Dynamic two arm hybrid position/force control

A useful two arm robot system will not only need to cooperatively manipulate the same object, but also need the ability for external force control. As an example, assume two robots are building a space station, which requires them to connect a structure to a partially built space station. This implies that they need to cooperatively move the object to the desired position, and then apply a force to connect it. Therefore, two arm hybrid position/force control is necessary. To accomplish this task quickly and accurately the dynamics of arm 1, arm 2, and the object must be taken into account. The external and internal forces must be clearly defined to be used in the servo control loop. There are several ways to choose the internal force: zero internal force, arbitrary force distribution, minimizing object strain energy, and minimizing the total torque. An example is shown to illustrate the trade-offs. A controller is presented which incorporates the dynamics of each arm, the dynamics of the object, and servos on the internal and external force. Experimental results show that servoing on the internal force will reduce the force error significantly.     

On the invariance of the hybrid position/force control

Recently, the noninvariance nature of the Raibert and Craig hybrid control scheme, based on the work of Mason and others, has been pointed out. In fact, on the basis of the screw theory, Lipkin and Duffy demonstrated that the selection of the position and force controlled degrees of freedom in the Raibert and Craig scheme, may give wrong results if a translation or change in unit length of the coordinate frame is performed. A general theoretical solution to this problem, called kinestatic filtering, has been given by Lipkin and Duffy. In this paper, the two approaches are summarized and discussed. First, the conditions in which the Mason filtering technique fails are determined and, second, the situations where the Lipkin and Duffy approach cannot be applied owing to degeneracy of the twist and wrench spaces, are reported. As a consequence of this analysis, a new invariant kinestatic filtering method is proposed. The method here presented is based on the original Mason approach and requires the definition of a task-dependent filter based on the knowledge of the position of the compliant frame. Examples are presented and discussed.     

A position/force control for a robot finger with soft tip and uncertain kinematics

We consider the position and force regulation problem for a soft tip robot finger in contact with a rigid surface under kinematic and dynamic parametric uncertainties. The reproducing force is assumed to be related to the displacement through a nonlinear function whose characteristics are unknown, but both the actual displacement and force can be directly measured. Kinematic uncertainties concern the rigid surface orientation and the contact point location. Kinematic parameters involved in the contact point location concern the length from the last joint to the contact point and the rest of the link lengths in the general case. An adaptive controller with a composite update parameter law is proposed, and the asymptotic stability of the force and estimated position errors under dynamic and kinematic uncertainties is shown for the planar case. Simulation results for a three‐degrees‐of‐freedom planar robotic finger are presented. © 2002 Wiley Periodicals, Inc.     

Symmetry position/force hybrid control for cooperative object transportation using multiple humanoid robots

A symmetry position/force hybrid control framework for cooperative object transportation tasks with multiple humanoid robots is proposed in this paper. In a leader-follower type cooperation, follower robots plan their biped gaits based on the forces generated at their hands after a leader robot moves. Therefore, if the leader robot moves fast (rapidly pulls or pushes the carried object), some of the follower humanoid robots may lose their balance and fall down. The symmetry type cooperation discussed in this paper solves this problem because it enables all humanoid robots to move synchronously. The proposed framework is verified by dynamic simulations.     

Adaptive neuro fuzzy based hybrid force/position control for an industrial robot manipulator

In this paper an ANFIS-PD+I (AFSPD+I) based hybrid force/position controller has been proposed which works effectively with unspecified robot dynamics in the presence of external disturbances. A constraint is put to limit the movement of manipulator in XY Cartesian coordinates. The validity of the proposed controller has been tested using a 6-degree of freedom PUMA robot manipulator. The performance comparison have been done with the fuzzy proportional derivative plus integral, fuzzy proportional integral derivative and conventional proportional integral derivative controllers subjected to the same data set with proposed controller. The projected AFSPD+I controller adhered to the desired path closer and smoother than the other mentioned controllers.     

Mimo and siso self-tuning hybrid position/force control of robotic manipulators

This article presents a new approach for the hybrid position/force control of a manipulator by using self-tuning regulators (STR). For this purpose, the discrete-time stochastic multi-input multi-output (MIMO) and single-input single-output (SISO) models are introduced. The MIMO model's output vector has the positions and velocities of the gripper expressed in the world (xyz) coordinate system as the components. The SISO model outputs are the hybrid errors consisting of the derivatives of the position and force errors at the joints. The inputs of both models are the joint torques. The unknown parameters of those models can be calculated recursively on-line by the square-root estimation algorithm (SQR). An adaptive MIMO and SISO self-tuning type controllers are then designed by minimizing the expected value of a quadratic criterion. This performance index penalizes the deviations of the actual position and force path of the gripper from the desired values expressed in the Cartesian coordinate system. An integrating effect is also included in the performance index to remove the steady-state errors. Digital simulation results using the parameter estimation and the control algorithms are presented and the performances of those two controllers are discussed. © 1996 John Wiley & Sons, Inc.     

Dynamic hybrid position/force control of a two degree-of-freedom flexible manipulator

In this article, a method is proposed whereby both contact force exerted by a flexible manipulator and position of end-effector while in contact with a surface are controlled. We approximate elastic deformations by means of B-spline functions and derive dynamic equations of joint angles, vibration of the flexible link, and constraint force. A controller for the hybrid position/force control of the flexible manipulator is designed on the basis of the singular perturbation method. Simulation results confirm that the controller performs remarkably well. © 3994 John Wiley & Sons, Inc.     

Vision guided manipulation for planetary robotics — position control

Manipulation systems for planetary exploration operate under severe restrictions. They need to integrate vision and manipulation to achieve the reliability, safety, and predictability required of expensive systems operating on remote planets. They also must operate on very modest hardware that is shared with many other systems, and must operate without human intervention.Typically such systems employ calibrated stereo cameras and calibrated manipulators to achieve precision of the order of one centimeter with respect to instrument placement activities. This paper presents three complementary approaches to vision guided manipulation designed to robustly achieve high precision in manipulation. These approaches are described and compared, both in simulation and on hardware.In situ estimation and adaptation of the manipulator and/or camera models in these methods account for changes in the system configuration, thus ensuring consistent precision for the life of the mission. All the three methods provide several-fold increases in accuracy of manipulator positioning over the standard flight approach.     

Bilateral parallel force/position teleoperation control

The extension of parallel force/position control to teleoperation systems is considered in this article. In the proposed four‐channel bilateral controller, higher priority is granted to position control at the master side and to force control at the slave side. The primary goal of this control architecture is the enhancement of force and position tracking performance in the presence of uncertainties in the system and environment. The stability and performance of the proposed controller is investigated by analyzing the three decoupled single‐degree‐of‐freedom systems obtained from decoupling and projecting the closed‐loop system dynamics onto the slave task‐space orthogonal directions. Experimental results demonstrate significant improvement in transparency. © 2002 Wiley Periodicals, Inc.     

Fuzzy-neuro position/force control for robotic manipulators with uncertainties

The performance of a controller for robot force tracking is affected by the uncertainties in both the robot dynamic model and the environmental stiffness. This paper aims to improve the controller’s robustness by applying the neural network to compensate for the uncertainties of the robot model at the input trajectory level rather than at the joint torque level. A self-adaptive fuzzy controller is introduced for robotic manipulator position/force control. Simulation results based on a two-degrees of freedom robot show that highly robust position/force tracking can be achieved, despite the existence of large uncertainties in the robot model.     

A hybrid receding-horizon control scheme for nonlinear systems

A hybrid receding-horizon control scheme for nonlinear discrete-time systems is proposed. Whereas a set of optimal feedback control functions is defined at the continuous level, a discrete-event controller chooses the best control action, depending on the current conditions of a plant and on possible external events. Such a two-level scheme is embedded in the structure of abstract hybrid systems, thus making it possible to prove a new asymptotic stability result for the hybrid receding-horizon control approach.     

A hybrid model predictive control strategy for nonlinear plant-wide control

A plant-wide control strategy based on integrating linear model predictive control (LMPC) and nonlinear model predictive control (NMPC) is proposed. The hybrid method is applicable to plants that can be decomposed into approximately linear subsystems and highly nonlinear subsystems that interact via mass and energy flows. LMPC is applied to the linear subsystems and NMPC is applied to the nonlinear subsystems. A simple controller coordination strategy that counteracts interaction effects is proposed for the case of one linear subsystem and one nonlinear subsystem. A reactor/separator process with recycle is used to compare the hybrid method to conventional LMPC and NMPC techniques.     

Knowledge driven robotics for kitting applications

This article presents a newly developed knowledge methodology/model that was designed to support the IEEE Robotics and Automation Society’s Ontologies for Robotics and Automation Working Group. This methodology/model allows for the creation of systems that demonstrate flexibility, agility, and the ability to be rapidly re-tasked. The methodology/model will be illustrated through a case study in the area of robotic kit building. Through this case study, the knowledge model will be presented, and automatic tools for optimizing the knowledge representation for planning systems and execution systems will be discussed.     

Continuous shape-grinding experiment based on model-independent force/position hybrid control method with on-line spline approximation

Based on the analysis of interaction between manipulator’s hand and working object, a model representing the constrained dynamics of robot is first discussed. The constraint forces are expressed by algebraic function of states, input generalized forces, and constraint condition, and then a decoupling control method of force and position of manipulator’s hand tip is proposed. In order to give the grinding system the ability to adapt to any object shape being changed by the grinding, estimating function of the constraint condition in real time for the adaptive force/position control was added, which is indispensable for the proposed method without using force sensor. This paper explores whether the performance of the proposed controller is independent of alloy work-piece models or not. The experimental result is shown to verify the feature of the decoupling control of force and position of the tip.     

Adaptive hybrid force/position control of robot manipulators using an adaptive force estimator in the presence of parametric uncertainty

This study is devoted to sensorless adaptive force/position control of robot manipulators using a position-based adaptive force estimator (AFE) and a force-based adaptive environment compliance estimator. Unlike the other sensorless method in force control that uses disturbance observer and needs an accurate model of the manipulator, in this method, the unknown parameters of the robot can be estimated along with the force control. Even more, the environment compliance can be estimated simultaneously to achieve tracking force control. In fact, this study deals with three challenging problems: No force sensor is used, environment stiffness is unknown, and some parametric uncertainties exist in the robot model. A theorem offers control laws and updating laws for two control loops. In the inner loop, AFE estimates the exerted force, and then, the force control law in the outer loop modifies the desired trajectory of the manipulator for the adaptive tracking loop. Besides, an updating law updates the estimated compliance to provide an accurate tracking force control. Some experimental results of a PHANToM Premium robot are provided to validate the proposed scheme. In addition, some simulations are presented that verify the performance of the controller for different situations in interaction.     

Motion/force control scheme for electrically driven cooperative multiple mobile manipulators

This paper presents the motion and force control problem of rigid-link electrically driven cooperative mobile manipulators handling a rigid object. Although, the motion/force control problem of cooperative mobile manipulators has been enthusiastically studied. But there is little research on the motion/force control of electrically driven cooperative mobile manipulators. Due to the inclusion of the actuator dynamics with the manipulator’s dynamics, the controller exhibits some important characteristics. For the electromechanical system, we have designed a novel controller at the dynamic level as well as at the actuator level. In the proposed control scheme, at the dynamic level, uncertain non-linear mechanical dynamics is approximated with a hybrid controller containing model-based control scheme combined with model-free neural network based control scheme together with an adaptive bound. The adaptive bound is used to suppress the effects of external disturbances, friction terms, and reconstruction error of the neural network. At the actuator level, for the approximation of the unknown electrical dynamics, the model-free neural network is utilized. The developed control scheme provides that the position tracking errors, as well as the internal force, converge to the desired levels. Additionally, direct current motors are also controlled in such a way that the desired currents and torques can be attained. In order to make the overall system to be asymptotically stable, online learning of the weights and the parameter adaptation of the parameters is utilized in the Lyapunov function. The superiority of the developed control method is carried out with the numerical simulation results and its superior robustness is shown in a comparative manner.     

Stability of hybrid position and force control for robotic manipulator with kinematics and dynamics uncertainties

Most research so far on hybrid position and force control laws of robotic manipulator has assumed that knowledge of kinematics is known exactly. In the presence of modeling errors, it is unknown whether the stability of the constrained robot system can still be ensured. In this paper, stability and setpoint control problems of constrained robot with kinematics and dynamics uncertainties are formulated and solved. We shall show that the manipulator end-effector's motion is asymptotically stable even in the presence of such uncertainties.     

A CDM-backstepping control with nonlinear observer for electrically driven robot manipulator

This paper presents a CDM-backstepping strategy for motion control of Electrically-driven manipulator under the conditions of uncertainty and the action of external disturbance, while incorporating a nonlinear observer. Based on this model, a systematic analysis and design algorithm is developed to deal with stabilization and trajectory tracking of elbow robot, one feature of this work is employing the backstepping observer to achieve the exponential stability with position and velocity estimations. The results of computer simulations demonstrate that accurate and robust motion control can be achieved by using the proposed approach.     

A learning algorithm for improved hybrid force control of robotarms

An investigation on the hybrid force control of robot arms by learning is presented. A well-known force control scheme based on feedback linearization is used to build up an algorithm which improves, trial by trial, force and position tracking over a finite time interval. Differently from other published learning control schemes, the proposed algorithm does not rely on high gain feedback. Robustness and convergence in spite of sufficiently small system parameter uncertainties and disturbances is proven by means of the contraction mapping principle     

基于降阶位置/力模型的机器人神经网络控制

双足机器人的双脚支撑期是实现其步行运动的重要过程,然而耦合的位置/力控制难以保证其稳定平滑运动.本文提出了一种基于降阶位置/力模型的机器人控制策略,整合了位置控制子空间模型和力控制子空间模型,通过模型降阶减小了控制器设计的复杂度,并采用神经网络自适应控制方法综合多控制目标,实现了双足机器人的平滑稳定控制并有效地抑制了系统外扰和参数不确定性的影响.最后,仿真算法验证了该控制方法和模型的有效性.