The control of robot manipulators with bounded input

The problem of tracking a desired trajectory in the state space of ann-link robotic manipulator with bounds on the allowable input torque is considered. Using a so-called optimal decision strategy (ODS), a pointwise optimal control law is derived which, at each timet, minimizes the deviation between the vector of joint accelerations and a desired joint acceleration vector, subject to the input constraints. The design of the optimal control law is reduced to the solution of a quadratic programming problem which is solved using the primal-dual method. The solution gives an on-line feedback control scheme for trajectory following in the presence of input constraints. In addition, we extend the above optimal decision strategy to the case where the controller design is based on a simplified model or where the plant itself is imprecisely known. The resulting torque optimization scheme may be incorporated into any existing control scheme to account for input bounds. This has important implications for the problem of deriving robust control schemes that take into account parameter uncertainty and model simplification. Simulations are presented for the case of a three-link manipulator with bounded torque, and our results are compared to the computed torque method. Our simulations show that by optimally adjusting the input torque to each joint when one or more of them saturates, significant improvement in tracking performance can result.     

A practical PID regulator with bounded torques for robot manipulators

This paper proposes a saturated nonlinear PID regulator for industrial robot manipulators. Our controller considers the natural saturation problem given by the output of the control computer, the saturation phenomena of the internal PI velocity controller in the servo driver, and the actuator torque constraints of the robot manipulator. An approach based on the singular perturbations method is used to analyze the exponential stability of the closed-loop system. Experimental essays show the feasibility of the proposed controller. Furthermore, the theoretical results justify why the classical PID used in industrial robots preserves its exponential stability despite the saturation effects of the electronic control devices and the actuator torque constraints.     

力矩受限的机器人吸引域估计方法

针对力矩受限的机器人组合非线性反馈控制的局部稳定区域描述问题,研究了吸引域的估计方法.利用不变集属性和椭球性质,定义两种不同意义的最大椭球不变集来逼近吸引域,分别采用设置初始状态法和参考形状集法求解.通过带有约束的优化问题描述,所有条,件均能转化为线性矩阵不等式条件,易于求解.由于采用优化技术,能够减小吸引域估计的保守性.数值算例验证了所提方法的有效性.     

A novel global asymptotic stable set-point fuzzy controller with bounded torques for robot manipulators

This paper addresses the set-point control of robot manipulators with friction where avoiding saturation of the actuators is a major issue. The original contribution is a novel direct fuzzy control system dealing with both practical constraints in mechanical manipulators: saturation and friction. The control system is made by taking advantage of input-output properties of the so-called sectorial fuzzy controllers. When friction is considered, we prove, via Lyapunov theory, that the steady state position errors owing to static friction are inside of a global attractor, which can be arbitrarily reduced. In case of absence of friction, the closed-loop system becomes globally asymptotic stable. In both cases, the important theoretical and practical feature of maintaining the control actions always within prescribed limits according to the actuator torque capabilities is guaranteed. Experimental evaluation of the proposed direct fuzzy control system on a nonlinear direct-drive robot arm is presented to validate its effectiveness.     

Robust control algorithms with compensation of bounded perturbations

A scheme of robust control of uncertain plants which does without measuring the derivatives of the controlled variable and compensates the bounded external perturbations to within δ was proposed. Consideration was given to the problems of stabilization and reference model-based control. Examples of computer-assisted simulation illustrating efficiency of the propose scheme of control were presented.     

Consensus seeking in multiagent cooperative control systems with bounded control input

Consensus algorithms in multiagent cooperative control systems with bounded control input are studied in this paper.Consensus algorithms are considered for the single-integrator dynamics and double-integrator dynamics under different communication interaction topologies,and show that consensus is reached asymptotically using the algorithm proposed in this paper for the single-integrator dynamics if the undirected interaction graph is connected,and consensus is reached asymptotically if the directed interaction graph is strongly connected,respectively.In addition,the paper further shows that consensus is reached asymptotically using the algorithm proposed for the double-integrator dynamics if the directed interaction graph is strongly connected.The effectiveness of these algorithms is demonstrated through simulations.     

Robust control of robot manipulators with parametric uncertainty

This paper proposes a robust control law for n-link robot manipulators with parametric uncertainty whose upper bound is not assumed to be known. The proposed robust control based on the Corless-Leitmann approach includes a simple estimation law for the upper bound on the parametric uncertainty and an additional control input to be updated as a function of the estimated value. Using the Lyapunov stability theory, the uniform ultimate boundedness of the tracking error is proved     

Robust control with compensation of bounded perturbations and noise

A problem of robust control of the plant is considered when the state vector is measured with noise and the plant is subjected to parametric and external bounded perturbations. A class of objects is singled out, for which noise and perturbations can be compensated on the controlled variable up to the required accuracy. A numeric example and the results of simulation of the designed tracking system are given.     

输入力矩受限的机器人鲁棒自适应跟踪控制

在输入力矩受限的情况下, 提出一种全新的简单鲁棒自适应跟踪控制算法, 当参数的估计范围包含其真实值时, 证明了闭环系统的渐近稳定跟踪;当有干扰存在, 常规参数估计自适应控制算法不能实现稳定控制时, 本算法仍然使系统稳定, 在本算法中, 所估计的参数在跟踪控制律前馈项中表现为非线性, 这是区别于常规参数估计自适应算法的一个最重要特征. 因此本算法控制器的设计更有灵活性, 另一方面获得更好的控制品质和鲁棒性, 特别是对参数域估计误差即参数范围估计错误的强鲁棒性, 均为仿真算例所验证.     

Robust neural network–based tracking control and stabilization of a wheeled mobile robot with input saturation

This paper presents a robust neural network–based control scheme to deal with the problem of tracking and stabilization simultaneously for a wheeled mobile robot subject to parametric uncertainties, external disturbances, and input saturation. At first, a new error‐state transformation scheme is designed by introducing some auxiliary variables as an additional virtual control signals to reduce the adverse effect caused by the underactuation. These variables can change their structures for different desired trajectories to be tracked. Then, a robust control law is proposed combining with a kinematic controller and a dynamic controller, while a three‐layer neural network system is applied to approximate model uncertainties. Stability analysis via the Lyapunov theory shows that the proposed controller can make tracking errors converge to bounded neighborhoods of the origin. Finally, some simulation results are illustrated to verify the effectiveness of the proposed control strategy.     

Robust adaptive control for robot manipulators

A composite adaptive control law for robot manipulators in task space, which uses both the tracking error and the prediction error to drive parameter estimation, is developed in this paper. It is shown that global stability and convergence can be achieved for the adaptive control algorithm in the ideal case, and furthermore that the algorithm can be easily modified by using parameter projection to achieve robustness with respect to a class of unmodelled dynamics. In addition, the algorithm has the advantage that no requirement is needed for the inverse of the jacobian matrix or for the bounded inverse of the estimated inertia matrix. A simulation example is provided for performance demonstration.     

Robust microprocessor control of robot manipulators

In this paper the problem of robust tracking for robot manipulators in the presence of uncertainty and input constraints is studied. Using the theory of uncertain dynamical systems, robust non-linear control strategies, with guaranteed tracking properties that can be quantified given bounds on the extent of model uncertainty, sensor noise, input disturbances, etc., are derived. A torque optimization strategy is also utilized to optimize the joint torques in the event of actuator saturation. The algorithm has been implemented using a single Motorola 12 MHz MC68000 microprocessor on a three-link revolute joint manipulator constructed by the School of Mechanical Engineering at Cornell University. Experimental results are presented here showing the feasibility and performance of the control stragegy.     

Robust control of nonlinear systems with input unmodeled dynamics

In the rich repertoire of design methodologies for uncertain nonlinear systems, the problem of unmodeled dynamics has thus far received little attention. The authors initiate the investigation of nonlinear systems with input unmodeled dynamics. First the authors show that even in their simplest forms, they can result in dramatic shrinking of the region of attraction and in finite escape time. In this paper the authors introduce a dynamic nonlinear damping design which guarantees global boundedness in the presence of input unmodeled dynamics. With this design the authors achieve global asymptotic stability for strict-feedback systems and output-feedback systems     

具有输入饱和的近空间飞行器鲁棒控制

针对近空间飞行器这一类存在外部扰动,输入饱和和参数不确定的多输入多输出线性系统,提出了一种基于干扰观测器的抗饱和鲁棒控制方案.将干扰观测器与抗饱和控制技术相结合,从而消除系统存在的未知外部扰动、输入饱和和不确定性对系统控制的影响.首先,设计干扰观测器对线性外部系统产生的未知扰动进行估计.然后根据干扰观测器输出,通过超前抗饱和方法设计抗饱和补偿器,并将其加入到鲁棒控制器的设计中,保证闭环系统存在输入饱和、未知外部扰动和参数不确定情况下的稳定性.为便于设计,干扰观测器、抗饱和补偿器和控制器设计矩阵均通过求解线性矩阵不等式得到.最后,将提出的鲁棒抗饱和控制方法应用于近空间飞行器,仿真结果验证了该控制方案的有效性.     

Robust nonlinear control of systems with input unmodeled dynamics

We consider systems with unmodeled dynamics appearing at the input, and present a redesign that preserves global asymptotic stability. The admissible unmodeled dynamics are relative degree zero and minimum phase. The redesign is illustrated on an analytical example.     

Robust control of robot manipulators with fast load adaptation

Many adaptive robot controllers have been proposed in the literature to solve manipulator trajectory tracking problems for high-speed operations in the presence of parameter uncertainties. However, most of these controllers stem from the applications of the existing adaptive control theory, which is traditionally focused on tracking slowly time-varying parameters. In fact, manipulator dynamics have fast transient processes for high-speed operations and load changes are abrupt. These observations motivate the present research to incorporate change detection techniques into self-tuning schemes for tracking abrupt load variations and achieving fast load adaptation. To this end, a robustly global stabilizing controller for a robot model with parametric and non-parametric uncertainies is developed based on the Lyapunov second method, and it is then made adaptive via the self-tuning regulator concept. The two-model approach to online change detection in load is used and the estimation algorithm is reinitialized once load changes are detected. This allows a much faster adaptive identification of load parameters than the ordinary forgetting factor approach. Simulation results demonstrate that the proposed controller achieves better tracking accuracy than the existing adaptive and non-adaptive controllers.     

Robust distributed control of robot formations with parameter uncertainty

This paper discusses robot formations in a distributed framework. The most important contribution is the incorporation of robustness into robot formation systems. When robots carry out tasks in a poor environment, the parameters in their models fluctuate around the nominal values, which may destroy the stability of the formation system. By modeling the group of robots as an interconnected system, we aim to develop a set of robust distributed controllers such that the overall system is robust to external disturbance as well as parameter uncertainty. Based on the graph rigidity theory, we also consider the rotation of a formation that plays an important role in real-time applications. Both simulations and real-time experiments are carried out to validate the effectiveness of the proposed framework.     

力矩输入有界的柔性关节机器人轨迹跟踪控制

为解决柔性关节机器人在关节驱动力矩输出受限情况下的轨迹跟踪控制问题,提出一种基于奇异摄动理论的有界控制器.首先,利用奇异摄动理论将柔性关节机器人动力学模型解耦成快、慢两个子系统.然后,引入一类平滑饱和函数和径向基函数神经网络非线性逼近手段,依据反步策略设计了针对慢子系统的有界控制器.在快子系统的有界控制器设计中,通过关节弹性力矩跟踪误差的滤波处理加速系统的收敛.同时,在快、慢子系统控制器中均采用模糊逻辑实现控制参数的在线动态自调整.此外,结合李雅普诺夫稳定理论给出了严格的系统稳定性证明.最后,通过仿真对比实验验证了所提出控制方法的有效性和优越性.     

Robust control for spacecraft rendezvous with disturbances and input saturation

This paper deals with the stabilization problem of spacecraft rendezvous in the presence of disturbances and input saturation. A dead zone operator based model is used to describe the saturation phenomenon. By using Lyapunov method, two groups of control laws are obtained, which ensure the input-to-state stability and the input-to-state practical stability of the closed-loop systems respect to disturbance acceleration, respectively. Simulation results are provided to illustrate the effectiveness of the proposed approaches.     

Robust model predictive control of constrained linear systems with bounded disturbances

This paper provides a novel solution to the problem of robust model predictive control of constrained, linear, discrete-time systems in the presence of bounded disturbances. The optimal control problem that is solved online includes, uniquely, the initial state of the model employed in the problem as a decision variable. The associated value function is zero in a disturbance invariant set that serves as the ‘origin’ when bounded disturbances are present, and permits a strong stability result, namely robust exponential stability of the disturbance invariant set for the controlled system with bounded disturbances, to be obtained. The resultant online algorithm is a quadratic program of similar complexity to that required in conventional model predictive control.