Adaptive robust control of nonholonomic systems with stochastic disturbances

Based on the Brockett’s necessary condition for feedback asymptotic stabilization[1], nonholonomic systems fail to be stabilized at the origin by any static continuous state feedback though they are open loop controllable. There are two novel approaches …     

Adaptive model following control of nonlinear robotic systems

An adaptive model following control law for nonlinear robotic systems with rotational joints is presented. The derivation of the controller does not require any knowledge of nonlinear system matrics and the uncertainty in the system. In the closed-loop system the joint angles asymptotically converge to the reference trajectories.     

虚拟控制系数未知的非完整系统的自适应神经网络控制

针对一类虚拟控制系数未知的多输入链式非完整控制系统,提出了一种自适应神经网络控制策略.在控制策略的设计中,采用了State-scaling与Backstepping技术相结合的方法.Nussbaum-type增益技术用来解决系统的控制方向完全未知的问题.所提出的自适应神经网络控制策略解决了由复杂系统所引起的奇异问题,并通过选择适当的控制参数,使闭环系统半全局一致有界,且系统的状态渐近收敛到包含原点的任意小的一个收敛域.一种基于切换策略的自适应控制方法解决了当x0(t0)=0时所引起的系统不可控问题.仿真结果验证了算法的有效性.     

Adaptive output feedback control for nonholonomic systems with uncertain chained form

The output feedback adaptive control problem is investigated for nonholonomic systems with strongly nonlinear uncertainties and unknown virtual control directions. A nonlinear output feedback switching controller based on the output measurement of the first subsystem is employed in order to make the state scaling effective and ensure the convergence of the system states. The novel observer/estimator is introduced for state and unknown parameter estimates. The integrator backstepping technique by the use of a constructive recursive is applied to the design of the adaptive controller and to overcome the unknown virtual control directions. The simulation result validates the effectiveness of the proposed scheme.     

不确定性机器人系统自适应鲁棒迭代学习控制

利用Lyapunov方法, 提出了一种不确定性机器人系统的自适应鲁棒迭代学习控制策略, 整个系统在迭代域里是全局渐近稳定的. 所考虑的机器人系统同时包含了结构和非结构不确定性. 在设计时, 系统的不确定性被分解成可重复性和非重复性两部分, 并考虑了系统的标称模型. 在所提出的控制策略中, 自适应策略用来估算做法确定性的界, 界的修正与迭代学习控制量一样的迭代域得以实现的. 计算机仿真表明本文提出的控制策略是有效的.     

Adaptive NN-based finite-time trajectory tracking control of wheeled robotic systems

Neural Computing and Applications - The trajectory tracking and finite-time control problems of wheeled robotic systems with nonlinear dynamics and uncertainties are investigated in this paper. An...     

Discontinuous control of nonholonomic systems

The problem of asymptotic convergence for a class of nonholonomic control systems via discontinuous control is addressed and solved from a new point of view. It is shown that control laws, which ensures asymptotic (exponential) convergence of the closed-loop system, can be easily designed if the system is described in proper coordinates. In such coordinates, the system is discontinuous. Hence, the problem of local asymptotic stabilization for a class of discontinuous nonholonomic control systems is dealt with and a general procedure to transform a continuous system into a discontinuous one is presented. Moreover, a general strategy to design discontinuous control laws, yielding asymptotic convergence, for a class of nonholonomic control systems is proposed. The enclosed simulation results show the effectiveness of the method.     

Adaptive control of nonlinear systems: A case study of underwater robotic systems

The problem of controlling underwater mobile robots in 6 degrees of freedom (DOF) is addressed. Underwater mobile robots where the number of thrusters and control surfaces exceeds the number of controllable DOF are considered in detail. Unlike robotic manipulators underwater mobile robots should include a velocity dependent thruster configuration matrix B( q ), which modifies the standard manipulator equation to: Mq + C( q ) q + g(x) = B( q ) u where x = J( x ) q . Uncertainties in the thruster configuration matrix due to unmodeled nonlinearities and partly known thruster characteristics are modeled as multiplicative input uncertainty. This article proposes two methods to compensate for the model uncertainties: (1) an adaptive passivity-based control scheme and (2) deriving a hybrid (adaptive and sliding) controller. The hybrid controller combines the adaptive scheme where M, C, and g are estimated on-line with a switching term added to the controller to compensate for uncertainties in the input matrix B. Global stability is ensured by applying Barbalat's Lyapunov-like lemma. The hybrid controller is simulated for the horizontal motion of the Norwegian Experimental Remotely Operated Vehicle (NEROV).     

Adaptive sliding mode control of nonlinear robotic systems with time-varying parameters

In this paper an adaptive sliding mode control scheme is presented for nonlinear robotic systems with bounded time-varying parameters. The control scheme developed is very simple and computationally efficient since it does not require a knowledge of either The mathematical model or the parameter values of the robotic dynamics. It is shown that the controller is globally stable in the presence of a class of state-dependent uncertainties and that the size of the tracking error can be made arbitrarily small.     

Adaptive robust stabilization of dynamic nonholonomic chained systems

In this article, the stabilization problem is investigated for dynamic nonholonomic systems with unknown inertia parameters and disturbances. First, to facilitate control system design, the nonholonomic kinematic subsystem is transformed into a skew‐symmetric form and the properties of the overall systems are discussed. Then, a robust adaptive controller is presented in which adaptive control techniques are used to compensate for the parametric uncertainties and sliding mode control is used to suppress the bounded disturbances. The controller guarantees the outputs of the dynamic subsystem (the inputs to the kinematic subsystem) to track some bounded auxiliary signals which subsequently drive the kinematic subsystem to the origin. In addition, it can also be shown all the signals in the closed loop are bounded. Simulation studies on the control of a unicycle wheeled mobile robot are used to show the effectiveness of the proposed scheme. © 2001 John Wiley & Sons, Inc.     

Adaptive fuzzy switched control design for uncertain nonholonomic systems with input nonsmooth constraint

In this paper, a fuzzy adaptive switched control approach is proposed for a class of uncertain nonholonomic chained systems with input nonsmooth constraint. In the control design, an auxiliary dynamic system is designed to address the input nonsmooth constraint, and an adaptive switched control strategy is constructed to overcome the uncontrollability problem associated with x₀(t₀) = 0. By using fuzzy logic systems to tackle unknown nonlinear functions, a fuzzy adaptive control approach is explored based on the adaptive backstepping technique. By constructing the combination approximation technique and using Young's inequality scaling technique, the number of the online learning parameters is reduced to n and the ‘explosion of complexity’ problem is avoid. It is proved that the proposed method can guarantee that all variables of the closed-loop system converge to a small neighbourhood of zero. Two simulation examples are provided to illustrate the effectiveness of the proposed control approach.     

Adaptive tracking control of uncertain nonholonomic dynamic system

This note considers the tracking problem of nonholonomic dynamic systems with unknown inertia parameters. A new controller is proposed relying on newly defined tracking errors and the passivity property of the nonholonomic dynamic system. The proposed controller ensures that the entire state of the system asymptotically tracks the desired trajectory. Simulation results show effectiveness of the proposed controller     

随机非完整链式系统的自适应状态反馈镇定

基于反步技术,对带有不确定非线性项和不确定非线性系数的随机非完整链式系统,设计了自适应状态反馈镇定器.给出了能够保证系统依概率几乎渐近稳定到平衡点的切换控制.最后用仿真验证了控制器的有效性.     

Adaptive stabilization of uncertain nonholonomic systems by output feedback

The adaptive output feedback control strategy is presented for a class of nonholonomic systems in chained form with nonlinearity uncertainties. A new observer and a filter are introduced for the states and parameter estimation. The proposed control strategies guarantee the convergence of the closed-loop system. The simulation example demonstrates the efficiency of the proposed method.     

不确定非完整动力学系统的自适应模糊滑模控制器

为研究不确定非完整动力学系统的收敛性, 根据速度跟踪原理, 对其提出一种新的自适应模糊滑模控制器. 该控制器在存在的参数不确定性与外界干扰的情况下, 能使系统的广义坐标与广义速度收敛到预先给定的界内. 仿真算例验证了所提出方法的有效性.     

Adaptive observer-based trajectory tracking control of nonholonomic mobile robots

In this paper, an adaptive observer-based trajectory tracking problem is solved for nonholonomic mobile robots with uncertainties. An adaptive observer is first developed to estimate the unmeasured velocities of a mobile robot with model uncertainties. Using the designed observer and the backstepping technique, a trajectory tracking controller is designed to generate the torque as an input. Using Lyapunov stability analysis, we prove that the closed-loop system is asymptotically stable with respect to the estimation errors and tracking errors. Finally, the simulation results are presented to validate the performance and robustness of the proposed control system against uncertainties.     

Adaptive global stabilization of nonholonomic systems with strong nonlinear drifts

A new input-to-state scaling scheme is first introduced to transform a class of nonholonomic systems in a chained form with strong nonlinear drifts and unknown constant parameters into a strict feedback form. The backstepping technique is then applied to design a global adaptive stabilization controller. A switching strategy based on the control input magnitude rather than the time is derived to get around the phenomenon of uncontrollability. Simulation examples validate the effectiveness of the proposed controller.     

Classical framework for nonholonomic mechanical control systems

With this paper we recall some of the formal framework developed in classical analytical mechanics with emphasis on constrained systems. It can be used in describing nonholonomic control systems and serve as a starting point for further differential-geometric investigations.     

Bearing-based formation manoeuvre control of nonholonomic multi-agent systems

ABSTRACT

This paper concerns on the bearing-based leader–follower formation manoeuvre control problem for two- (2D) and three-dimensional (3D) multi-agent systems with nonholonomic constraint. The target formation is defined by relative-bearing measurements, which, for example, can be obtained from onboard cameras. The contributions of this paper are twofold. Firstly, a distributed formation manoeuvre control law is proposed for 2D nonholonomic agents according to the inter-bearing measurement. The multi-agent systems can achieve the desired formation which is defined by the bearings information. The formation manoeuvre can be achieved by steering at least two leaders. Secondly, the control law is nontrivially extended to 3D nonholonomic multi-agents systems. The leader–follower formation tracking problem can also be solved by the proposed proportional-integral control scheme. Simulation results for 2D and 3D nonholonomic multi-agents systems are presented. Experiments that used ground mobile robots verify the effectiveness of the proposed control laws.     

Learning control in robotic systems

The concept of learning and training machines, and some early methodologies were introduced about two decades ago. Robotic systems, undoubtedly, can be developed to a more advanced and intelligent stage. The realization of the learning capability, analogous to the human learning and thinking process, is a desired primary function. A betterment process has been investigated in the literature for applications of learning control to robotic systems. The existing schemes are a type of one-step learning process. A neighboring (2m+1)-step learning control scheme for robotic systems is presented in this paper. For each process, a betterment algorithm which chooses a generalized momentum as an output function, is executed. Also, it is associated with a conceptual learning process by adding a self-teaching knowledge base for speeding up the convergence, so that the learning capability of the resulting robotic systems can be enhanced.