Sub-task Tracking Research Articles

Tracking control of redundant robot manipulators using RBF neural network and an adaptive bound on disturbances

In this paper, a hybrid trajectory tracking controller is designed for redundant robot manipulators, consisting of RBF neural network and an adaptive bound on disturbances. The controller is composed of computed torque type part, RBF neural network and an adaptive controller. The controller achieves end-effector trajectory tracking as well as subtask tracking effectively. The controller is able to learn the existing structured and unstructured uncertainties in the system in online manner. The RBF network learns the unknown part of the robot dynamics with no requirement of the offline training. The adaptive controller is used to estimate the unknown bounds on unstructured uncertainties and neural network reconstruction error. The overall system is proved to be asymptotically stable in the sense of Lyapunov. Finally, numerical simulation studies are performed on a 3R planar robot manipulator to show the effectiveness of the control scheme.

ADAPTIVE ROBUST TRACKING CONTROL OF AN UNDERWATER VEHICLE-MANIPULATOR SYSTEM WITH SUB-REGION AND SELF-MOTION CRITERIA

This paper proposes an adaptive robust control scheme for an Underwater-Vehicle Manipulator System (UVMS). The proposed controller enables the tracking of the intersection of multiple local sub-regions that are assigned for each subsystem of a UVMS under the influence of modelling uncertainties as well as additive disturbances. The presence of variable ocean currents creates hydrodynamic forces and moments that are not well-known or predictable, even though they are bounded. Therefore, the control task of tracking a prescribed sub-region trajectory is challenging due to these additive bounded disturbances. In the presented adaptive control law, a least-squares estimation algorithm is utilized rather than gradient-type approach. The use of the self-motion due to the kinematically redundant system allows performance of multiple subtasks (e.g., maintaining manipulability and avoidance of mechanical joint limits). The asymptotically sub-region and sub-task tracking are ensured using the proposed control law despite the parametric uncertainty of the UVMS and external additive disturbances. The stability analysis is carried out using the Lyapunov-type approach. The simulation results illustrate the validity of the proposed control scheme.

Neural network based control scheme for redundant robot manipulators subject to multiple self-motion criteria

The aim of this paper is to design a neural network based adaptive control scheme for redundant manipulators in the presence of model uncertainties and external disturbances together with multiple self-motion criteria. With reference to the paper (N. Kumar, V. Panwar, N. Sukavanam, S.P. Sharma, J.H. Borm, Neural network based nonlinear tracking control of kinematically redundant robot manipulators, Mathematical and Computer Modelling 53 (2011) 1889–1901), an adaptive mechanism is also developed to estimate the uncertain bound of external disturbances and neural network approximation error etc. without requirement of known bounds. By Lyapunov method asymptotic error convergence can be guaranteed for both task-space and sub-task tracking errors. A comparative simulation study with a robust controller and the proposed controller are included for a 3 link planar robot manipulator.

Tracking control scheme for an underwater vehicle-manipulator system with single and multiple sub-regions and sub-task objectives

This study presents a novel tracking control scheme for an underwater vehicle-manipulator system (UVMS) where the proposed controller is not only used to track the prescribed sub-region but also allows the use of the self-motion to perform various sub-tasks (i.e. drag minimisation, obstacle avoidance and manipulability) because of the kinematically redundant system. In the proposed control scheme, the desired primary task of the UVMS is specified as two sub-regions that are assigned for the vehicle and end-effector. Despite the parametric uncertainty associated with the underwater dynamic model, the controller ensures the sub-task tracking without affecting the sub-region and attitude tracking control objective. The Lyapunov-type approach is utilised to design the controller and an extension to an adaptive-robust control scheme with multiple sub-regions and sub-task objectives is also performed to illustrate the flexibility of the approach. The presence of variable ocean currents creates hydrodynamic forces and moments that are not well known or predictable, even though they are bounded. Therefore the control task of tracking a prescribed sub-region trajectory is challenging because of these additive bounded disturbances. Furthermore, multiple sub-task criteria that are formulated using a weighted-sum approach are added to the control objective. Simulation results are presented to demonstrate the performance of the proposed control law.

Neural network-based nonlinear tracking control of kinematically redundant robot manipulators

In this paper, neural network-based nonlinear dynamical control of kinematically redundant robot manipulators is considered. The neural network-based controller achieves end-effector trajectory tracking as well as subtask tracking effectively. A feedforward neural network is employed to learn the parametric uncertainties, existing in the dynamical model of the robot manipulator. The whole system is shown to be stable in the sense of Lyapunov. Numerical simulation studies are carried out for a 3R planar robot manipulator to show the effectiveness of the control scheme.

Robust tracking control of kinematically redundant robot manipulators subject to multiple self-motion criteria

SUMMARYIn this study, we consider a model based robust control scheme for kinematically redundant robot manipulators that also enables the use of self motion of the manipulator to perform multiple sub-tasks (e.g., maintaining manipulability, avoidance of mechanical joint limits, and obstacle avoidance). The controller proposed ensures uniformly ultimately bounded end-effector and sub-task tracking despite the parametric uncertainty associated with the dynamic model. A Lyapunov based approach has been utilized in the controller design and extension to a non minimum set of parameters for orientation representation has been presented to illustrate the flexibility of the approach. Extensive simulation studies performed initially on a 3 link planar robot arm (for the planar case) and on a six degree of freedom (DOF) Puma type robot arm (for the 3D case with quaternion feedback) are presented to demonstrate the capabilities and the performance of the controller. The results were then experimentally tested on an actual Puma 560 robot to illustrate the feasibility of the proposed method.

Nonlinear Tracking Control of Kinematically Redundant Robot Manipulators

In this short paper, we consider the nonlinear control of kinematically redundant robot manipulators. Specifically, we use a Lyapunov technique to design a model-based nonlinear controller that achieves exponential link position and subtask tracking. We note that the control strategy does not require the computation of positional inverse kinematics and does not place any restriction on the self-motion of the manipulator; hence, the extra degrees of freedom are available for subtasks (i.e., maintaining manipulability, avoidance of mechanical limits and obstacle avoidance). Experimental implementations on a redundant robot are also included to illustrate the performance of the proposed control law.