VARIABLE STRUCTURE CONTROL OF TWO MANIPULATORS HANDLING A CONSTRAINED OBJECT

The control of two manipulators handling a constrained object involves the control of the position of the object, the internal force used to grasp the object, and the constraint force due to the constraint surface. The robustness of the controller must be guaranteed when the system faces parameter uncertainties and or external disturbances. In this paper, a variable structure control law is proposed. This controller guarantees the asymptotic convergence of the position of the object, internal force, and constraint force to their desired values when uncertainties on the parameters and external disturbances are present in the system. Simulation results for two planar robots moving an object along a horizontal plane illustrate the fact that the proposed controller achieves the desired asymptotic tracking.     

VSC coordinated control of two manipulator arms in the presence ofenvironmental constraints

A variable structure control (VSC) method is developed for motion, internal force, and constrained force control of two manipulators grasping a common constrained object. Based on a transformed dynamic equation of the entire system in the joint space, a motion and force control are designed together via a VSC method with robustness to parametric uncertainties and external disturbances. The proposed controller guarantees the system with prescribed qualities in the sliding mode and during the reaching transient. Simulation results illustrate the method     

Adaptive neural controller for cooperative multiple robot manipulator system manipulating a single rigid object

In this article, an adaptive neural controller is developed for cooperative multiple robot manipulator system carrying and manipulating a common rigid object. In coordinated manipulation of a single object using multiple robot manipulators simultaneous control of the object motion and the internal force exerted by manipulators on the object is required. Firstly, an integrated dynamic model of the manipulators and the object is derived in terms of object position and orientation as the states of the derived model. Based on this model, a controller is proposed that achieves required trajectory tracking of the object as well as tracking of the desired internal forces arising in the system. A feedforward neural network is employed to learn the unknown dynamics of robot manipulators and the object. It is shown that the neural network can cope with the unknown nonlinearities through the adaptive learning process and requires no preliminary offline learning. The adaptive learning algorithm is derived from Lyapunov stability analysis so that both error convergence and tracking stability are guaranteed in the closed loop system. Finally, simulation studies and analysis are carried out for two three-link planar manipulators moving a circular disc on specified trajectory.     

Adaptive variable structure controller of redundant robots with mobile/fixed obstacles avoidance

In this paper, a variable structure adaptive controller is proposed for redundant robot manipulators constrained by moving obstacles. The main objective of the controller is to force the model states of the robot to track those of a chosen reference model. In addition, the controller is designed directly in Cartesian space and no knowledge on the dynamic model is needed, except its structure. The parameters of the controller are adapted using adaptive laws obtained via Lyapunov stability analysis of the closed loop. The performances of the proposed controller are evaluated using a 3 DOF robot manipulator evolving in a vertical plane constrained by a mobile obstacle. The obtained results show its effectiveness compared to other tested variable structure controllers.     

Obituary

A model-based decentralized adaptive controller is proposed for multiple manipulators in a class of co-operations called holonomic co-operations, in which the manipulators are holonomically constrained. In this controller we calculate the control input and estimate unknown robotic parameters in individual state spaces of the manipulators instead of that of the whole system. Consequently, no coordinator exists in the system and the control architecture is decentralized. The model-based adaptive algorithm is used to estimate the unknown or uncertain parameters. It is proven that a Lyapunov function guarantees asymptotic convergence of tracking errors of both the trajectory and interactive force among the manipulators. We also discuss issues regarding communication among the robots according to motion constraints associated with the co-operation. Finally, the validity and performance of the proposed method are verified by simulations on two six-DOF manipulators.     

Model-based and neural-network-based adaptive control of two robotic arms manipulating an object with relative motion

In the study of constrained multiple robot control, the relative motion between the constraint object and the end effectors of manipulators are usually neglected in the literature. However, in many industrial applications, such as assembly and machining, the constraint object is required to move with respect to not only the world coordinates but also the end effectors of the robotic arms. In this paper, dynamic modelling of two robotic arms manipulating an object with relative motion is presented first, then a model-based adaptive controller and a model-free neural network controller are developed. Both controllers guarantee the asymptotic tracking of the constraint object and the boundedness of the constraint force. Asymptotic convergence of the constraint force can also be achieved under certain conditions. Simulation studies are conducted to verify the effectiveness of the approaches.     

Modeling and control of two constrained manipulators

The coordinated control of two manipulators in the presence of environment constraints is studied in this paper. Such a control method is needed in applications in which the two manipulators grasp a common object whose motion is constrained by environments. The two manipulators are not only constrained with each other, but also constrained by the environment in their workspace. It is realized that the motion and constraint equations obtained directly from mechanics are not suitable for the control purpose. A set of equivalent equations are derived, which are in the standard form of the nonlinear system representation with clear state equations and output equations. A nonlinear feedback is found which exactly linearizes and decouples the dynamic nonlinear system of the two constrained manipulators. The coordinated controller design is then carried out based on the linearized system by using linear system theory.     

Sliding-mode motion/force control of constrained robots

A sliding-mode controller is proposed for the simultaneous position and force control of constrained robot manipulators with parametric uncertainties. Based on this controller, the trajectories of the closed-loop system can reach a stable sliding surface in finite time. Under this condition, the asymptotic convergence of the motion error and force error can be successfully ensured with improved results compared to previous studies     

参数未知的多个操作器的一种自适应控制算法

在多个操作器的内力与运动的控制中,内力控制是主要的挑战.在假设接触模型为点接触的前提下,讨论了参数未知的多个操作器内力的自适应控制.先给出多个操作器所成系统具有的一些特性;其次导出了内力误差与动力学参数间的关系;最后在条件PE(persistent-exciting)不成立的情况下,给出了一种保证内力收敛于零的复合控制.由理论证明及仿真实验可知,本文给出的控制器既使被操作的物体运动轨迹渐进跟踪期望的运动轨迹,又使作用在物体上的内力渐进跟踪期望的内力.     

六自由度机械臂约束预测控制系统的设计

本文提出了一种基于约束预测控制的机械臂实时运动控制方法.该控制方法分为两层,分别设计了约束预测控制器和跟踪控制器.其中,约束预测控制器在考虑系统物理约束的条件下,在线为跟踪控制器生成参考轨迹;跟踪控制器采用最优反馈控制律,使机械臂沿参考轨迹运动.为了简化控制器的设计和在线求解,本文采用输入输出线性化的方式简化机械臂动力学模型.同时,为了克服扰动,在约束预测控制器中引入前馈策略,提出了带前馈一反馈控制结构的预测控制设计.因此,本文设计的控制器可以使机械臂在满足物理约束的条件下快速稳定地跟踪到目标位置.通过在PUMA560机理模型上进行仿真实验,验证了预测控制算法的可行性和有效性.