基于激光雷达的多关节机器人姿态自动控制研究

为了避免机器人坡面行进姿态与平坦地形直行姿态出现较大偏差,保证多关节机器人运动稳定性,研究基于激光雷达的多关节机器人姿态自动控制方法;结合激光雷达定位导航技术,构建CPG单元振荡器模型,根据运动步态生成原则优化处理足结构参数,完成多关节机器人的运动姿态参数设定;根据姿态参数设定结果实现运动坐标转换,利用动力学方程的简化与分解表达式,确定非线性耦合项参数化处理结果,整合所得变量数据建立反馈控制器连接闭环,利用反馈控制器连接闭环自动控制多关节机器人姿态;对比实验结果表明,在激光雷达技术作用下,机器人上、下坡步长与平坦直行步长之间的误差最大值仅为10%,机器人行进过程中不会出现明显晃动情况,多关节机器人运动稳定性较高。     

机器人最优滑动模态变结构PI控制

本文将ＰＩ反馈控制和滑动模态变结构控制规律两者的优点结合起来，针对多关节机器人提出了一种新型最优控制方案。该方案在ＰＩ反馈控制输出的缓慢变化和变结构控制输出的快速切换之间采取一种折衷，引入的积分环节能有效减小控制器输出切换的频率和幅度，达到削弱抖动的目的，给出了滑动模态切换面的优化设计方案。理论分析和仿真结果表明该控制方案能使机器人在各种复杂的非线性动力学情况下保持优良的定位和跟踪性能，为滑动模态     

基于神经网络的非线性系统近似线性化

神经网络具有同时逼近某一函数及其高阶导数的功能，这一结果为神经网络在非线性系统中的应用提供了可行的工具，本文提出了一种利用网络的近似功能的非线性系统的近似线性方法，无论系统是否满足可积条件，神经网络都可实现其对各条件的近似职分，从而构造满足系统近似线性化的反馈控制，对球－杆系统的仿真结果显示了这种方法的有效性。     

含有非驱动关节机器人的学习控制

含有非驱动关节的机器人的运动控制比一般的机器人要困难得多．因为非驱动关节 不能直接控制，系统属于非完全可控系统，一般的光滑反馈控制方法对这样的系统是无效的 ．本文提出了一种学习控制的方法，通过学习获得高精度的前馈控制，实现欠驱动机器人的 高精度运动控制，并在一台实际的欠驱动机器人上进行了实验，给出了实验结果．     

柔性臂漂浮基空间机器人建模与轨迹跟踪控制

利用拉格朗日法和假设模态方法建立了末端柔性的两臂漂浮基空间机器人的非线性动力学方程．通过坐标变换,推导出一种新的以可测关节角为变量的全局动态模型,并在此基础上运用基于模型的非线性解耦反馈控制方法得到关节相对转角与柔性臂的弹性变形部分解耦形式控制方程．最后,讨论了柔性臂漂浮基空间机器人的轨迹跟踪问题,并通过仿真实例计算,表明该模型转换及控制方法对于柔性臂漂浮基空间机器人末端轨迹跟踪控制的有效性．     

高阶耦合机器人的自适应模糊滑模分散控制

针对一类存在模型不确定性和未知非线性扰动的机器人系统,考虑其不确定项和未知扰动项的上界是关于系统状态的普通高阶多项式,结合模糊系统的逼近能力,提出了一种基于滑模控制原理的自适应模糊分散控制方法.该方法不仅能够使得关节之间相互耦合的机器人各关节的控制器仅由本关节的信息就能完全确定,而且消除了现存文献在设计机器人分散控制器...     

一类非齐次高阶非线性系统的连续反馈控制设计

研究了一类非齐次的高阶非线性系统的连续状态反馈控制设计问题. 通过定义一列适当的辅助函数,放宽了对非线性项的约束条件. 利用传统的积分反推技术,并增加一个积分项的方法,得到了这类系统的稳定性,给出了控制器的设计方法,并通过一个例子验证了本文的理论结果.     

基于扩张状态观测的机器人分散鲁棒跟踪控制

针对模型不确定性多关节机器人的轨迹跟踪控制问题, 提出一种基于扩张状态观测补偿的关节独立分散鲁棒控制方法. 设计多个并联的扩张状态观测器, 分别对每个独立关节的未知模型动态与外部扰动进行动态估计和补偿, 采取非线性状态反馈控制以提高系统的控制性能. 各个关节的控制完全独立, 因而控制器结构简单、可靠、易于实现. 对所设计的关节分散鲁棒控制器的 Lyapunov 稳定性进行了分析, 证明系统是指数收敛全局一致最终有界稳定的. 对 PUMA560 机械手的大量控制仿真验证了本文方法的有效性.     

辅助起立机器人反馈控制与速度前馈控制性能对比

对辅助起立机器人的位置控制方法进行了研究.在不同频率时,使用位置速度反馈控制对其进行位置控制测试,辅助起立机器人滑动关节的位置精度较好,但旋转关节位置精度较差.在反馈控制系统的基础上增加速度前馈控制之后进行测试,结果表明增加的速度前馈控制可以有效地提高辅助起立机器人两个运动关节的位置精度,其中旋转关节的位置精度明显改善.证明了在反馈控制的基础上增加速度前馈控制可以有效提高辅助起立机器人位置精度.     

高阶系统方法— I.全驱系统与参数化设计

本文首先指出了控制领域中普遍使用的增广一阶系统方法的弊端, 介绍了高阶全驱系统的概念及其在控制器设计方面的优势, 并通过一些基础物理定律、串联系统、严反馈系统和可反馈线性化系统等例子说明了高阶全驱系统的普遍性, 进而指出高阶全驱系统是动态系统的一种描述形式, 是面向控制的模型.然后介绍了一类高阶全驱系统的一种参数化设计方法.通过适当选取一类非线性状态反馈控制律, 可获得一个具有希望特征结构的线性定常闭环系统, 并给出了闭环系统特征向量和反馈控制律的完全参数化表示, 讨论了解的存在性条件以及设计参数集合的稠密性等相关问题.最后对高阶全驱系统方法的后续问题做了说明和展望.     

Finite-time control for robot manipulators

Finite-time control of the robot system is studied through both state feedback and dynamic output feedback control. The effectiveness of the proposed approach is illustrated by both theoretical analysis and computer simulation. In addition to offering an alternative approach for improving the design of the robot regulator, this research also extends the study of the finite-time control problem from second-order systems to a large class of higher order nonlinear systems.     

非线性迭代学习算法在机器人上肢康复中的应用

针对上肢康复机器人轨迹跟踪控制中存在的患者痉挛扰动非线性及不确定性问题, 结合康复机器人系统执行具有重复性的特点以及迭代学习算法特有的性质, 提出一种非线性迭代学习控制算法, 改进了机器人常用的线性动力学控制系统, 使得在模型信息不精确以及只有角度信息可测的情况下, 也能获得良好的轨迹跟踪性能; 应用Lyapunov 稳定性理论和LaSalle 不变性原理证明了闭环系统的全局渐近稳定性. 仿真结果表明, 所提出的非线性迭代学习控制具有良好的控制性能.

     

Tracking in nonlinear differential-algebraic control systems with applications to constrained robot systems

We consider the design of a feedback control law for control systems described by a class of nonlinear differential-algebraic equations so that certain desired outputs track given reference inputs. The nonlinear differential-algebraic control system being considered is not in state variable form. Assumptions are introduced and a procedure is developed such that an equivalent state realization of the control system described by nonlinear differential-algebraic equations is expressed in a familiar normal form. A nonlinear feedback control law is then proposed which ensures, under appropriate assumptions, that the tracking error in the closed loop differential-algebraic system approaches zero exponentially. Applications to simultaneous contact force and position tracking in constrained robot systems with rigid joints, constrained robot systems with joint flexibility, and constrained robot systems with significant actuator dynamics are discussed.     

Neural Control for Driving a Mobile Robot Integrating Stereo Vision Feedback

This paper proposes a neural control integrating stereo vision feedback for driving a mobile robot. The proposed approach consists in synthesizing a suitable inverse optimal control to avoid solving the Hamilton Jacobi Bellman equation associated to nonlinear system optimal control. The mobile robot dynamics is approximated by an identifier using a discrete-time recurrent high order neural network, trained with an extended Kalman filter algorithm. The desired trajectory of the robot is computed during navigation using a stereo camera sensor. Simulation and experimental result are presented to illustrate the effectiveness of the proposed control scheme.     

Robust model predictive control for constrained continuous-time nonlinear systems

In this paper, a robust model predictive control (MPC) is designed for a class of constrained continuous-time nonlinear systems with bounded additive disturbances. The robust MPC consists of a nonlinear feedback control and a continuous-time model-based dual-mode MPC. The nonlinear feedback control guarantees the actual trajectory being contained in a tube centred at the nominal trajectory. The dual-mode MPC is designed to ensure asymptotic convergence of the nominal trajectory to zero. This paper extends current results on discrete-time model-based tube MPC and linear system model-based tube MPC to continuous-time nonlinear model-based tube MPC. The feasibility and robustness of the proposed robust MPC have been demonstrated by theoretical analysis and applications to a cart-damper springer system and a one-link robot manipulator.     

A predictive controller based on dynamic matrix control for a non-minimum phase robot manipulator

In this paper a hybrid control strategy is presented based on Dynamic Matrix Control (DMC) and feedback linearization methods for designing a predictive controller of five bar linkage manipulator as a MIMO system (two inputs and two outputs). Analyzing the internal dynamic of robot shows the open loop system is unstable and non-minimum phase, so in order to apply the predictive controller, special modifications are needed. These modifications on non-minimum phase behavior are performed using feedback linearization procedure based on state space realization. The design objective is to track a desirable set point as well as time varying trajectories as a command references with globally asymptotical stabilization. The proposed controller is applied to nonlinear fully coupled model of the typical five bar linkage manipulator with non-minimum phase behavior. Simulation results show that the proposed controller has good efficiency. The step responses of system with and without feedback linearization process illustrated that the mentioned modification for stabilizing is performed properly. After applying the proposed predictive controller, the joint angle of robot tracks the reference input while another input acts as the disturbance and vice versa.     

Teleoperation Control of a Position‐Based Impedance Force Controlled Mobile Robot by Neural Network Learning: Experimental Studies

Effective haptic performance in teleoperation control systems can be achieved by solving two major problems: the time‐delay in communication channels and the transparency of force control. The time‐delay in communication channels causes poor performance and even instability in a system. The transparency of force feedback is important for an operator to improve the performance of a given task. This article suggests a possible solution for these two problems through the implementation of a teleoperation control system between the master haptic device and the slave mobile robot. Regulation of the contact force in the slave mobile robot is achieved by introducing a position‐based impedance force control scheme in the slave robot. The time‐delay problem is addressed by forming a Smith predictor configuration in the teleoperation control environment. The configuration of the Smith predictor structure takes the time‐delay term out of the characteristic equation in order to make the system stable when the system model is given a priori. Since the Smith predictor is formulated from exact linear modeling, a neural network is employed to identify and model the slave robot system as a nonlinear model estimator. Simulation studies of several control schemes are performed. Experimental studies are conducted to verify the performance of the proposed control scheme by regulating the contact force of a mobile robot through the master haptic device.     

Semi-global sampled-data output feedback disturbance rejection control for a class of uncertain nonlinear systems

This paper investigates the semi-global output feedback disturbance rejection control problem for a class of uncertain nonlinear systems with additive disturbances using linear sampled-data control. Aiming to reject the adverse effects caused by the uncertainties and unknown nonlinear perturbations which may not satisfy the strict feedback or feedforward structure, a new generalised discrete-time extended state observer is proposed to estimate the disturbance at sampling points. An output feedback disturbance rejection control law is then constructed in a sampled-data form which facilitates digital implementations. By selecting adequate control gains and a sufficiently small sampling period to restrain the state growth under a zero-order-hold input, the semi-global asymptotic stability of the hybrid closed-loop system and the disturbance rejection ability are proved. Both numerical example and an application of a single-link robot arm system demonstrate the feasibility and efficacy of the proposed method.     

Linear learning control of robot motion

For the trajectory following problem of a robot manipulator, a new linear learning control law, consisting of the conventional proportional-integral-differential (PID) control law, with respect to position tracking error, and an iterative learning term is provided. The learning part is a linear feedback control of position, velocity, and acceleration errors (PDD²). It has been shown that, under the proposed learning control, the position, velocity, and acceleration tracking errors are asymptotically stable in the presence of highly nonlinear dynamics. The proposed control is robust in the sense that exact knowledge about nonlinear dynamics is not required except for the bounding functions on their magnitudes. Further, neither is linear approximation of nonlinear dynamics nor repeatability of robot motion required.     

Neural Network Identification Based Multivariable Feedback Linearization Robust Control for a Two-Link Manipulator

Regarding to the variations of the load and unmodeled dynamic, robot manipulators are known as a nonlinear dynamic system. Overcoming such problems like uncertainties and nonlinear characteristics in the model of two-link manipulator is the principal goal of this paper. To approach to this aim, a neural network is combined with a linear robust control in which the result has the advantages of, the first, approximated nonlinear elements and the second, the guaranteed robustness. To design the proposed controller, at first, multivariable feedback linearization is employed to convert the nonlinear model to linear one. Second, the unknown parameters of the system are identified by neural network based on a new proposed learning rule. Third, Mixed linear feedback-H_?∞? robust control method is proposed to stabilize the closed loop system. The closed loop system based on the proposed controller is analyzed and some numerical simulations are performed. Results show suitable responses of the closed loop system.