Direct-drive Robot Manipulator Research Articles

Online identification of a robot using batch adaptive control

A technique to identify parameters of a robot dynamic model is presented in this paper. It is based on a batch adaptive control algorithm that, using a model of the robot dynamics, realizes a repetitive robot trajectory. The tracking error decreases due to a feedforward control input generated from the dynamic model. This feedforward input is computed after adaptation of the model parameters at the end of each trial. As the algorithm is effective, even if the model parameters are all initially set to zero, it can be used to recover their physical values. For that purpose, an identification experiment is carried out during which the robot is excited persistently. The estimation technique admits an online implementation without a delay between trials and is quite appealing for use in practice. Its merits are experimentally demonstrated on a spatial direct-drive robotic manipulator with 3 rotational joints.

Observer-corrector control design for robots with destabilizing unmodeled dynamics

Industrial robotic manipulators and other mechatronic systems often possess undesirable higher order dynamics exhibited in the form of resonance conditions which affect closed-loop stability when feedback control is applied. In this study, an alternative reduced-complexity control design strategy is presented. The fundamental idea of the proposed approach is to synthesize a substitute feedback signal which reflects the dominant dynamics essential for operation of the system subject to control but does not include the undesirable higher order dynamic effects. A unique arrangement of a band-limited state observer and a low-pass filter corrector is employed for this purpose, providing a mechanism to extract the dominant dynamics from the output of the system with minimal amplitude and phase distortion. The resulting synthetic signal is used as a controller input, effectively eliminating destabilizing effects of the undesirable higher order dynamics. As a result, the controller can be designed practically without taking the higher order dynamic effects into account, which allows for use of conventional control techniques, and translates into reduced modeling requirements, simplified controller design and shorter development time when compared to a complete dynamic analysis. The effectiveness of the proposed concept is demonstrated experimentally on motion control of a four-axis direct-drive robotic manipulator for automated pick-place operations in semiconductor manufacturing applications. It is concluded that the proposed control design strategy provides improved control performance, increased stability margin, and added robustness against variations in system parameters in comparison to common methods adopted currently in the engineering practice.

Robust adaptive trajectory tracking independent of models for robotic manipulators

AbstractThis article proposes a robust adaptive trajectory control scheme for robotic trajectory tracking under uncertainties. The control scheme is globally exponentially convergent without the knowledge of the robotic dynamics and is simple in structure with a small computation. It can make the trajectory error convergent to an arbitrary small region. Lyapunov approach is used to analyze the stability and the robustness of this control scheme. Experiments on a two‐link direct‐drive robotic manipulator verify the validity of the proposed control scheme. © 2001 John Wiley & Sons, Inc.

Supervisory Control for a Robot Teleoperation System: a Hybrid Control Approach

A supervisory controller for a robotic teleoperation system with a long time delay. The proposed low-level control structure was designed to control the position and the interaction force between the remote manipulator and the environment. Both the human operator model and the hand controller are considered in the local station. A 2 d.o.f. direct drive robot manipulator is modelled in the remote station. The supervisory control is designed using the theory of hybrid systems so that an additional system is incorporated, which evolves according to the occurrence of uncontrolled events like communication intenuptions, etc. Simulations have been carried out to show the system's performance.

On stabilization of gradient-based training strategies for computationally intelligent systems

Develops a training methodology for computationally intelligent systems utilizing gradient information in parameter updating. The devised scheme uses the first-order dynamic model of the training procedure and applies the variable structure systems approach to control the training dynamics. This results in an optimal selection of the learning rate, which is continually updated as prescribed by the adopted strategy. The parameter update rule is then mixed with the conventional error backpropagation method in a weighted average. The paper presents an analysis of the imposed dynamics, which is the response of the training dynamics driven solely by the inputs designed by a variable structure control approach. The analysis continues with the global stability proof of the mixed training methodology and the restrictions on the design parameters. The simulation studies presented are focused on the advantages of the proposed scheme with regards to the compensation of the adverse effects of the environmental disturbances and its capability to alleviate the inherently nonlinear behavior of the system under investigation. The performance of the scheme is compared with that of a conventional backpropagation. It is observed that the method presented is robust under noisy observations and time varying parameters due to the integration of gradient descent technique with variable structure systems methodology. In the application example studied, control of a two degrees of freedom direct-drive robotic manipulator is considered. A standard fuzzy system is chosen as the controller in which the adaptation is carried out only on the defuzzifier parameters.

Robot Control Optimization Using Reinforcement Learning

Conventional robot control schemes are basically model-based methods. However, exact modeling of robot dynamics poses considerable problems and faces various uncertainties in task execution. This paper proposes a reinforcement learning control approach for overcoming such drawbacks. An artificial neural network (ANN) serves as the learning structure, and an applied stochastic real-valued (SRV) unit as the learning method. Initially, force tracking control of a two-link robot arm is simulated to verify the control design. The simulation results confirm that even without information related to the robot dynamic model and environment states, operation rules for simultaneous controlling force and velocity are achievable by repetitive exploration. Hitherto, however, an acceptable performance has demanded many learning iterations and the learning speed proved too slow for practical applications. The approach herein, therefore, improves the tracking performance by combining a conventional controller with a reinforcement learning strategy. Experimental results demonstrate improved trajectory tracking performance of a two-link direct-drive robot manipulator using the proposed method.

Fuzzy Control Algorithm Eliminating Steady-state Position Errors of Robotic Manipulators

In order to eliminate position errors existing at the steady state in the motion control of robotic manipulators, a new fuzzy control algorithm is propeosed using three variables, position error, velocity error and integral of position errors as input variables of the fuzzy controller. Although the number of input variables of the fuzzy controller is increased from two to three, the number of fuzzy control rules is just increased by two. Three dimensional look-up table is used to reduce the computational time in real-time control, and a technique reducing the amount of necessary memory is introduced. Simulation and experimental studies show that the position errors at the steady state are decreased more than 90% compared to those of existing fuzzy controller when the proposed fuzzy controller is applied to the 2 axis direct drive SCARA robot manipulator.

Neural network controllers for robot manipulators application of damping neurons

_This paper presents an effective adaptive neural network feedback controller for force control of robot manipulators in an unknown environment by applying damping neurons which possess elastic-viscous properties. The unexpected overshooting and oscillation caused by the unknown and/or unmodeled dynamics of a robot manipulator and an environment can be decreased efficiently by the effect of the proposed damping neurons. Furthermore, a fuzzy controlled evaluation function is applied for the learning of the proposed neural network controller, so that the controller is able to adapt to the unknown environment more effectively. The effectiveness of the proposed neural network controller is evaluated by experiment with a 3 d.o.f. direct-drive planar robot manipulator.

Adaptive control for a class of direct drive robot manipulators

Direct drive robot technology has received considerable attention by the robotics research community given its potential for developing robots with superior positioning accuracy and repeatability characteristics. Direct drive robots do not have the compliance and backlash problems typically associated with geared robots. With the absence of gearing, however, the motors in direct drive robots must be able to produce considerably larger torques than the motors in a geared robot. As a result, the inductances of the motor windings are much larger, thereby making the electrical motor dynamics no longer negligible. In addition, many motors that are ideal for use in direct drive robots are highly non-linear and consequently their dynamics must be included in the overall control design if the benefits of direct drive technologies are to be fully realized. In this paper we design adaptive controllers for a class of direct drive robot manipulators whose actuators consist of Brushed Direct Current (BDC), and Brushless Direct Current (BLDC) and switched reluctance (SR) motors. In particular, the controllers presented handle the multilink dynamics of a rigid link robot and the multiple-input non-linear electrical dynamics of BLDC and SR motors. The result is a class of adaptive controllers that achieve global asymptotic convergence of the link position tracking error in spite of parametric uncertainty throughout the entire electromechanical robot model. In addition, the controllers only require measurements of link position, link velocity and electrical currents.

Adaptive control for a class of direct drive robot manipulators

Direct drive robot technology has received considerable attention by the robotics research community given its potential for developing robots with superior positioning accuracy and repeatability characteristics. Direct drive robots do not have the compliance and backlash problems typically associated with geared robots. With the absence of gearing, however, the motors in direct drive robots must be able to produce considerably larger torques than the motors in a geared robot. As a result, the inductances of the motor windings are much larger, thereby making the electrical motor dynamics no longer negligible. In addition, many motors that are ideal for use in direct drive robots are highly non-linear and consequently their dynamics must be included in the overall control design if the benefits of direct drive technologies are to be fully realized. In this paper we design adaptive controllers for a class of direct drive robot manipulators whose actuators consist of Brushed Direct Current (BDC), and Brushless Direct Current (BLDC) and switched reluctance (SR) motors. In particular, the controllers presented handle the multilink dynamics of a rigid link robot and the multiple-input non-linear electrical dynamics of BLDC and SR motors. The result is a class of adaptive controllers that achieve global asymptotic convergence of the link position tracking error in spite of parametric uncertainty throughout the entire electromechanical robot model. In addition, the controllers only require measurements of link position, link velocity and electrical currents.

ロボット研究地図 実時間予見仮想目標値を用いたＤＤロボットのディジタル非線形制御

A digital nonlinear control of a 3-link direct drive robot manipulator by means of a real time preview virtual desired signal is proposed. The digital nonlinear control is based on the principle that only the acceleration changes at the moment when the input torque changes. A linear optimal control system is designed on the basis of this principle. The real time preview virtual desired signal is the virtual one for improving tracking characteristics for the original desired signal. It is designed considering control characteristics of the already designed digital nonlinear control system and it is derived at real time by feedback of state variables. The real time preview virtual desired signal that means the real time virtual desired signal with preview feedforward compensation is also designed and its characteristics are also studied.The digital nonlinear control system with this real time virtual desired signal is applied to the 3-link direct drive robot manipulator. And frequency characteristics, system responses for circle and square desired orbits are examind in experiment and these effectiveness is confirmed.

Reexamination of the DCAL controller for rigid link robots

SummaryIn this paper, we present two DCAL-like (Desired Compensation Adaptation Law) controllers for link position tracking of n-link, rigid, revolute robot manipulators. First, we use a simplified stability analysis to illustrate global asymptotic link position-velocity tracking for a DCAL-like controller with nonlinear feedback. The proof is simplified by employing a different structure for the nonlinear feedback than that originally proposed and by making use of the nonlinear damping control design tool. We then use the nonlinear damping tool to show that a DCAL-like controller with linear feedback can guarantee semi-global asymptotic link position-velocity tracking. The proposed nonlinear and linear feedback DCAL-like controllers are experimetally tested and compared using the Integrated Motion Inc. 2-link direct drive robot manipulator.

An Investigation of Impedance Control for Robot Manipulators

This article documents investigations into impedance control carried out on a direct drive robot manipulator. Both linear and nonlinear versions of impedance contrallers have been investigated. The two impedance controllers have been simu lated numerically and implemented on Carleton University's direct drive robot. The performance of these controllers is ex perimentally compared in terms of force trajectory following and contact stability. The article highlights the practical and experimental limitations of the proposed methods. Furthermore, the experimental results indicate that the controller design, the manipulator kinematics, and the desired task must all be considered and evaluated simultaneously.

Learning hybrid force and position control of robot manipulators

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. >

Motion Control of a Vertically Articulated DirectDrive Robot Manipulator

Title: Motion Control of a Vertically Articulated DirectDrive Robot Manipulator | Author: Akira Shimada, Tsuyoshi Umeda, Norio Yokoshima, Naoki Kawawada, Hiroshi Watanabe, Toshimi Shioda and Masahide Nagai

Learning control of a parallel-link direct-drive robot manipulator

A learning control method for a parallel link direct-drive robot manipulator is introduced. The acceleration error is used to correct the motion of the direct-drive motors. A theoretical method, which estimates the convergence condition of the learning control process based on the limit condition of a geometric series, is proposed. The validity of the proposed theory is proved through a computer simulation, and the method is applied to the practical system of a parallel link direct-drive robot manipulator. The convergence condition of a learning control system can be quantitatively estimated by the divergent index which is defined in this study.

軌跡追従型タイムディレイコントロールの提案とそのロボットマニピュレータ制御への応用

This paper focuses on the control of systems with unknown dynamics and deals with a class of systems described by (d/dt) x=ƒ (x, t) +h (x, t) +B (x, t) u+d (t) where h (x, t) and d (t) are unknown dynamics and unexpected disturbances, respectively. Under the assumption of accesibility to all the state variables and their derivatives, Time Delay Control (TDC) has been proposed in our previous paper, based on model reference design approach. This paper first proposes the extended TDC scheme based on trajectory following design approach. The control system's stability, structure and trajectory following property is then discussed for linear-timeinvariant and single-input-single-output systems. Finally, the control performance is evaluated through experiments using a two degree-of-freedom, direct-drive robot manipulator. The theoretical and experimental results indicate that this control method shows excellent robustness properties to unknown dynamics and disturbances based on a very simple control algorithm using time delay.

Design and analysis of the statically balanced direct-drive robot manipulator

A practical architecture, using a four-bar linkage, is considered for the University of Minnesota direct-drive robot (Kazerooni, H., Kim, S.: A new architecture for direct drive robots. In Proc. IEEE International Conference on Robotics and Automation, Philadelphia, Pennsylvania, April 1988). This statically balanced direct-drive robot has been constructed for stability analysis of the robot in constrained manipulation (Kazerooni, H. et al.: Fundamentals of robust compliant motion for robot manipulators. IEEE J. Robotics Automation 2: 1986; Kazerooni, H.: On the robot compliant motion control. ASME J. Dynamic Systems Msmt Control; 111 (3): September 1989. Kazerooni, H. et al.: Theory and experiments on robot compliant motion control. ASME J. Dynamic Systems Msmt Control, June 1990). As a result of the elimination of the gravity forces (without any counterweights), smaller actuators and, consequently, smaller amplifiers were chosen. The motors yield acceleration of 5 g at the robot end point without overheating. High torque, low speed, brushless AC synchronous motors are used to power the robot. Graphite-epoxy composite material is used for construction of the robot links. A 4-node parallel processor has been used to control the robot. A compliant motion control method has been derived and experimentally verified to guarantee stable constrained maneuvers for the robot. As part of the research work, a general criterion has been derived to guarantee the stability of robot manipulators in constrained maneuvers.

Learning control of a parallel-link direct-drive robot manipulator.

This paper describes the learning control method of a parallellink direct-drive robot manipulator. The acceleration error is used to correct the motion of the direct-drive motors. A theoretical method, which estimates the convergent limit of the learning control process based on the limit of a geometric series, is proposed. The validity of this proposed theory is proven through computer simulation ; it is then applied to the practical system of a parallel-link direct-drive robot manipulator. The convergent limit of a learning control system can be quantitatively estimated by the divergent coefficient which is defined in this study.

Design of Direct-Drive Mechanical Arms

This paper describes the design concept of a new robot based on the direct-drive method using rare-earth d-c torque motors. Because these motors have high torque, light weight and compact size, we can construct robots with far better performance than those presently available. For example, we can eliminate all the transmission mechanisms, such as reducers and chain belts, between the motors and their loads, and construct a simple mechanism (direct-drive) where the arm links are directly coupled to the motor rotors. This elimination can lead to excellent performance: no backlash, low friction, low inertia, low compliance and high reliability, all of which are suited for high-speed, high-precision robots. First we propose a basic configuration of direct-drive robots. Second a general procedure for designing direct-drive robots is shown, and the feasibility of direct drive for robot actuation is discussed in terms of weights and torques of joints. One of the difficulties in designing direct-drive robots is that motors to drive wrist joints are loads for motors to drive elbow joints, and they are loads for motors at shoulders. To reduce this increasing series of loads is an essential issue for designing practical robots. We analyze the joint mass system for simplified kinematic model of the direct-drive robots, and show how the loads are reduced significantly by using rare-earth motors with light-weight and high torque. We also discuss optimum kinematic structures with minimum arm weight. Finally, we describe the direct-drive robotic manipulator (CMU arm) developed at Carnegie-Mellon University, and verify the design theory.