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Adaptive Control Law (adaptive + control_law)

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Engineering100%

Selected Abstracts

Neural network-based adaptive attitude tracking control for flexible spacecraft with unknown high-frequency gain

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 6 2010
Qinglei Hu
Abstract Adaptive control design using neural networks (a) is investigated for attitude tracking and vibration stabilization of a flexible spacecraft, which is operated at highly nonlinear dynamic regimes. The spacecraft considered consists of a rigid body and two flexible appendages, and it is assumed that the system parameters are unknown and the truncated model of the spacecraft has finite but arbitrary dimension as well, for the purpose of design. Based on this nonlinear model, the derivation of an adaptive control law using neural networks (NNs) is treated, when the dynamics of unstructured and state-dependent nonlinear function are completely unknown. A radial basis function network that is used here for synthesizing the controller and adaptive mechanisms is derived for adjusting the parameters of the network and estimating the unknown parameters. In this derivation, the Nussbaum gain technique is also employed to relax the sign assumption for the high-frequency gain for the neural adaptive control. Moreover, systematic design procedure is developed for the synthesis of adaptive NN tracking control with L2 -gain performance. The resulting closed-loop system is proven to be globally stable by Lyapunov's theory and the effect of the external disturbances and elastic vibrations on the tracking error can be attenuated to the prescribed level by appropriately choosing the design parameters. Numerical simulations are performed to show that attitude tracking control and vibration suppression are accomplished in spite of the presence of disturbance torque/parameter uncertainty. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Adaptive synchronization for nonlinear FitzHugh,Nagumo neurons in external electrical stimulation

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 9 2008
Chung-Wen Lai
Abstract This paper investigates the synchronization problem for FitzHugh,Nagumo (FHN) neurons in external electrical stimulations. Using the sliding mode control technique, an adaptive control law is established that guarantees synchronization even when the parameters of the master and slave FHN neurons are fully unknown. A proportional-integral switching surface is introduced to simplify the task of assigning the stability of the closed-loop error system in the sliding mode. Furthermore, the proposed synchronization scheme is then applied to a secure communication system. Computer simulations are provided to verify the effectiveness of the proposed adaptive synchronization scheme. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Nonsingular path following control of a unicycle in the presence of parametric modelling uncertainties

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 10 2006
L. Lapierre
Abstract A new type of control law is derived to steer the dynamic model of a wheeled robot of unicycle type along a desired path. The methodology adopted for path following control deals explicitly with vehicle dynamics and plant parameter uncertainty. Furthermore, it overcomes stringent initial condition constraints that are present in a number of path following control strategies described in the literature. This is done by controlling explicitly the rate of progression of a ,virtual target' to be tracked along the path, thus bypassing the problems that arise when the position of the virtual target is simply defined by the projection of the actual vehicle on that path. In the paper, a nonlinear adaptive control law is derived that yields convergence of the (closed-loop system) path following error trajectories to zero. Controller design relies on Lyapunov theory and backstepping techniques. Simulation results illustrate the performance of the control system proposed. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Modeling and control for a Gough-Stewart platform CNC machine

JOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 11 2004
Yung Ting
In this paper, a complete dynamic model on task space for a 6 degrees of freedom (DOF) Gough-Stewart platform-type computer numerical control (CNC) machine is derived. The rotation terms of the legs are included for those inertia effects cannot be negligible in the machine tool applications. The formulation derived by means of the Euler-Lagrange method is convenient for designing the adaptive control law. Also, the average-type force model for end milling process is derived and included in the dynamic model and control. A composite adaptive control scheme is developed by use of filtering dynamics technique. An appropriate estimator gain is designed in the parameter adaptation law that is useful for estimating the selected important cutting parameters. Experimental results verify the proposed adaptive control scheme can achieve good tracking performance. © 2004 Wiley Periodicals, Inc. [source]

Adaptive tracking control for electrically-driven robots without overparametrization

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 2 2002
Yeong-Chan Chang
Abstract This paper addresses the motion tracking control of robot systems actuated by brushed direct current motors in the presence of parametric uncertainties and external disturbances. By using the integrator backstepping technique, two kinds of adaptive control schemes are developed: one requires the measurements of link position, link velocity and armature current for feedback and the other requires only the measurements of link position and armature current for feedback. The developed adaptive controllers guarantee that the resulting closed-loop system is locally stable, all the states and signals are bounded, and the tracking error can be made as small as possible. The attraction region can be not only arbitrarily preassigned but also explicitly constructed. The main novelty of the developed adaptive control laws is that the number of parameter estimates is exactly equal to the number of unknown parameters throughout the entire electromechanical system. Consequently, the phenomenon of overparametrization, a significant drawback of employing the integrator backstepping technique to treat the control of electrically driven robots in the previous literature, is eliminated in this study. Finally, simulation examples are given to illustrate the tracking performance of electrically driven robot manipulators with the developed adaptive control schemes. Copyright © 2002 John Wiley & Sons, Ltd. [source]

ADAPTIVE SLIDING MODE BACKSTEPPING CONTROL OF NONLINEAR SYSTEMS WITH UNMATCHED UNCERTAINTY

ASIAN JOURNAL OF CONTROL, Issue 4 2004
Ali J. Koshkouei
ABSTRACT This paper considers an adaptive backstepping algorithm for designing the control for a class of nonlinear continuous uncertain processes with disturbances that can be converted to a parametric semi-strict feedback form. Sliding mode control using a combined adaptive backstepping sliding mode control (SMC) algorithm, is also studied. The algorithm follows a systematic procedure for the design of adaptive control laws for the output tracking of nonlinear systems with matched and unmatched uncertainty. [source]