Two-link Robot Arm Research Articles

随意性瞬目を用いた2リンク型ロボットアームの先端位置制御コントローラの開発

随意性瞬目を用いた2リンク型ロボットアームの先端位置制御コントローラの開発

Feedforward Learning Control by Scheduled Locally Weighted Regression

It is widely recognized that feedforward (FF) control is effective for response shaping but requires an accurate model of the plant. In view of this, on-line FF learning control has been studied extensively in literature. This paper proposes a learning scheme for FF control based on scheduled locally weighted regression (LWR). When the plant dynamics change with a schedule parameter available in real time, the input/output behavior is labelled by the parameter and accumulated as regression data. FF control signal is then generated by means of regression weighted locally with respect to the current schedule parameter (query). The proposed scheme is illustrated by numerical simulation of i) a linear time-varying system and then ii) a two-link robot arm which is highly nonlinear and challenging.

Delaying tool chatter in turning with a two-link robotic arm

Chatter leaves a rough machined surface, accelerates wear of the cutter and creates unacceptably loud noise levels. A conventional approach to suppress chatter is to slow the material removal rate. Such an action is usually successful to avoid chatter, but causes increased production time and cost. Therefore, it is desirable to maintain a reasonably fast rate of production and employ a chatter control measure. In this research, a semi-active parameter control technique is investigated numerically during a robotic turning process. Investigations have been performed on a two-link robotic arm model. The control of chatter has been achieved by varying the joint stiffness in a synchronized mode with the spindle speed. Stability lobe diagrams have been compared for controlled and uncontrolled cases. Simulation results showed that significant improvements can be achieved by varying the joint stiffness of robotic structures. The proposed method is stable, effective and requires no additional hardware to implement at the actuated joints of robots.

Grey-box Modeling of Elastic-joint Robot with Harmonic Drive and Timing Belt

We previously proposed a multivariable identification method, called the “decoupling identification method”, for a horizontal two-link robot arm with elastic harmonic drive gears. This paper extends the identification method to a vertical two-link robot arm with the harmonic drive gears and timing belts, under gravity. The mechanical resonance effects of the timing belts in the high-frequency range are decimated in advance, to apply the decoupling method. The characteristics of the timing belts are independently estimated as perturbations in the high-frequency range for robust controller design. The effectiveness of the extended identification method was experimentally verified using the vertical two-link robot with the elastic elements.

Finite-gain L2 Stability of PID Set Position Control with Anti-windup Compensation for Euler-Lagrange Systems with Actuator Saturation

Finite-gain L2 stabilization is achieved locally for a system using a PID set position controller and a newly proposed static anti-windup compensation for Euler-Lagrange systems with actuator saturation and external disturbances. In a closed-loop nonlinear system with feedback and input saturation, L2 stability of the Euler-Lagrange systems is guaranteed based on the passivity of anti-windup compensation. The control performance to counter an external disturbance added for the purpose of input saturation is verified by numerical simulations and experiments using a two-link robot arm.

Anti-windup Adaptive Law for Euler–Lagrange Systems with Actuator Saturation

This paper proposes an anti-windup adaptive law for Euler–Lagrange systems with actuator saturation. The target posture is set to be constant and the asymptotic input is compensated by adaptive estimation of parameters of the gravity term. In order to achieve global asymptotic stability and to assure adaptive control performance, an output saturation function of the error vector between the rotation and target angles is introduced on the basis of quasi-natural potential. According to the saturation state, switching of the adaptive law is proposed to achieve asymptotic stability based on the Lyapunov theorem. The control performance is verified by numerical simulations and experiments on a two-link robot arm.

Reinforcement Learning Controller Design for Affine Nonlinear Discrete-Time Systems using Online Approximators

In this paper, reinforcement learning state- and output-feedback-based adaptive critic controller designs are proposed by using the online approximators (OLAs) for a general multi-input and multioutput affine unknown nonlinear discretetime systems in the presence of bounded disturbances. The proposed controller design has two entities, an action network that is designed to produce optimal signal and a critic network that evaluates the performance of the action network. The critic estimates the cost-to-go function which is tuned online using recursive equations derived from heuristic dynamic programming. Here, neural networks (NNs) are used both for the action and critic whereas any OLAs, such as radial basis functions, splines, fuzzy logic, etc., can be utilized. For the output-feedback counterpart, an additional NN is designated as the observer to estimate the unavailable system states, and thus, separation principle is not required. The NN weight tuning laws for the controller schemes are also derived while ensuring uniform ultimate boundedness of the closed-loop system using Lyapunov theory. Finally, the effectiveness of the two controllers is tested in simulation on a pendulum balancing system and a two-link robotic arm system.

Decoupling Identification Method of Serial Two-link Two-inertia System for Robot Motion Control

AbstractThe goal of our study is to obtain a precise dynamic model by applying the technique of system identification for the model-based control of a nonlinear robot arm, taking joint-elasticity into consideration. We previously proposed “decoupling identification method” for a planar serial two-link robot arm with elastic-joints caused by the Harmonic-drive reduction gears. First, this paper reviews the decoupling effectiveness of the proposed identification method. This method serves as an extension of the conventional rigid-joint-model-based identification, and treats the robot arm as a serial two-link two-inertia system with nonlinearity. The main idea of the decoupling method is nonlinear interaction torques between two links are utilized as identification inputs besides motor inputs. The torques can be computed using the rigid-joint-model parameters and link-accelerometer signals, and enable the serial two-link two-inertia system to be divided into two linear one-link systems. Typical multi-input multi-output linear model estimation algorithms can be applied for the identification method. Physical parameters such as motor inertias, link inertias, joint-friction coefficients and joint-spring coefficients of the dynamic model are estimated by applying the coefficient comparison method to the transfer functions of the one-link two-inertia systems. This is a gray-box modeling approach. Second, this paper extends the proposed method to closed-loop identification from open-loop identification. Thus the method is applicable for not only a SCARA (Selective Compliant Assembly Robot Arm) but also a PUMA (Programmable Universal Manipulation Arm) under gravity. Third, this paper unveils the robustness of the decoupling method against estimation errors of coupling-inertia parameters for computing nonlinear interaction torques; these parameters are obtained by the conventional rigid-joint-model-based identification. Several experiments using the planar serial two-link robot arm with elastic-joints are conducted to demonstrate the effectiveness and robustness of the decoupling identification method.

Ball-Pitching Challenge with an Underactuated Two-Link Robot Arm

AbstractMaking use of natural dynamics of mechanical systems is extremely powerful for generating highly dynamic motion with impressive performance. Optimal trajectories, however, are difficult to find when the systems has weak actuation or totally passive degrees of freedom during motion. We discuss how the complexity of a finite-time optimal control problem for an underactuated two-link robot gets reduced by using a geometric parameterization of motion. A performance index for maximum velocity is analytically derived enabling a parametric search and study of parameter sensitivities. We investigate a formulation of a necessary condition for optimality. A small number of parameters describes an optimal pitching motion.

Vision-Based Trajectory Generation for a Two-Link Robotic Arm Using Quadtree Decomposition and Curve Smoothing

In this article, the development of an autonomous robot trajectory generation system based on a single eye-in-hand webcam, where the workspace map is not known a priori, is described. The system makes use of image processing methods to identify locations of obstacles within the workspace and the Quadtree Decomposition algorithm to generate collision free paths. The shortest path is then automatically chosen as the path to be traversed by the robot end-effector. The method was implemented using MATLAB running on a PC and tested on a two-link SCARA robotic arm. The tests were successful and indicate that the method could be feasibly implemented on many practical applications.

A robust training algorithm of discrete-time MIMO RNN and application in fault tolerant control of robotic system

In this paper, a novel robust training algorithm of multi-input multi-output recurrent neural network and its application in the fault tolerant control of a robotic system are investigated. The proposed scheme optimizes the gradient type training on basis of three new adaptive parameters, namely, dead-zone learning rate, hybrid learning rate, and normalization factor. The adaptive dead-zone learning rate is employed to improve the steady state response. The normalization factor is used to maximize the gradient depth in the training, so as to improve the transient response. The hybrid learning rate switches the training between the back-propagation and the real-time recurrent learning mode, such that the training is robust stable. The weight convergence and L 2 stability of the algorithm are proved via Lyapunov function and the Cluett’s law, respectively. Based upon the theoretical results, we carry out simulation studies of a two-link robot arm position tracking control system. A computed torque controller is designed to provide a specified closed-loop performance in a fault-free condition, and then the RNN compensator and the robust training algorithm are employed to recover the performance in case that fault occurs. Comparisons are given to demonstrate the advantages of the control method and the proposed training algorithm.

Anti-windup Controller Design using Gravity Compensation with Parameter Estimation Error for Euler–Lagrange Systems with Actuator Saturation

A static anti-integrator-windup controller design that employs gravity compensation and takes parameter error into consideration is proposed for Euler–Lagrange systems. Gravity compensation is performed by treating the time derivative of the gravity term for asymptotic stability based on passivity when actuator saturation occurs. A new design variable is introduced to increase the degrees of freedom of controller design. The optimality of the proposed PI controller is examined using an estimation function for actuator saturation. The proposed controller and its optimality are verified by numerical simulations and experiments using a two-link robot arm.

遺伝的アルゴリズムを適用したＦｕｚｚｙ ＡＲＴによる２リンクロボットアームの行動獲得の効率化

An efficient learning method using Fuzzy ART with Genetic Algorithm is proposed. The proposed method reduces the number of trials by using a policy acquired in other tasks because a reinforcement learning needs a lot of the number of trials until an agent acquires appropriate actions. Fuzzy ART is an incremental unsupervised learning algorithm in responce to arbitrary sequences of analog or binary input vectors. Our proposed method gives a policy by crossover or mutation when an agent observes unknown states. Selection controls the category proliferation problem of Fuzzy ART. The effectiveness of the proposed method was verified with the simulation of the reaching problem for the two-link robot arm. The proposed method achieves a reduction of both the number of trials and the number of states.

経由点と遺伝的アルゴリズムを用いた電磁吸着搬送2関節ロボットアームの準最短時間軌道計画と実験(機械力学,計測,自動制御)

In this study, the quasi-minimum time trajectory planning method of an electromagnetic attraction transfer two-link robot arm using two via points and the genetic algorithm. The electromagnetic attraction transfer work is executed by use of an electromagnetic attraction hand attached to the end of the two-link robot arm. In the quasi-minimum time trajectory planning, the trajectory polynomial expressions using two via points are derived by using the boundary and continuous conditions of joint angle, joint angle velocity and joint angle acceleration, and the trajectory is expressed using three genes. Then, using three genes and the genetic algorithm and defining the fitness function for the quasi-minimum time trajectory, the quasi-minimum time trajectory planning algorithm is constructed. Furthermore, the numerical calculations and the experiments are demonstrated, and the usefulness of the present quasi-minimum time trajectory planning method is confirmed.

Decoupling Identification for Serial Two-link Robot Arm with Elastic Joints

The objective of our study is to build a precise model by applying the technique of system identification for the model-based control of a nonlinear robot arm, taking joint-elasticity into consideration. This paper proposes a systematic identification method, called “decoupling identification”, for a serial two-link robot arm with elastic joints caused by the Harmonic drive® reduction gears. The proposed method serves as an extension of the conventional rigid-joint-model-based identification. The robot arm is treated as a serial two-link two-inertia system with nonlinearity. The decoupling identification method using link-accelerometer signals enables the serial two-link two-inertia system to be divided into two linear one-link two-inertia systems. The MATLAB®'s commands for state-space model estimation are utilized in the proposed method. Physical parameters such as motor inertias, link inertias, joint-friction coefficients and joint-spring coefficients are estimated through the identified one-link two-inertia systems. Experimental results using a SCARA-type planar two-link robot arm with elastic reduction gears showed an accuracy of the proposed identification method.

Anti-windup Controller Design for Anthropoid Robot Manipulators with Actuator Saturations

A static anti-integrator-windup controller design method is proposed for anthropoid robot manipulators. The asymptotic stability is achieved using a simple static anti-windup gain matrix for set point position control with PI or PID controllers. The control performance is verified on a two-link robot arm with a gravity term by numerical simulations and experiments.

Decoupling Identification with Closed-loop-controlled Elements for Two-link Arm with Elastic Joints

Abstract The purpose of our study is to build a precise model by applying the technique of system identification for the model-based control of a nonlinear robot arm, taking joint-elasticity into consideration. We previously proposed a systematic identification method, called “decoupling identification”, for a “SCARA-type” planar two-link robot arm with elastic joints caused by the Harmonic-drive reduction gears. The proposed method serves as an extension of the conventional rigid-joint-model-based identification. The robot arm is treated as a serial two-link two-inertia system with nonlinearity. The decoupling identification method using link-accelerometer signals enables the serial two-link two-inertia system to be divided into two linear one-link two-inertia systems. The MATLAB's commands for state-space model estimation are utilized in the proposed method. Physical parameters such as motor inertias, link inertias, joint-friction coefficients and joint-spring coefficients are estimated through the identified one-link two-inertia systems. This paper describes an accuracy evaluation of the identification method through experimental results using the two-link arm with “closed-loop-controlled elements”. The results show the method is applicable to a “PUMA-type” vertical robot arm under gravity.

Robust control of robotic manipulators based on integral sliding mode

For rigid body robot manipulators, the computed torque approach provides asymptotic stability for tracking control tasks. However, the state dependent matrices needed to complete the computed torque algorithm are normally unknown and possibly too complex for a real-time implementation. This paper proposes a simple controller with computed-torque-like structure enhanced by integral sliding mode, having pole-placement capability. For the reduction of the chattering effect generated by the sliding mode part, the integral sliding mode is posed as a perturbation estimator with quasi-continuous control action provided by an additional low-pass filter. The time-constant of the latter tunes the controller functionality between the perturbation compensation and a pure integral sliding mode control, as well as between chattering reduction and system robustness. A comparative simulation study between conventional sliding mode control, integral sliding mode control, and integral sliding mode in form of a perturbation estimator for a two-link robot arm validates the proposed design.

Grasp Transfer Control Using Cartesian Coordinate Two-Link Robot Arm with Prototype Robot Hand Consisting of Stepping Motors, Gears and Plate Springs

In this study, the grasp transfer control system by a Cartesian coordinate two-link robot arm with a prototype robot hand is presented. The prototype robot hand consists of permanent-magnet-type stepping motors, gears and plate springs. The grasp force control of the robot hand is performed by a feedforward control of the stepping motors based on the dimension of a grasped object. The Cartesian coordinate two-link robot arm consists of ball screws and hybrid stepping motors. Then the numerical simulations and experiments of the grasp transfer control have been carried out, and it is confirmed that the grasp transfer control could be successfully performed, and the grasp force could be accurately controlled among the motion control of the Cartesian coordinate two-link robot arm.

Adaptive biomimetic control of robot arm motions

By introducing a biologically inspired robotic model that combines a modified feedback error learning, an unsupervised learning, and the viscoelastic actuator system in order to drive adaptive arm motions, this paper discusses the potential usefulness of a biomimetic design of robot skill. The feedback error learning is consistent with the cerebellar adaptation, the unsupervised learning, the synergy network adaptation, and the viscoelastic system of the muscles. The proposed model applies a feedforward adaptive scheme in the low dimensional control space and an adaptive synergy distribution to control redundant actuators effectively. The combination of the two adaptive control schemes is tested by controlling a two-link planar robot arm with six muscular actuators in the gravitational field. The simulation-based study demonstrates that the control scheme adapts the robot arm motions quickly and robustly to generate smooth, human-like motions.