带有未知参数和有界干扰的移动机器人轨迹跟踪控

针对模型参数未知和存在有界干扰的非完整移动机器人的轨迹跟踪控制问题,本文提出了一种鲁棒自适应轨迹跟踪控制器方法.非完整移动机器人的控制难点在于它的运动学系统是欠驱动的.针对这一难点,本文利用横截函数的思想,引入新的辅助控制器,使得非完整移动机器人系统不再是一个欠驱动系统,缩减了控制器设计的难度,进而利用非线性自适应算法和参数映射方法构造李雅谱诺夫函数.通过李雅普诺夫方法设计控制器和参数自适应器,从而使得非完整移动机器人的跟随误差任意小,即可以任意小的误差来跟随任意给定的参考轨迹.仿真结果证明了方法的有效性.     

带有未知参数和有界干扰的移动机器人轨迹跟踪控制

针对模型参数未知和存在有界干扰的非完整移动机器人的轨迹跟踪控制问题,本文提出了一种鲁棒自适应轨迹跟踪控制器方法.非完整移动机器人的控制难点在于它的运动学系统是欠驱动的.针对这一难点,本文利用横截函数的思想,引入新的辅助控制器,使得非完整移动机器人系统不再是一个欠驱动系统,缩减了控制器设计的难度,进而利用非线性自适应算法和参数映射方法构造李雅谱诺夫函数.通过李雅普诺夫方法设计控制器和参数自适应器,从而使得非完整移动机器人的跟随误差任意小,即可以任意小的误差来跟随任意给定的参考轨迹.仿真结果证明了方法的有效性.     

任意切换下高能随机系统的神经网络预设控制

针对一类不确定高能随机非线性系统,开展自适应神经网络backstepping控制研究,并保证在任意切换信号下的预设跟踪性能.该高能系统假定系统动态和任意切换信号未知.首先,利用预设性能控制,保证跟踪控制性能;其次,RBF神经网络用来克服未知系统动态,仅用到单一自适应更新参数,从而克服过参数问题;最后,基于公共的Lyapunov稳定性理论提出自适应神经网络控制策略,并减少了学习参数.最终结果表明所设计的公共控制器能保证所有闭环信号半全局最终一致有界,并能在任意切换下保证预设的跟踪性能.仿真结果进一步表明所提出方法的有效性.     

卫星编队飞行的鲁棒自适应控制方法

研究了主从式框架下编队飞行的相对控制问题.首先推导了描述主从星相对运动的完整非线性动力学模型, 利用完整模型的无摄动形式提出了最优参考轨迹生成问题,并应用高斯伪谱法将此问题转换成非线性规划问题,使其可以数值求解; 基于Lyapunov 方法设计了闭环系统的鲁棒自适应控制器,在存在未知干扰、未知主星轨道参数与控制以及未知从星质量的情况下, 仅利用相对状态测量即能够保证闭环系统的参考轨迹跟踪误差和参数估计误差全局一致最终有界,并证明了跟踪误差的最终界可以 通过选取合理的控制器参数使其任意小;最后给出了具体的仿真场景验证了本文主要结果的有效性.     

带非三角结构的非线性系统自适应控制设计

针对一类带有非三角结构的不确定非线性系统,研究了其控制设计问题,提出了一种自适应跟踪控制设计方法。通过构造一种新的李雅普诺夫函数,并运用增加幂次积分方法和模糊控制方法,设计得到了一种新型自适应跟踪控制器。控制器只引入了一个参数自适应律,避免了反推设计中的过参数化问题,同时保证了输出跟踪误差可以任意小,且使得闭环系统的所有信号有界。最后,将控制方法应用到单连杆机械臂系统上,通过仿真研究验证了所提控制方案的有效性。     

不确定性R¨ossler 系统的自适应鲁棒跟踪控制

研究具有外部不确定性R¨ossler 混沌系统的鲁棒跟踪控制问题. 基于动态面控制原理设计自适应鲁棒控制器, 给出了系统参数的自适应更新律, 使得被控闭环系统的各误差变量一致有界. 系统输出曲线渐近跟踪任意期望轨道, 且跟踪误差能被控制在任意小的范围内, 而无须知道系统的参数及外部不确定性的界限. 基于稳定理论给出了具体的稳定性分析, 并通过数值仿真验证了该方法的有效性及鲁棒性.

     

高阶非线性不确定系统的自适应实际输出跟踪控制

本文研究了一类高阶非线性不确定系统的自适应实际输出跟踪控制问题, 该问题在未知控制系数的下界精确知道的假设下已经得到了研究. 基于新的鲁棒自适应控制和连续控制思想, 成功去除该假设条件. 进而应用增加幂次积分的方法, 给出了构造连续自适应实际输出跟踪控制器的系统化方法. 该控制器确保闭环系统的所有状态全局稳定, 并且经过有限时间后, 跟踪误差可以被某一事先给定的任意正数界定. 最后, 通过一个仿真算例验证了理论结果的正确性.     

电液伺服系统自适应逆向递推控制器设计

针对电液伺服系统的精确位置跟踪控制问题,引入虚拟控制量的概念,提出了一种逆向递推控制器的设计方法;同时考虑到实际系统存在内部参数和外负载力的不确定性,采用Lyapunov稳定理论对系统的不确定性进行自适应控制律设计,最终得到了自适应Backstepping控制器,并进行了稳定性分析.仿真结果表明,设计的自适应Backstepping控制器不需要知道内部不确定性参数和外部负载力干扰的边界,与常规PID控制方法相比,具有较强的鲁棒性及良好的位置跟踪性能.     

具有强相容参数估计的多变量自校正前馈控制器

本文提出了一种新的自校正前馈控制器.该控制器具有如下优点:1)可以控制具有任意 传输延时结构的随机多变量系统;2)可以控制开环不稳定或非最小相位系统;3)不加积分作用 可以自适应消除偏差和可测干扰对输出的影响,消除跟踪误差;4)具有全局稳定特性;5)参数 估计具有强相容性.     

一类未知参数非线性系统的自适应无源控制

对一类含有未知参数的本质非线性系统,通常的Backstepping方法无法应用.为此,提出一种基于状态反馈的自适应无源控制对策,通过对非线性系统进行反馈无源化控制,设计自适应无源镇定控制器和自适应无源输出跟踪控制器.设计中适当调节自适应无源控制器参数,能够保证闭环系统稳定且所有信号全局一致有界,从而解决了此类含有未知参数非线性系统的稳定性和输出跟踪问题.仿真算例验证了提出控制方案的有效性,表明系统具有较强的稳定性和跟踪特性.     

An Adaptive Unscented Kalman Filter-based Controller for Simultaneous Obstacle Avoidance and Tracking of Wheeled Mobile Robots with Unknown Slipping Parameters

A novel unified control approach is proposed to simultaneously solve tracking and obstacle avoidance problems of a wheeled mobile robot (WMR) with unknown wheeled slipping. The longitudinal and lateral slipping are processed as three time-varying parameters and an Adaptive Unscented Kalman Filter (AUKF) is designed to estimate the slipping parameters online More specifically, an adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the Unscented Kalman Filter (UKF) context. A stable unified controller is applied to simultaneously handle tracking and obstacle avoidance for this WMR system to compensate for the unknown slipping effect. Applying Lyapunov stability theory, it is proved that tracking errors of the closed-loop system are asymptotically convergent regardless of unknown slipping, the tracking errors converge to the zero outside the obstacle detection region and obstacle avoidance is guaranteed inside the obstacle detection region. The effectiveness and robustness of the proposed control method are validated through simulation and experimental results.     

On the adaptive control of robot manipulators with velocity observers

To achieve accurate tracking control of robot manipulators many schemes have been proposed. A common approach is based on adaptive control techniques, which guarantee trajectory tracking under the assumption that the robot model structure is perfectly known and linear in the unknown parameters, while joint velocities are available. Despite tracking errors tend to zero, parameter errors do not unless some persistent excitation condition is fulfilled. There are few works dealing with velocity observation in conjunction with adaptive laws. In this note, an adaptive control/observer scheme is proposed for tracking position of robot manipulators. It is shown that tracking and observation errors are ultimately bounded, with the characteristic that when a persistent excitation condition is matched then they, as well as the parameter errors, tend to zero. Simulation results are in good agreement with the developed theory.     

Hybrid adaptive fuzzy identification and control of nonlinearsystems

We present a combined direct and indirect adaptive control scheme for adjusting an adaptive fuzzy controller, and adaptive fuzzy identification model parameters. First, using adaptive fuzzy building blocks, with a common set of parameters, we design and study an adaptive controller and an adaptive identification model that have been proposed for a general class of uncertain structure nonlinear dynamic systems. We then propose a hybrid adaptive (HA) law for adjusting the parameters. The HA law utilizes two types of errors in the adaptive system, the tracking error and the modeling error. Performance analysis using a Lyapunov synthesis approach proves the superiority of the HA law over the direct adaptive (DA) method in terms of faster and improved tracking and parameter convergence. Furthermore, this is achieved at negligible increased implementation cost or computational complexity. We prove a theorem that shows the properties of this hybrid adaptive fuzzy control system, i.e., bounds for the integral of the squared errors, and the conditions under which these errors converge asymptotically to zero are obtained. Finally, we apply the hybrid adaptive fuzzy controller to control a chaotic system, and the inverted pendulum system     

Motion synchronization of dual-cylinder pneumatic servo systems with integration of adaptive robust control and cross-coupling approach

We investigate motion synchronization of dual-cylinder pneumatic servo systems and develop an adaptive robust synchronization controller. The proposed controller incorporates the cross-coupling technology into the integrated direct/indirect adaptive robust control （DIARC） architecture by feeding back the coupled position errors, which are formed by the trajectory tracking errors of two cylinders and the synchronization error between them. The controller employs an online recursive least squares estimation algorithm to obtain accurate estimates of model parameters for reducing the extent of parametric uncertainties, and uses a robust control law to attenuate the effects of parameter estimation errors, unmodeled dynamics, and disturbances. Therefore, asymptotic convergence to zero of both trajectory tracking and synchronization errors can be guaranteed. Experimental results verify the effectiveness of the proposed controller.     

Improving the tracking performance of mechanical systems by adaptive extended friction compensation

The paper discusses a tracking control system and shows with simulation and experimental results that extended friction models can be successfully incorporated in a computed-torque-like adaptive control scheme. The friction model used includes Coulomb, viscous, and periodic friction with sense of direction dependent parameters. To get small tracking errors, adaptation of the friction model parameters is necessary. The tracking performance is an order of magnitude better than with PD control. The robustness of the scheme for parameter inaccuracies is sufficient, owing to the adaptation, but the controller gains are limited due to stability problems caused by unmodeled dynamics.     

控制增益未知的多变量极值搜索系统神经网络自适应协同控制

针对一类控制增益未知的多变量极值搜索系统,提出了一种神经网络自适应协同控制方法.该方法利用协同控制实现状态变量之间的协同收敛,并确保对系统内部参数扰动和外界干扰具有不变性;以极值搜索控制方法得到的搜寻变量作为输入量,设计多层神经网络逼近状态变量的极值变化率和未知的变量与函数;采用Nussbaum函数解决系统控制增益未知的问题;同时运用自适应参数抵消神经网络逼近误差的影响.稳定性分析证明了系统的状态跟踪误差、输出量与其极值之间的误差、极值搜索变量的跟踪误差以及神经网络各参数的估计误差均指数收敛至原点的一个有界邻域.理论分析与仿真结果验证了该方法的有效性.     

空间机械臂的鲁棒复合自适应控制

对于本体姿态受控而位置不受控的空间机械臂系统,考虑存在参数不确定性及非参数 不确定性,首先建立了系统的估计模型,并且提出了一种鲁棒复合自适应控制方法,其参数适应 律由估计误差和跟踪误差共同决定.证明了这种方法不仅可维持系统的全局渐近稳定,而且还 可快速收敛和减小跟踪误差.     

Robust adaptive control using a universal approximator for SISOnonlinear systems

This paper deals with the robust adaptive control of a class of nonlinear systems in the presence of parametric uncertainties and dominant uncertain nonlinearities. The proposed controller utilizes the robust adaptive control to guarantee uniform boundedness and convergence of tracking errors. In addition, an adaptive fuzzy logic system is used as a universal approximator to reduce the model uncertainties coming from uncertain nonlinearities and to improve tracking performance. The approach does not require the matching condition imposed on control systems by using the backstepping design procedure, and provides boundedness of tracking errors under poor parameter adaptation. The method can be applied to a class of single-input single-output (SISO) nonlinear systems, transformable to a parametric-strict-feedback form     

Adaptive output feedback tracking control of a nonholonomic mobile robot

An adaptive output feedback tracking controller for nonholonomic mobile robots is proposed to guarantee that the tracking errors are confined to an arbitrarily small ball. The major difficulties are caused by simultaneous existence of nonholonomic constraints, unknown system parameters and a quadratic term of unmeasurable states in the mobile robot dynamic system as well as their couplings. To overcome these difficulties, we propose a new adaptive control scheme including designing a new adaptive state feedback controller and two high-gain observers to estimate the unknown linear and angular velocities respectively. It is shown that the closed loop adaptive system is stable and the tracking errors are guaranteed to be within the pre-specified bounds which can be arbitrarily small. Simulation results also verify the effectiveness of the proposed scheme.     

一类复杂非线性系统的模糊控制

针对一类复杂非线性系统,把模糊T-S模型和自适应模糊逻辑系统结合起来,提出了一种跟踪控制方案.首先,应用模糊T-S模型对非线性系统建模,设计观测器用来观测系统状态:其次,应用基于权值、中心和宽度3个参数可调节的自适应时延模糊逻辑系统补偿器来消除建模误差和小确定性.文中证明了闭环系统满足期望的跟踪性能.示例仿真结果表明了该方案的有效性.