Terminal control in sea observation systems with mobile platforms for data collection

The expediency of application of terminal control in sea observation systems is shown on the example of a system of profile measurements of the ocean environment constructed on the basis of controlled diving buoys, sea probes. The model of motion of a diving buoy with controlled buoyancy is given. The control law is synthesized according to basic ideas [1], as applied to the digital controller. A method of choosing the controller parameters is proposed, and an algorithm for program implementation of the synthesized control law is developed. It is shown that the constructed iterative algorithm of calculation of the control signal possesses the adaptability property. The results of simulation of control processes under the action of perturbations on the system and variation of the plant parameters are presented.     

约束条件下的巡线机器人逆运动学求解

高压输电线路巡检机器人是一种多关节悬挂运动机构,要实现其运动控制就需要根据机器人的本身机构特点和悬挂系统的运动约束条件进行运动学分析.本文利用微分扭转法对巡线机器人的正向运动学进行了求解,并通过对机器人悬挂系统的力学分析,得到了机器人运动学的约束条件,并在这种约束条件下,采用了一种可用来进行实时控制的迭代循环坐标下降(CCD)算法,来进行机器人的逆运动学求解.这种迭代算法对于有运动约束系统的逆运动学求解具有较强的适用性,而且它具有较好的收敛性和有效性,适合于在线计算,便于巡线机器人的实时运动控制.     

Vibrational control of a class of nonlinear systems by nonlinear multiplicative vibrations

Vibrational control is a nonclassical control principle which proposes a utilization of zero mean parametric excitation of a dynamical system for control purposes. This paper extends nonlinear vibrational control theory developed in [8], [9] to systems controlled by nonlinear multiplicative vibrations. Condition for partial vibrational stabilization with respect to a component of a steady-state vector, the choice of stabilizing vibrations, and the transient motions are discussed for a certain practically important class of nonlinear vibrationally controlled systems. The application of the results is demonstrated on the example of a catalytic reactor, using a combination of numerical and analytical techniques.     

非最小相位系统的基函数型自适应迭代学习控制

针对重复运行的未知非最小相位系统的轨迹跟踪问题, 结合时域稳定逆特点, 提出了一种新的基函数型自适应迭代学习控制(Basis function based adaptive iterative learning control, BFAILC)算法. 该算法在迭代控制过程中应用自适应迭代学习辨识算法估计基函数模型, 采用伪逆型学习律逼近系统的稳定逆, 保证了迭代学习控制的收敛性和鲁棒性. 以傅里叶基函数为例, 通过在非最小相位系统上的控制仿真, 验证了算法的有效性.     

A solution to the inverse kinematics of redundant manipulators

A solution to the inverse kinematics is a set of joint coordinates which correspond to a given set of task space coordinates (position and orientation of end effector). For the class of kinematically redundant robots, the solution is generically nonunique such that special methods are required for obtaining a solution. The method addressed in the paper, introduced earlier and termed “generalized inverse,” is based on a certain partitioning of the Jacobian functional corresponding to a nonlinear relationship of the inverse kinematics type. The article presents a new algorithm for solving the inverse kinematics using the method of generalized inverse based on a modified Newton-Raphson iterative technique. The new algorithm is efficient, converges rapidly, and completely generalizes the solution of the inverse kinematics problem for redundant robots. The method is illustrated by numerical examples.     

Adaptive Control for the Systems Preceded by Hysteresis

Hysteresis hinders the effectiveness of smart materials in sensors and actuators. It is a challenging task to control the systems with hysteresis. This note discusses the adaptive control for discrete time linear dynamical systems preceded with hysteresis described by the Prandtl-Ishlinskii model. The time delay and the order of the linear dynamical system are assumed to be known. The contribution of the note is the fusion of the hysteresis model with adaptive control techniques without constructing the inverse hysteresis nonlinearity. Only the parameters (which are generated from the parameters of the linear system and the density function of the hysteresis) directly needed in the formulation of the controller are adaptively estimated online. The proposed control law ensures the global stability of the closed-loop system, and the output tracking error can be controlled to be as small as required by choosing the design parameters. Simulation results show the effectiveness of the proposed algorithm.     

Stability of iterative learning control with data dropouts via asynchronous dynamical system

In this paper, the stability of iterative learning control with data dropouts is discussed. By the super vector formulation, an iterative learning control (ILC) system with data dropouts can be modeled as an asynchronous dynamical system with rate constraints on events in the iteration domain. The stability condition is provided in the form of linear matrix inequalities (LMIS) depending on the stability of asynchronous dynamical systems. The analysis is supported by simulations.     

非线性系统高阶迭代学习算法

结合迭代学习控制算法中的开环和闭环方案，本文针对更一般的非线性系统，讨论高阶算法的广泛适用性。理论和仿真结果表明了高阶算法在输出跟踪和干扰抑制方面的有效性。     

Discrete-time adaptive iterative learning control with unknown control directions

An adaptive iterative learning control scheme is proposed for a class of discrete-time nonlinear systems with random initial conditions and iteration-varying desired trajectories. The discrete Nussbaum gain method is incorporated into the control design to tackle the problem associated with the lack of a priori knowledge of the control directions. The proposed control algorithm guarantees the boundedness of all the signals in the controlled system. The tracking error converges to zero asymptotically along the iterative learning axis. The effectiveness of the proposed control law is verified through numerical simulation.     

基于折扣广义值迭代的智能最优跟踪及应用验证

设计了一种基于折扣广义值迭代的智能算法,用于解决一类复杂非线性系统的最优跟踪控制问题.通过选取合适的初始值,值迭代过程中的代价函数将以单调递减的形式收敛到最优代价函数.基于单调递减的值迭代算法,在不同折扣因子的作用下,讨论了迭代跟踪控制律的可容许性和误差系统的渐近稳定性.为了促进算法的实现,建立一个数据驱动的模型网络用...     

H∞ control of uncertain dynamical fuzzydiscrete-time systems

A new kind of dynamical fuzzy model is proposed to represent discrete-time complex systems which include both linguistic information and system uncertainties. A new stability analysis and control system design approach is then developed for this kind of dynamical fuzzy model. Furthermore, a constructive algorithm is developed to obtain the H(infinity) feedback control law. An example is given to illustrate the application of the method.     

Iterative learning control and the singularity robust Jacobian inverse for mobile manipulators

The method of iterative learning control, to a large extent, has been inspired by robotics research, focused on the control of stationary manipulators. In this article we deal with the inverse kinematics problem for mobile manipulators, and show that a very basic singularity robust Jacobian inverse can be derived in a natural way within the framework of iterative learning control. To achieve this objective we have exploited the endogenous configuration space approach. The introduced Jacobian inverse defines the singularity robust Jacobian inverse kinematics algorithm for mobile manipulators. A Kantorovich-type estimate of the region of guaranteed convergence of the algorithm is derived. For two example kinematics, this estimate has been computed efficiently.     

改进的自适应多种群DE的机械臂控制方法

根据机械臂关节轴线方向建立了连杆坐标系,利用Denavit-Hartenberg（D-H）法得到连杆坐标系变换矩阵;通过连杆坐标系变换矩阵得到机械臂正运动控制模型;通过正运动模型得到逆运动控制模型,逆运动控制模型是多目标约束优化问题,该模型的最优解既可以保证控制精度,又可以保证各个关节角变动幅度的总代价（定义为旋转副）达到最小。为了求解机械臂逆运动模型,提出了自适应多种群差分演化算法（AMPDE）,多种群策略可以提升个体共享群体信息的能力,自适应变异策略可以提升种群多样性,数值实验表明该算法可以有效求解机械臂逆运动学模型。     

6R机器人实时逆运动学算法研究

提出一套解决各类6R机器人逆运动学问题的实时算法. 一般算法通过矢量计算和16阶矩阵分解得到一般6R机器人的最多16组逆运动学解. 封闭解法直接提取运动学等式求出关节变量的解析解. 组合算法将封闭解法或一般算法的结果作为初始值, 采用牛顿-拉夫森方法迭代出逆运动学精确解, 适用于所有接近满足封闭解条件或一般算法条件的6R机器人. 求解实验结果表明, 整套算法最大算法时间约为2.03 ms, 为任意几何结构的6R机器人应用于强实时系统提供了逆运动学解决方案.     

基于迭代学习观测器的航天器姿态主动容错控制

针对含有外部扰动和执行器故障的一类航天器姿态控制系统,本文提出基于迭代学习观测器的主动容错控制方案.首先,建立了含有外部扰动和执行器故障的航天器姿态控制系统的运动学和动力学模型.其次,为了提高观测器的故障估计精度,在传统迭代学习观测器设计基础上引入上一时刻状态估计误差信息,文章提出一种改进型学习估计算法.进一步,基于滑模控制和指定时间稳定理论,利用学习观测器的故障估计信息设计指定时间主动容错控制器.与现有的航天器主动容错控制方案相比,本文所提出的算法的优势在于可以使故障系统的姿态能在指定时间跟踪上指令信号.基于Lyapunov方法,本文从理论上证明了改进型学习观测器和姿态容错控制系统的稳定性.最后,通过数值仿真,说明了所提容错控制方案的有效性和可行性.     

Implications of dissipativity,inverse optimal control,and stability margins for nonlinear stochastic feedback regulators

In this paper, we derive stability margins for optimal and inverse optimal stochastic feedback regulators. Specifically, gain, sector, and disk margin guarantees are obtained for nonlinear stochastic dynamical systems controlled by nonlinear optimal and inverse optimal Hamilton‐Jacobi‐Bellman controllers that minimize a nonlinear‐nonquadratic performance criterion with cross‐weighting terms. Furthermore, using the newly developed notion of stochastic dissipativity, we derive a return difference inequality to provide connections between stochastic dissipativity and optimality of nonlinear controllers for stochastic dynamical systems. In particular, using extended Kalman‐Yakubovich‐Popov conditions characterizing stochastic dissipativity, we show that our optimal feedback control law satisfies a return difference inequality predicated on the infinitesimal generator of a controlled Markov diffusion process if and only if the controller is stochastically dissipative with respect to a specific quadratic supply rate.     

Inverse dynamics of redundant manipulators using a minimum number of control forces

The number of the control actuators used by the inverse kinematics and dynamics algorithms that have been developed in the literature for generating redundant robot joint trajectories is equal to the number of the degrees of freedom of the manipulator. In this article, an inverse dynamics algorithm that performs the tasks using only a minimum number of actuators is proposed. The number of actuators is equal to the dimension of the task space, and the control forces are solved simultaneously with the corresponding system motion. It is shown that because all degrees of freedom are not actuated, the control forces may lose the ability to make an instantaneous effect on the end-effector acceleration at certain configurations, yielding the dynamical equation set of the system to be singular. The dynamical equations are modified in the neighborhood of the singular configurations by utilizing higher-order derivative information, so that the singularities in the numerical procedure are avoided. Asymptotically stable inverse dynamics closed-loop control in the presence of perturbations is also discussed. The algorithm is further generalized to closed chain manipulators. Three-link and two-link redundant planar manipulators are analyzed to illustrate the validity of the approach. © 2995 John Wiley & Sons, Inc.     

矩阵方程AXB+CYD=E最佳逼近自反解的迭代算法

利用复合最速下降法的迭代算法能够求出矩阵方程[AXB+CYD=E]的最佳逼近自反解,但其收敛速度很慢。针对这一问题,提出一种利用共轭方向法的迭代算法。对于任给初始自反矩阵[X1]和[Y1],无论矩阵方程[AXB+CYD=E]是否相容,该算法都可以经过有限次迭代计算出其最佳逼近自反解。两个数值例子表明该算法是可行的,且收敛速度更快。     

Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties

This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input multi-output nonlinear system. The control strategy is an inverse nonlinear controller combined with an adaptive neural network with sliding mode control using an on-line learning algorithm. The adaptive neural network with sliding mode control acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations in its entire structure (kinematics and dynamics). The controllers are obtained by using Lyapunov's stability theory. Experimental results of a case study show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.     

带有时滞的区间动力系统的鲁棒稳定性研究

给出了区间动力系统稳定性的相关概念,讨论了带有时滞的区间动力系统的鲁棒稳定性.通过构造控制矩阵和迭代相结合的方法,给出了一类带有时滞的区间动力系统鲁棒稳定的判别方法,并且给出了系统鲁棒稳定时参数取值的最大区间.将含有时滞的区间动力系统的稳定性转化为判别一个矩阵的稳定性,推广和改进了已有的结论.