冗余驱动并联机械手的混合位置/力自适应控制

针对冗余驱动并联机构研究一种自适应的混合位置/力控制算法.基于并联机构中约束 子流形的几何性质,将冗余驱动并联机构的逆动力学自然投影到位形空间和约束力空间.基于投 影方程,提出一种统一的具有渐进稳定性的自适应混合位置/力控制算法.采用最小二范数准则 求解冗余解问题,实现了实际驱动关节力矩的优化.仿真结果验证了控制方法的有效性.     

Robust motion/force control of nonholonomic mobile manipulators using hybrid joints

In this paper, robust force/motion control strategies are presented for mobile manipulators under both holonomic and non-holonomic constraints in the presence of uncertainties and disturbances. The proposed control strategies guarantee that the system motion converges to the desired manifold with prescribed performance and constraint force control is developed using the passivity of hybrid joints rather than force feedback control. Experiment results validate that not only the states of the system asymptotically converge to the desired trajectory, but also the constraint force asymptotically converges to the desired force.     

On cooperative manipulation of dynamic objects

This paper proposes a decentralized position/internal force hybrid control approach for multiple robot manipulators to cooperatively manipulate an unknown dynamic object. In this approach, each autonomous robot has its own controller and uses its own sensor information in performing the fast cooperation. This approach eliminates a lot of information communications between each robot and reduces numerous computations. The influences of the position and the internal force estimation errors to the overall control system is analyzed. A cooperative identification method for each autonomous robot to identify the object's complex dynamics, cooperatively, is presented. In addition, the trade-off between the unilateral force constraint and the robots' position response is studied. Experiments show the effectiveness of this control approach.     

Direct adaptive impedance control of robot manipulators

This article presents an adaptive scheme for controlling the end-effector impedance of robot manipulators. The proposed control system consists of three subsystems: a simple “filter” that characterizes the desired dynamic relationship between the end-effector position error and the end-effector/environment contact force, an adaptive controller that produces the Cartesian-space control input required to provide this desired dynamic relationship, and an algorithm for mapping the Cartesian-space control input to a physically realizable joint-space control torque. The controller does not require knowledge of either the structure or the parameter values of the robot dynamics and is implemented without calculation of the robot inverse kinematic transformation. As a result, the scheme represents a general and computationally efficient approach to controlling the impedance of both nonredundant and redundant manipulators. Furthermore, the method can be applied directly to trajectory tracking in free-space motion by removing the impedance filter. Computer simulation results are given for a planar four degree-of-freedom redundant robot under adaptive impedance control. These results demonstrate that accurate end-effector impedance control and effective redundancy utilization can be achieved simultaneously by using the proposed controller.     

Adaptive force and position control of manipulators

The article presents simple methods for the design of adaptive force and position controllers for robot manipulators within the hybrid control architecture. The force controller is composed of an adaptive PID feedback controller, an auxiliary signal, and a force feedforward term, and achieves tracking of desired force setpoints in the constraint directions. The position controller consists of adaptive feedback and feedforward controllers as well as an auxiliary signal, and accomplishes tracking of desired position trajectories in the free directions. The controllers are capable of compensating for dynamic cross-couplings that exist between the position and force control loops in the hybrid control architecture. The adaptive controllers do not require knowledge of the complex dynamic model or parameter values of the manipulator or the environment. The proposed control schemes are computationally fast and suitable for implementation in online control with high sampling rates. The methods are applied to a two-link manipulator for simultaneous force and position control. Simulation results confirm that the adaptive controllers perform remarkably well under different conditions.     

Dynamic state feedback control of two cooperative manipulators

Dynamic coordinated control of two robot manipulators that rigidly grasp a common object is studied. A dynamic coordinated control model for the two manipulators is derived that is suitable for system analysis and design in state space. The model takes into account kinematic and dynamic constraints between the two manipulators, and is explicitly described by non-linear state equtions and non-linear output equations in the state space. Since coordinated control requires the control of forces applied to the object by manipulators, the output equations include both position components and force components. While robotic systems with position outputs can be linearized using a static state feedback, systems with force outputs, such as the present two robot system, require a dynamic non-linear state feedback for exact linearization. By using dynamic non-linear feedback, coordinated control of two robotic manipulators is converted into a control problem of linear systems.     

基于迭代学习的机械手操作空间力/位置混合控制算法

基于对常规机械手操作空间力/位置混合控制算法的简单回顾,及对该算法所遇到 困难的分析,提出了一种基于迭代学习的机械手操作空间力/位置混合控制算法,来改善机械 手同高刚度环境接触时,机械手力/位置混合控制的动态控制性能．给出了学习算法的收敛条 件及其证明.实验表明该算法具有快速的收敛性,能达到很高的力/位置动态控制精度.     

On the use of velocity feedback in hybrid force/velocity control of industrial manipulators

This paper deals with the implementation of a hybrid force/velocity control for contour tracking tasks of unknown objects performed by industrial robot manipulators. In particular, we address the problem of the configuration dependent dynamics of the manipulator in constrained motion and we propose a method, based on inertial and stiffness ellipses, that allows to predict the presence of the consequent force oscillations. We also show that these oscillations can be highly reduced by employing an additional normal velocity feedback loop. A large number of experimental results obtained with a two degrees-of-freedom SCARA robot are shown.     

基于迭代学习的机械手操作空间力/位置混合控制算法

基于对常规机械手操作空间力／位置混合控制算法的简单回顾，及对该算法所遇到困难的分析，提出了一种基于迭代学习的机械手操作空间力／位置混合控制算法，来改善机械手同高刚度环境接触时，机械手力／位置混合控制的动态控制性能．给出了学习算法的收敛条件及其证明．实验表明该算法具有快速的收敛性，能达到很高的力／位置动态控制精度．     

Feedback stabilization and tracking of constrained robots

Mathematical models for constrained robot dynamics, incorporating the effects of constraint force required to maintain satisfaction of the constraints, are used to develop explicit conditions for stabilization and tracking using feedback. The control structure allows feedback of generalized robot displacements, velocities, and the constraint forces. Global conditions for tracking, based on a modified computed-torque controller and local conditions for feedback stabilization, using a linear controller, are presented. The framework is also used to investigate the closed-loop properties if there are force disturbances, dynamics in the force feedback loops, or uncertainty in the constraint functions     

Robust Hybrid Control of Constrained Robot Manipulators via Decomposed Equations

Basing on a constraint Jacobian induced orthogonal decomposition of the task space and by requiring the force controller to be orthogonal to the constraint manifold, the dynamics of the constrained robots under hybrid control is decomposed into a set of two equations. One describes the motion of robots moving on the constraint manifold, while the other relates the constraint force with the hybrid controller. This decomposition does not require the solution of the constraint equation in partition form. In this setting, the hybrid control of constrained robots can be essentially reduced to robust stabilization of uncertain nonlinear systems whose uncertainties do not satisfy the matching condition. A continuous version of the sliding-mode controller (from Khalil [12]) is employed to design a position controller. The force controller is designed as a proportional force error feedback of high gain type. The coordination of the position controller and the force controller is shown to achieve ultimately bounded position and force tracking with tunable accuracy. Moreover, an estimate of the domain of attraction is provided for the motion on the constraint manifold. Simulation for a planar two-link robot constraining on an ellipse is given to show the effectiveness of a hybrid controller. In addition, the friction effect, viewed as external disturbance to the system, is also examined through simulations.     

Cooperative Control of a 3D Dual-Flexible-Arm Robot

This paper discusses cooperative control of a dual-flexible-arm robot to handle a rigid object in three-dimensional space. The proposed control scheme integrates hybrid position/force control and vibration suppression control. To derive the control scheme, kinematics and dynamics of the robot when it forms a closed kinematic chain is discussed. Kinematics is described using workspace force, velocity and position vectors, and hybrid position/force control is extended from that on dual-rigid-arm robots. Dynamics is derived from constraint conditions and the lumped-mass-spring model of the flexible robots and an object. The vibration suppression control is calculated from the deflections of the flexible links and the dynamics. Experiments on cooperative control are performed. The absolute positions/orientations and internal forces/moments are controlled using the robot, each arm of which has two flexible links, seven joints and a force/torque sensor. The results illustrate that the robot handled the rigid object damping links' vibration successfully in three-dimensional space.     

A globally stable state feedback controller for flexible joint robots

The paper addresses the problem of controlling the joints of a flexible joint robot with a state feedback controller and proposes a gradual way of extending such a controller towards the complete decoupling of the robot dynamics. The global asymptotic stability for the state feedback controller with gravity compensation is proven, followed by some theoretical remarks on its passivity properties. By proper parameterization, the proposed controller structure can implement a position, a stiffness or a torque controller. Experimental results on the DLR lightweight robots validate the method.     

Polynomial family of PD-type controllers for robot manipulators

This paper addresses the problem of position control for robot manipulators. A new polynomial family of PD-type controllers with gravity compensation for the global position of robots manipulators is presented. The previous results on the linear PD controller are extended to the proposed polynomial family. The classical PD controller can be found among this large class of controllers when its proportional gain is a diagonal matrix. The main contribution of this paper is to prove that the closed-loop system composed by full nonlinear robot dynamics and the proposed family of controllers is globally asymptotically stable in agreement with Lyapunov's direct method and LaSalle's invariance principle. Besides the theoretical results, a real-time experimental comparison is also presented to illustrate the performance of the proposed family with other well-known control algorithms such as PD and PID schemes on a three degrees of freedom direct-drive arm.     

Adaptive hybrid control of manipulators on uncertain flexible objects

This paper presents an adaptive hybrid control approach for a robot manipulator to interact with its flexible object. Because of its flexibility, the object dynamics influence the robot's control system, and since it is usually a distributed parameter system, the object dynamics as seen from the robot change when the robot moves. The problem becomes further complicated such that it is difficult to decompose the robot's position and contact force control loops. In this paper, we approximate the object's distributed parameter model into a lumped 'position state-varying' model. Then, by using the well-known nonlinear feedback compensation, we decompose the robot's control space into a position control subspace and object torque control subspace. We design the optimal state feedback for the position control loop and control the robot's contact force through controlling the resultant torque of the object. We use the model-reference simple adaptive control strategy to control the torque control loop. We also study the problem on how to select a reasonable reference model for this control loop. Experiments of a PUMA robot interacting with an aluminum beam show the effectiveness of our approach.     

Hybrid force and motion control of flexible joint parallel manipulators using inverse dynamics approach

An inverse dynamics control algorithm is developed for hybrid motion and contact force trajectory tracking control of flexible joint parallel manipulators. First, an open-tree structure is considered by the disconnection of adequate number of unactuated joints. The loop closure constraint equations are then included. Elimination of the joint reaction forces and the other intermediate variables yield a fourth-order relation between the actuator torques and the end-effector position and contact force variables, showing that the control torques do not have an instantaneous effect on the end-effector contact forces and accelerations because of the flexibility. The proposed control law provides simultaneous and asymptotically stable control of the end-effector contact forces and the motion along the constraint surfaces by utilizing the feedback of positions and velocities of the actuated joints and rotors. A two degree of freedom planar parallel manipulator is considered as an example to illustrate the effectiveness of the method.     

Indirect robot model learning for tracking control

Learning task-space tracking control on redundant robot manipulators is an important but difficult problem. A main difficulty is the non-uniqueness of the solution: a task-space trajectory has multiple joint-space trajectories associated, therefore averaging over non-convex solution space needs to be done if treated as a regression problem. A second class of difficulties arise for those robots when the physical model is either too complex or even not available. In this situation machine learning methods may be a suitable alternative to classical approaches. We propose a learning framework for tracking control that is applicable for underactuated or non-rigid robots where an analytical physical model of the robot is unavailable. The proposed framework builds on the insight that tracking problems are well defined in the joint task- and joint-space coordinates and consequently predictions can be obtained via local optimization. Physical experiments show that state-of-the art accuracy can be achieved in both online and offline tracking control learning. Furthermore, we show that the presented method is capable of controlling underactuated robot architectures as well.     

Feedback linearization of differential-algebraic systems and force and position control of manipulators

The question of realization and feedback linearization of a class of differential-algebraic system is considered. Based on nonlinear inversion of an input-output map, an analytical expression for the constraint force vector satisfying the algebraic constraints is derived. In this derivation, certain requirements on the relative degree of the output variables are relaxed. Using a new representation of the system in an extended state space, a control law is derived for the independent control of the chosen output variables satisfying algebraic constraints. These results are applied for the position and force control of robotic manipulators. Simulation results are presented for a three-link robotic arm with revolute joints. It is shown that in the closed-loop system, precise position and force trajectory control is accomplished in spite of uncertainty in the robot parameters.     

Position/torque hybrid control of a rigid,high-gear ratio quadruped robot

In this study, we introduce position/torque hybrid control for a newly designed rigid and high-gear ratio quadruped robot. The Experimental results indicated that the use of this control strategy allows the quadruped robot to maintain its stability while walking, and foot contact can be stabilized with only knee torque control and other joints are position controlled, without contact force feedback. Additionally, we suggested a smooth pattern connection method within or from preview control to the center of mass natural dynamics, and vice versa. We validated the proposed control strategies by conducting experiments.     

Stability analysis of robust adaptive hybrid position/force controller for robot manipulators using neural network with uncertainties

The aim of this paper is to design a robust adaptive neural network-based hybrid position/force control scheme for robot manipulators in the presence of model uncertainties and external disturbance. The feedforward neural network employed to learn a highly nonlinear function requires no preliminary learning. The control purposes are to achieve the stability in the sense of Lyapunov for desired interaction force between the end-effector and the environment and to regulate robot tip position in cartesian space. An adaptive compensator is also developed to eliminate the effect of disturbance term of neural network approximation error and external disturbance or unmodeled dynamics etc. A key feature of this compensator is that the prior information of the disturbance bound is not required. Finally, a comparative simulation study with a model-based robust control scheme for a two-link robot manipulator is presented.