Experimental Verification of Adaptive Dominant Type Hybrid Adaptive and Learning Controller for Trajectory Tracking of Robot Manipulators

2013 ◽  
Vol 25 (4) ◽  
pp. 737-747 ◽  
Author(s):  
Munadi ◽  
◽  
Tomohide Naniwa ◽  
Keyword(s):  
Adaptive Control ◽  
Motion Control ◽  
Trajectory Tracking ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Tracking Error ◽  
Control Input ◽  
Dominant Type ◽  
Dynamical Parameters ◽  
Small Robot

This paper presents an experimental study to verify an adaptive dominant type hybrid adaptive and learning controller for acquiring an accurate trajectory tracking of periodic desired trajectory of robot manipulators. The proposed controller is developed based on combining the model-based adaptive control (MBAC), repetitive learning control (RLC) and proportionalderivative (PD) control in which the MBAC input becomes dominant than other inputs. Dominance of adaptive control input gives the advantage that the proposed controller could adjust the feed-forward motion control input immediately after changing the desired motion or load of the manipulator. In motion control law, the proposed controller uses only one vector to estimate the unknown dynamical parameters. It makes the proposed controller as a simpler hybrid adaptive and learning controller which does not need much computational power and also is easily be implemented for real applications of robot manipulators. The proposed controller is verified through experiments on a four-link small robot manipulator as representation of a scale robot manipulator to ensure this controller can be applied in the real applications of robot manipulators. The experimental results show the effectiveness of the proposed controller by indicating the position tracking error approaches to zero.

Design of an adaptive control law using trigonometric functions for robot manipulators

Robotica ◽  
2005 ◽  
Vol 23 (1) ◽  
pp. 93-99 ◽  
Author(s):  
Recep Burkan
Keyword(s):  
Adaptive Control ◽  
Robot Manipulators ◽  
Tracking Error ◽  
Uncertain System ◽  
Control Law ◽  
Trigonometric Functions ◽  
Feed Forward ◽  
Inertia Parameters ◽  
The Stability ◽  
Adaptive Control Law

In this study, a new approach of adaptive control law for controlling robot manipulators using the Lyapunov based theory is derived, thus the stability of an uncertain system is guaranteed. The control law includes a PD feed forward part and a full dynamics feed forward compensation part with the unknown manipulator and payload parameters. The novelty of the obtained result is that an adaptive control algorithm is developed using trigonometric functions depending on manipulator kinematics, inertia parameters and tracking error, and both system parameters and adaptation gain matrix are updated in time.

Trajectory tracking control of robot manipulators using a neural-network-based torque compensator

1998 ◽  
Vol 212 (5) ◽  
pp. 361-372 ◽  
Author(s):  
Q Li ◽  
S K Tso ◽  
W J Zhang
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
High Performance ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Lyapunov Stability Theory ◽  
System Stability ◽  
Trajectory Tracking Control ◽  
Modelling Uncertainties

In this paper, an adaptive neural-network-based torque compensator is developed for the trajectory-tracking control of robot manipulators. The overall control structure employs a classical non-linear decoupling controller for actuating torque computation based on an approximated robot dynamic model. To suppress the effects of uncertainties associated with the estimated model, a supplementary neural network algorithm is developed to generate compensation torques. The weight adaptation rule for this neuro-compensator is derived on the basis of the Lyapunov stability theory. Both global system stability and the error convergence can then be guaranteed. Simulation studies on a two-link robot manipulator demonstrate that high performance of the proposed control algorithm could be achieved under severe modelling uncertainties.

Globally exponential continuous controller/observer for position tracking in robot manipulators with hysteretic joint friction

Robotica ◽  
2009 ◽  
Vol 28 (5) ◽  
pp. 759-763 ◽  
Author(s):  
Srinivasulu Malagari ◽  
Brian J. Driessen
Keyword(s):  
Robot Manipulator ◽  
Robot Manipulators ◽  
Tracking Error ◽  
Degree Of Freedom ◽  
Position Tracking ◽  
Observer Error ◽  
Joint Friction

SUMMARYIn this work, we present a continuous observer and continuous controller for a multiple degree of freedom robot manipulator with hysteretic joint friction. The fictitious hysteresis state is of course unknown to the controller and must be estimated. The joint velocities are assumed measured here. For this considered plant, we propose and present a continuous observer/controller that estimates or observes the hysteresis state and drives the position tracking error to zero. We prove that the combined tracking error and observer error converges to zero globally exponentially.

scholarly journals Sliding Mode with Adaptive Control of Robot Manipulator Trajectory Tracking using Neural Network Approximation

2021 ◽  
Vol 10 (6) ◽  
pp. 120
Author(s):  
Monisha Pathak* ◽  
◽  
Dr. Mrinal Buragohain ◽  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Robotic Manipulator ◽  
Adaptive Sliding Mode Control ◽  
Control Approach ◽  
Adaptive Sliding Mode

This paper briefly discusses about the Robust Controller based on Adaptive Sliding Mode Technique with RBF Neural Network (ASMCNN) for Robotic Manipulator tracking control in presence of uncertainities and disturbances. The aim is to design an effective trajectory tracking controller without any modelling information. The ASMCNN is designed to have robust trajectory tracking of Robot Manipulator, which combines Neural Network Estimation with Adaptive Sliding Mode Control. The RBF model is utilised to construct a Lyapunov function-based adaptive control approach. Simulation of the tracking control of a 2dof Robotic Manipulator in the presence of unpredictability and external disruption demonstrates the usefulness of the planned ASMCNN.

scholarly journals A fast L1 adaptive constrained back-stepping controller using barrier Lyapunov function for anuncertain submersible vehicle

2021 ◽  
Author(s):  
Hossein Ahmadian ◽  
Mehdi Arefi ◽  
Alireza Khayatian ◽  
Allahyar Montazeri
Keyword(s):  
Adaptive Control ◽  
Lyapunov Function ◽  
Trajectory Tracking ◽  
Tracking Error ◽  
Control Technique ◽  
Remotely Operated Vehicle ◽  
Pass Filter ◽  
Low Pass Filter ◽  
L1 Adaptive Control ◽  
Barrier Lyapunov Function

Abstract In this paper, a new L1 adaptive back-stepping controller based on the barrier Lyapunov function (BLF) is proposed to respect the position and velocity constraints usually imposed in designing Euler-Lagrange systems. The purpose of this investigation is to improve different aspects of a conventional L1 adaptive control. More specifically, the modified controller has a lower complexity by removing the low-pass filter from the design procedure. The performance of the controller is also enhanced by having a faster convergence speed and increased robustness against nonlinear uncertainties and disturbances arising in practical applications. The proposed scheme is evaluated on two different Euler-Lagrange systems, i.e. a 6-DOF remotely operated vehicle (ROV) and a single-link manipulator. The results for the new back-stepping design are assessed in both scenarios in terms of settling time, percentage of overshoot, and trajectory tracking error. The results confirm that both tracking and state estimation errors for position and velocity outputs outperform the standard L1 adaptive control technique. The results also demonstrate the high performance of the proposed approach in removing the matched nonlinear time-varying disturbances and dynamic uncertainties and a good trajectory tracking despite the uncertainty on the input gain of the system.

scholarly journals Trajectory Tracking Control for Uncertain Robot Manipulators with Repetitive Motions in Task Space

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jiutai Liu ◽  
Xiucheng Dong ◽  
Yong Yang ◽  
Hongyu Chen
Keyword(s):  
Trajectory Tracking ◽  
Robot Manipulators ◽  
Tracking Error ◽  
Task Space ◽  
Learning Gain ◽  
Trajectory Tracking Control ◽  
Adaptive Iterative Learning Control ◽  
Tracking Tasks ◽  
Control Schemes ◽  
Composite Energy

This paper aims at the trajectory tracking problem of robot manipulators performing repetitive tasks in task space. Two control schemes are presented to conduct trajectory tracking tasks under uncertain conditions including unmodeled dynamics of robot and additional disturbances. The first controller, pure adaptive iterative learning control (AILC), is based upon the use of a proportional-derivative-like (PD-like) feedback structure, and its design seems very simple in the sense that the only requirement on the learning gain and control parameters is the positive definiteness condition. The second controller is designed with a combination of AILC and neural networks (NNs) where the AILC is adopted to learn the periodic uncertainties that attribute to the repetitive motion of robot manipulators while the add-on NNs are used to approximate and compensate all nonperiodic ones. Moreover, a combined error factor (CEF), which is composed of the weighted sum of tracking error and its derivative, is designed for network updating law to improve the learning speed as well as tracking accuracy of the system. Stabilities of the controllers and convergence are proved rigorously by a Lyapunov-like composite energy function. The simulations performed on two-link manipulator are provided to verify the effectiveness of the proposed controllers. The results of compared simulations illustrate that our proposed control schemes can significantly conduct trajectory tracking tasks.

Multiple model adaptive control using second level adaptation for a delta-wing aircraft

2015 ◽  
Vol 3 (1) ◽  
pp. 2-17 ◽  
Author(s):  
Narjes Ahmadian ◽  
Alireza Khosravi ◽  
Pouria Sarhadi
Keyword(s):  
Adaptive Control ◽  
Delta Wing ◽  
Block Diagram ◽  
Tracking Error ◽  
Control Input ◽  
Multiple Model ◽  
Content Type ◽  
Multiple Model Adaptive Control ◽  
And Control ◽  
Model Reference Adaptive

Purpose – The purpose of this paper is to design a stable controller such that the control input is applied to the delta-wing aircraft in order to adjust the roll dynamics. The controller must provide a desired tracking performance with minimum tracking error. Design/methodology/approach – In this paper, the second level adaptation (SLA) strategy is applied to control a delta-wing aircraft using multiple models. The implemented control structure is compared with the first level adaptation (FLA) and model reference adaptive control (MRAC) techniques. Findings – SLA architecture not only copes with a wide uncertainty domain caused by aerodynamic effects, but also its rapid and accurate convergence is one of its most important features. Furthermore, this strategy makes a smoother control signal with respect to FLA and MRAC even at the same initial times. It should be also noted that SLA using three models, copes with uncertainty that may occur to the aircraft at high Angle Of Attacks (AOAs) at the entire flight envelope. Originality/value – In this paper for the first time the application of this strategy is used to identify and control a delta-wing aircraft. Furthermore a systematic block diagram approach is proposed for the design.

A new simulation scheme for self-tuning adaptive control of robot manipulators

Robotica ◽  
1991 ◽  
Vol 9 (3) ◽  
pp. 335-339 ◽  
Author(s):  
Q. Wang ◽  
D. R. Broome
Keyword(s):  
Adaptive Control ◽  
Controller Design ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Degree Of Freedom ◽  
Dynamic Equations ◽  
Real Time Control ◽  
Highly Nonlinear ◽  
Self Tuning ◽  
Simulation Scheme

SUMMARYIn most dynamic adaptive control simulation of robotic manipulators, the Langrange–Euler (L–E) dynamic equations are first piecewise linearized about the desired reference and then discretized and rewritten in a state space form. This makes things very complicated and it is easy to make errors. What is more is that with a different reference this work must be done again. A new simulation scheme – Backward Recursive Self-Tuning Adaptive (BRSTA) – as it will be called, is suggested in this paper for adaptive controller design of robot manipulators. A two degree of freedom robot manipulator is used to verify the scheme in the condition of highly nonlinear and highly coupled system. A one degree of freedom robot manipulator is used for comparing both the forward and backward methods. The main advantages of this scheme include that it can be used for evaluating the self-tuning adaptive control laws and provide the initial process parameters for real-time control. And it is concluded here that the Newton–Euler (N–E) dynamic equations are equally well qualified as the Langrange–Euler (L-E) equations for the simulation of self-tuning adaptive control of robot manipulators.

Dynamics and Motion Control of Flexible Manipulators With Multi-Degree of Kinematic Redundancy

2005 ◽  
Author(s):  
Yue-Qing Yu ◽  
Ji-Yun Yang
Keyword(s):  
Motion Control ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Flexible Manipulator ◽  
Flexible Manipulators ◽  
Kinematic Redundancy ◽  
Flexible Robot ◽  
Flexible Robots ◽  
Dynamic Performances ◽  
Self Motion

The dynamics and motion control of flexible robot manipulators is an advanced topic in the study of robotics. The precise tracking of the end-effector trajectory of flexible robots can be improved by the self-motion of redundant manipulators. The flexible manipulator with single-degree of kinematic redundancy has been considered only at present. This study addresses on the dynamics and motion control of flexible robots with multi-degree of kinematic redundancy. Compared with the robot with one-degree of redundancy, the optimal motion programming of a flexible robot manipulator with two-degree of redundancy has been obtained successfully based on pseudo-inverse solution. The numerical results of planar three-link and four-link flexible manipulators show the advantage of multi-degree of redundancy in improving the kinematic and dynamic performances of flexible robot manipulators.

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