Modified Position-Force Control for a Manipulator Geometrically Constrained by Round Obstacles

In this paper, the problem of robust control of robot manipulators which perform noncontact and contact tasks is considered. Using the hybrid control strategy, two variable structure control algorithms are developed in task space. One of the algorithms is developed for control of robot manipulators in constrained motion; the other is proposed for control of manipulators in unconstrained and constrained motion. Computer simulation results are presented for illustration. >

A principle of ‘joint‐space orthogonalization’ is proposed as an extended notion of hybrid (force and position) control for robot manipulators under geometric constraints. The principle realizes the hybrid control in a strict sense by letting position feedback signals be orthogonal in joint space to the contact force vector whose components exert at corresponding joints. This orthogonalization is executed via a projection matrix computed in real‐time from a Jacobian matrix of the constraint equation in joint coordinates.To show the important role of the principle in control of robot manipulators, two basic set‐point control problems are analysed. One is a hybrid PID control problem for robot manipulators under geometric endpoint constraint and another is a coordinated control problem of two arms. It is shown that passivity properties of residual dynamics of robots follow from the introduction of a quasi‐natural potential and the joint‐space orthogonalization. Various stability problems of PID‐type feedback control schemes without compensating for the gravity force and with or without use of a force sensor are discussed from passivity properties of robot dynamics with the aid of the hyper‐stability theory.

A learning control scheme for robot manipulators whose endpoint is moving under geometrical constraints on a surface is proposed. In this scheme, the input torque command is composed of two signals updated separately at every trial by different laws. One is updated by the angular velocity error vector which is projected to the tangent plane of the constraint surface in joint space. The other is updated by the magnitude of contact force error at the endpoint. A theoretical proof of the uniform boundedness of velocity and position trajectories and the convergence of these to their desired ones is given, together with the convergence of force errors. Computer simulation remits by using a 3-DOF manipulator are presented to demonstrate the effectiveness of the proposed method. >

In the impedance control scheme in robotic manipulation applications, the interaction force can be indirectly controlled by acting on the manipulator reference position trajectory. However, the reference position depends on the environment parameters, like location and stiffness, which in practice are not exactly known. To deal with those uncertainties, this paper presents a fuzzy adaptive approach which determines the appropriate reference (target) positions to an impedance controller in order to obtain the desired force/position trajectory. Moreover, only an estimate of the environment location is necessary, and so the fuzzy adaptive controller corrects this estimated position based on the force error. The fuzzy adaptive controller is a kind of self organizing controller, which consists of a direct fuzzy controller and an adaptation mechanism, (the fuzzy inverse model) which adjusts the membership functions of the consequent parts of the direct fuzzy controller. To validate the proposed control scheme, simulation results with a two degree of freedom robot manipulator are presented where it is shown a good force/tracking performance with uncertainties on the environment location and on the stiffness value.

In this paper we experimentally study recently developed control algorithms for robotic manipulators with input-output delays. Our previous work demonstrates that the scattering transformation can be used as an effective tool to address delay instability problems in robot control. Specifically, the classical PI controller can be modified to regulate the robotic manipulator in the presence of constant and time-varying input-output delays by using the scattering transformation. These results are validated in this paper through experiments on the PHANToM Omni device. Furthermore, by appropriately compensating for the gravitational and frictional forces, a model of the PHANToM Omni device, suitable for control implementation, is also developed. The implementation of the control algorithms is discussed, and experiments are conducted to demonstrate the efficacy of the proposed results.

Real-time motion planning and control of multi-joint robot manipulators are studied using neural networks. The proposed neural network approach consists of two modules: one for real-time collision-free motion planning, the other for real-time fine control of the robot manipulators. The motion planning module is a biologically inspired, parallel connected, topologically organised neural network, where each neuron is characterised by a shunting equation, whose state space is the robot configuration joint space. This module is capable of planning a real-time optimal path for robot manipulators through the dynamic activity landscape of the neural network. The motion control module involves of a feedforward neural network together with a traditional PD feedback loop. It is capable of achieving real-time fine motion control of robot manipulators under significant uncertainties and without any prior knowledge of the robot dynamics. This module can quickly compensate sudden changes in the robot dynamics. The real-time fine control of robot manipulators is achieved through the online learning of the neural network. Both the motion planning and motion control modules are computationally efficient, and their global stabilities are proved using Lyapunov stability theory.

The learning control is applied to hybrid force and position control of robot manipulators. When the geometry and position of a constraint surface is known, the hybrid force and position controller and the feedforward compensator can be designed in the constraint coordinates. When the operation is periodic, the learning hybrid force and position control enhance the control performance as the feedforward compensator is updated in each cycle by the force and position error in the preceding trials. This scheme is proved to be asymptotically stable. A two degree of freedom SCARA-type direct-drive robot manipulator is used to test the learning hybrid force and position control. The deburring tool mounted on the upper link of the robot could follow a flat, tilted flat, and curved 1/4 " aluminum plate with a desired contact force of 10 N (within the root-mean-square force error of 1.95 N) and with a desired tangential velocity. The experiments confirmed the effectiveness of the learning hybrid force and position controller.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this paper, a family of hybrid control algorithms is presented; where it is merged a free camera-calibration image-based control scheme and a direct force controller, both with the same priority level. The aim of this generalised hybrid controller is to regulate the robot-environment interaction into a two-dimensional task-space. The design of the proposed control structure takes into account most of the dynamic effects present in robot manipulators whose inputs are torque signals. As examples of this generalised structure of hybrid force/vision controllers, a linear proportional-derivative structure and a nonlinear proportional-derivative one (based on the hyperbolic tangent function) are presented. The corresponding stability analysis, using Lyapunov's direct method and invariance theory, is performed to proof the asymptotic stability of the equilibrium vector of the closed-loop system. Experimental tests of the control scheme are presented and a suitable performance is observed in all the cases. Unlike most of the previously presented hybrid schemes, the control structure proposed herein achieves soft contact forces without overshoots, fast convergence of force and position error signals, robustness of the controller in the face of some uncertainties (such as camera rotation), and safe operation of the robot actuators when saturating functions (non-linear case) are used in the mathematical structure. This is one of the first works to propose a generalized structure of hybrid force/vision control that includes a closed loop stability analysis for torque-driven robot manipulators.

A robust adaptive control strategy based on a sliding mode is designed for the motion synchronization of multiple flexible-joint robotic manipulators system with time delay. First, synchronization motion of the system is considered as constraint conditions and is simplified to the position/force control. Then the time delay is considered as a first-order and compensated by the controller. By utilizing a sliding mode consisting of position error and force error, an adaptive controller based on back stepping is designed to drive the closed-loop system states to reach the adopted sliding model surface, thus can guarantee the asymptotic convergence of the position error and force error. For uncertainties of the manipulator parameters, joint stiffness and actuator inertia, an adaptive control law and robust control is applied to the online parameter estimation and bound compensation respectively. Finally, the simulation results show the feasibility of the proposed method.

Further results on adaptive iterative learning control of robot manipulators

Robotics has been a paradigm for science in the last few decades. At first, scientists’ efforts were devoted to the solution of the problem of planning and control of themotion of robot manipulators. However, the motion control of robot manipulators in unstructured environments is today an attractive scientific problem. An interesting solution for motion control is the use of sensor information, such as computer vision, in the system’s feedback. Several works and tools have been developed in recent years in this field (Corke; 2005), (Chaumette and Hutchinson; 2006). The more typical approaches consider visual perception for servoing and for the so called look and move (Hutchinson et al.; 1996). Visual servoing (Kelly et al.; 2000) can be classified into two approaches: camera-in-hand or camera-to-hand (Flandin et al.; 2000). In camera-to-hand robotic systems, multiple cameras or a single camera fixed in the worldcoordinate frame capture images of both, the robot and its environment. The tracking of the object with visual feedback can be made in 2D or 3D. An interesting solution for the visual servoing of camera-in hand robot manipulators in 2D can be found in e. g. (Bonfe et al.; 2002) and (Hernandez et al.; 2008) where stability demonstration of a decoupled controller have been presented. For 3D tracking some solutions reported are look and move controller, with one camera (Sim et al.; 2002) or more than one camera (Xie et al.; 2005). A 3D visual servoing with stability analysis in continuous time is presented by (Hernandez et al.; 2008a) and (Kelly et al.; 2006) present a direct visual servoing with transpose Jacobian control technique for regulation of robot manipulators in the 3D Cartesian space. In a similar vein (Enescu et al.; 2006) present mobile robot navigation for person tracking using a stereo head-camera. In this chapter, we consider the control problem of camera-in-hand robot manipulators in 2D and 3D. In both cases only one camera is mounted on the robot’s arm, which supplies visual information of the environment, with the aim of moving the manipulator by maintaining the image of the tracked object (a sphere) in the centre of the image plane, despite the possible movements of the object. In the 3D control the constant radius is used as a feature too. In this work, the proposed control system considers two loops in cascade, an internal loop solving the robots’ joint control, and an external loop implementing a dynamic look and move visual controller. A stability analysis in discrete time is developed under the conditions that it is possible to approximate the dynamic effect of the internal loop as an external loop time delay (Corke; 1996) and (Bonfe et al.; 2002). The more classic presentations of servovisual control are velocity controllers, based in the term feature Jacobian,(Chaumette and Hutchinson; 2006) 2

For the position/force control problem of multiple manipulators grasping a rigid object, an adaptive position/force control strategy is proposed based on a new sliding mode. With the Jacobian matrix, the joint space model for multiple manipulators can be converted into the task space model. In the process of controller design, we add the force error term into the sliding mode and design the position/force controller directly instead of decoupling the position and the force. The new position/force controller can make the force error converge to zero asymptotically, but the conventional controller can only guarantee it bounded. Considering the uncertainties of the dynamic model, adaptive control is introduced. The Lyapunov stability analysis demonstrates that the proposed strategy can guarantee the asymptotic convergence of position and force error. Simulation results of the two planar two-link manipulators are presented to validate the effectiveness and superiority of the proposed method in comparison with the conventional controller.

This paper studies the problem of modeling and control of multiple cooperative underactuated manipulators handling a rigid object. We reveal holonomic property of such a system by presenting a smooth feedback controller subject to two conditions: 1) there are not fewer active joints than the degrees of freedom of the object; and 2) the Jacobian matrix with respect to passive joints is not singular. This controller is an extension of the PD plus gravity compensation scheme and its asymptotic stability is guaranteed by the LaSalle theorem. Furthermore, we develop a trajectory tracking controller that yields asymptotic convergence of position errors and bounded interaction forces simultaneously. The performance of the proposed controllers has been investigated by simulations on two 6-DOF underactuated manipulators and by experiments on the cooperative underactuated manipulator system developed at CMU.

Polynomial family of PD-type controllers for robot manipulators

Dynamic control of soft robotic manipulators is a challenging field still in its nascent stages. Modeling is still a major hurdle due to its high dimensional nonlinear dynamic properties. Even if accurate models of these high dimensional nonlinear systems are available, the computational burden of the models or sensory requirements poses problems for robust and stable control. This letter addresses these two problems by using a data-driven model that uses only mechanical feedback for stable control of the soft robotic manipulator. Using a learning-based open loop dynamic controller, we investigate the self-stabilizing behavior that can be obtained from the complex dynamics of a soft manipulator. Experimental findings illustrate the advantage of using open-loop dynamic controllers for accurate self-stabilizing control of soft robotic manipulators without any sensory feedback.