Robust backstepping output tracking control for SISO uncertain nonlinear systems with unknown virtual control coefficients

The output tracking controller design problem is dealt with for a class of nonlinear strict-feedback form systems in the presence of nonlinear uncertainties, external disturbance, unmodelled dynamics and unknown time-varying virtual control coefficients. A new method based on signal compensation is proposed to design a linear time-invariant robust controller, which consists of a nominal controller and a robust compensator. It is shown that the closed-loop control system with a controller designed by the proposed method has robust asymptotical practical tracking property for any bounded initial conditions and robust tracking transient property if all initial states are zero.     

未知初始跟踪条件的非线性系统预设性能有限时间有界$H_infty$控制

研究一类具有外部扰动的非线性系统在初始跟踪条件未知情况下的预设性能有限时间有界$H_\infty$控制问题.针对预设性能控制设计,提出一个新的误差转换思想,并据此设计新的预设性能函数,解决预设性能控制依赖于系统被约束量初始条件的问题.基于所提出预设性能函数、有限时间控制理论以及有界$H_\infty$的设计方法,获得系统无需初始跟踪条件的预设性能有限时间有界$H_\infty$控制器,同时解决非线性系统在有界稳定情况下难以设计$H_\infty$控制器的问题,保证跟踪误差以预先设定的动态性能在有限时间内收敛至平衡点附近的小邻域内,并对外部干扰有较强的鲁棒性能.     

Adversarial Ground Target Tracking Using UAVs with Input Constraints

This paper deals with the problem of adversarial ground target tracking using Unmanned Aerial Vehicles (UAVs) subject to input constraints. For adversarial ground target tracking, tracking performance and UAV safety are two important considerations during tracking controller design. In this paper, a bang-bang heading rate controller is proposed to achieve circular tracking around the target. Exposure avoidance of the UAV to the target and minimizing the exposure time are studied respectively in terms of the initial state of the UAV. The performance of the proposed controller in both cases is also analyzed. Simulation results demonstrate the effectiveness of the proposed approach.     

带有误差修正项的自适应学习控制

本文讨论了二阶系统在任意初态偏差下的自适应控制问题,借助学习控制及其初始修正的思想,提出了两种带有修正初态偏差功能的自适应控制策略:一阶吸引子控制器和零阶吸引子控制器.两种控制器都是将整个控制过程分成若干个等长时间的子过程,在每个子过程中控制算法都会进行误差校正和参数学习.其中,一阶吸引子控制器在每个子过程中同时修正所...     

非参数不确定系统的有限时间迭代学习控制

针对任意初态情形,引入初始修正作用,研究一类非参数不确定时变系统能够达到实际完全跟踪性能的迭代学习控制方法. 采用Lyapunov-like综合,设计迭代学习控制器处理不确定性时变系统非参数化问题,其中含有有限时间控制作用,以实现在预先指定区间上的零误差跟踪. 并且,运用完全限幅学习机制,保证闭环系统中各变量的一致有界性以及跟踪误差的一致收敛性. 仿真结果表明了所提出控制方法的有效性.     

Fixed‐time attitude tracking control for spacecraft based on adding power integrator technique

In this article, the fixed‐time attitude tracking problem for rigid spacecraft is investigated based on the adding‐a‐power‐integrator control technique. First, a fixed‐time attitude tracking controller is designed to guarantee fixed‐time convergence of tracking errors. Then, by considering the presence of random disturbance and actuator faults, an adaptive fault‐tolerant attitude tracking controller is designed to guarantee tracking errors converge to a residual set of zero in a fixed time. The complete bounds on settling time are derived independently of initial conditions. The simulation results illustrate the highly precise and robust attitude control performance obtained by using the proposed controllers.     

An iterative learning controller with reduced sampling rate for plants with variations of initial states

In this paper a discrete-time iterative learning controller for single input single output systems is presented. The iterative learning controller works with a reduced sampling rate that ensures the reduction of an appropriate norm of the error trajectory from cycle to cycle and can cope with initial state error. Initial state error occurs when the initial state of the system is different from the initial state that is implicitly given by the reference trajectory. If the initial state changes for every learning iteration, then the controller cannot achieve ideal tracking but the error trajectory is bounded. Using two different sample times together with a potentially time variant learning gain improves the controller performance for dealing with initial state error. Simulation examples are presented to show the results of the proposed iterative learning controller with reduced sampling rate.     

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.     

Unified nonsingular tracking and stabilization controller design for unicycle-type wheeled mobile robots

A unified, singularity-avoidant controller which enables simultaneous trajectory tracking and posture stabilization of unicycle-type wheeled mobile robots is proposed. The design scheme is based on phase portrait analysis, dynamic feedback linearization and sliding mode control. Path planning via phase portrait analysis plays a key role in choosing the control parameters and the initial value of the extended state in avoiding any singularity. Simulation results on posture stabilization as well as an eight-shaped trajectory tracking are presented to demonstrate the performance of the proposed controller.     

Convergence and robustness of a discrete-time learning control scheme for constrained manipulators

The constrained motion control is one of the most common control tasks found in many industrial robot applications. The nonlinear and nonclassical nature of the dynamic model of constrained robots make designing a controller for accurate tracking of both motion and force a difficult problem. In this article, a discrete-time learning control problem for precise path tracking of motion and force for constrained robots is formulated and solved. The control system is able to reduce the tracking error iteratively in the presence of external disturbances and errors in initial condition as the robot repeats its action. Computer simulation result is presented to demonstrate the performance of the proposed learning controller. © 1994 John Wiley & Sons, Inc.     

Robust and perfect tracking of discrete-time systems

We study in this paper the problem of robust and perfect tracking for discrete-time linear multivariable systems. By robust and perfect tracking, we mean the ability of a controller to track a given reference signal with arbitrarily small settling time in the face of external disturbances and initial conditions. A set of necessary and sufficient conditions under which the proposed problem is solvable is obtained and, under these conditions, constructive algorithms are given that yield the required solutions. As is general to discrete-time systems, the solvability conditions to the above problem are quite restrictive. To relax these conditions, we propose an almost perfect tracking scheme, which is capable of tracking references precisely after certain initial steps.     

地月空间航天器绕飞接近跟踪控制

在圆形限制性三体问题的框架下研究了地月空间航天器绕飞接近跟踪控制方法.首先,以圆形限制性三体问题为基础建立了地月空间航天器相对运动模型,并通过无量纲化和线性化方法对模型进行了简化.接着,将地月空间航天器绕飞接近跟踪控制问题转化为模型参考输出跟踪问题,并结合模型参考输出跟踪理论和线性二次型最优控制理论设计绕飞接近跟踪控制器.最后,通过数学仿真验证控制器的有效性和可行性,仿真结果表明该方法能够实现地月空间航天器绕飞接近跟踪控制,对位置和速度的初始状态误差能够快速收敛.     

Fixed-time Consensus Tracking for Second-order Leader-follower Multi-agent Systems with Nonlinear Dynamics Under Directed Topology

In this paper, we address the fixed-time consensus tracking problem of second-order leader-follower multi-agent systems with nonlinear dynamics under directed topology. The consensus tracking algorithm consists of distributed observer and observer-based decentralized controller. The fixed-time distributed observer guarantees that each follower estimates the leader’s state under directed topology within a fixed time, where the upper bound of convergence time is independent on the initial conditions. The fixed-time decentralized controller makes each follower converge to the leader’s state in fixed time via tracking the distributed observer’s state and overcome the nonlinear dynamics without adding linear control terms. Finally, the numerical example is provided to illustrate the effectiveness of the results.

     

Robust Tracking Controller Design for a Class of Block Lower‐Triangular Systems

A class of multi‐input multi‐output (MIMO) block lower‐triangular systems are considered with the dynamic and static uncertainties related to the states of the former subsystems. A design procedure of robust tracking controller is presented, which avoids the problem of the “explosion of complexity” and can be implemented very simply. The closed‐loop system possesses robust properties that, 1) all signals involved are semi‐global uniformly ultimately bounded for bounded differentiable reference inputs; 2) for given initial tracking errors and uncertainty boundary, controller parameters can be found to guarantee that the tracking errors can be smaller than a specific positive constant after a limited time. Two numerical instances are given at last.     

Error-feedback servomechanism in non-linear systems

We consider the controller design of the error-feedback servomechanism in exponentially stable non-linear systems. Using the concepts of integral manifold and singular perturbation, a non-linear controller is constructed that ensures a desired steady state tracking rate for unknown constant set-points. The servo rate in the error-feedback servomechanism is shown to be governed by not only the plant characteristics but also the initial conditions of the plant and controller. A self-tuning scheme is proposed to speed up the servo process.     

IMC-based iterative learning control for batch processes with uncertain time delay

Based on the internal model control (IMC) structure, an iterative learning control (ILC) scheme is proposed for batch processes with model uncertainties including time delay mismatch. An important merit is that the IMC design for the initial run of the proposed control scheme is independent of the subsequent ILC for realization of perfect tracking. Sufficient conditions to guarantee the convergence of ILC are derived. To facilitate the controller design, a unified controller form is proposed for implementation of both IMC and ILC in the proposed control scheme. Robust tuning constraints of the unified controller are derived in terms of the process uncertainties described in a multiplicative form. To deal with process uncertainties, the unified controller can be monotonically tuned to meet the compromise between tracking performance and control system robust stability. Illustrative examples from the recent literature are performed to demonstrate the effectiveness and merits of the proposed control scheme.     

Vertical manoeuvring and rotor speed robust control for mini-helicopter

A robust control approach is presented for vertical manoeuvring helicopters with aerodynamics acting on the main rotor. An induced inflow observer is applied which only requires the measured values of the vertical accelerator in the body frame. The controller consists of three parts: a feedback linearisation controller, a nominal controller and a robust compensator. Feedback linearisation is applied to decouple the vertical motion dynamics and the rotor speed dynamics; the nominal controller aims to achieve desired performances for the nominal system without external disturbances or uncertainties; and the robust compensator is designed to restrain the effect of the external disturbance and the uncertainties. It is proven that all the states involved are bounded and the tracking error of the actual system can converge into an arbitrary specified boundary in a finite time for any given initial state. The results of outdoor flight experiments show that the robust tracking performances are consistent with the theory analysis conclusions.     

Position feedback global tracking control of EL systems: a statetransformation approach

The contribution of this paper is a dynamic position feedback global tracking controller for fully actuated Euler-Lagrange (EL) systems. The properties we show for the closed loop system are uniform stability and exponential convergence, global in the initial tracking errors and semiglobal in the initial estimation errors. The novelty of our approach is that our observer and control design are based on a new model for EL systems which is linear in the immeasurable velocities. This model is shown to fit robot manipulators. We also provide some simulation results     

Robust iterative learning control design is straightforward for uncertain LTI systems satisfying the robust performance condition

This note demonstrates that the design of a robust iterative learning control is straightforward for uncertain linear time-invariant systems satisfying the robust performance condition. It is shown that once a controller is designed to satisfy the well-known robust performance condition, a convergent updating rule involving the performance weighting function can be directly obtained. It is also shown that for a particular choice of this weighting function, one can achieve a perfect tracking. In the case where this choice is not allowable, a sufficient condition ensuring that the least upper bound of the /spl Lscr//sub 2/-norm of the final tracking error is less than the least upper bound of the /spl Lscr//sub 2/-norm of the initial tracking error is provided. This sufficient condition also guarantees that the infinity-norm of the final tracking error is less than the infinity-norm of the initial tracking error.     

基于模型预测—中枢模式发生器的六足机器人轨迹跟踪控制

为了模仿动物卓越的运动能力和环境适应能力,提出了六足仿生机器人的轨迹跟踪控制方法。首先建立了机器人的运动学模型,接着通过转向参数将机器人的速度和角速度与中枢模式发生器（CPG）参数结合起来,设计了转换函数。然后通过转换函数将模型预测控制器和CPG网络结合起来,提出了基于CPG的模型预测控制器（MPC-CPG）,并证明了其稳定性。最后对机器人跟踪圆周轨迹和直线轨迹进行了仿真和实验。实验表明,在有初始误差的条件下,机器人在MPC-CPG控制器的作用下能够快速地消除位置误差和航向角误差,跟踪上参考轨迹。轨迹跟踪的位置误差始终保持在-0.1～0.1 m,航向角误差保持在-27?～20?。在MPC-CPG控制器的作用下,机器人不仅具有较高的轨迹跟踪精度,同时还表现出良好的运动平滑性和协调性,进一步验证了所提出的MPC-CPG控制器的有效性。