposed such that it combines the inverse dynamics control technique and an adaptive robust PID control strategy to preserve robustness to both parametric and nonparametric uncertainties. A SPR-Lypunov stability analysis demonstrates that tracking errors are uniformly ultimately bounded (UUB) and exponentially converge to a small ball containing the origin. The proposed inverse dynamics tracking controller is successfully applied to a nonholonomic wheeled mobile robot (WMR) and experimental results are presented to validate the effectiveness of the proposed controller.