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Adaptive Control Scheme (adaptive + control_scheme)
Selected AbstractsAdaptive repetitive control for resonance cancellation of a distributed solar collector fieldINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2009J. D. Álvarez Abstract This paper deals with modelling and control of the outlet temperature in a distributed solar collector field. The resonance dynamics characteristics of this kind of system are similar to those of tubular heat exchangers in the closed-loop system bandwidth when fast responses are required. Simple low-order rational models are unable to capture the resonance dynamics, which can be excited by changes in both the heat transfer fluid flow and solar irradiation. This paper proposes a new model derived from a similar model for a tubular heat exchanger. This model allows the use of low-order controllers, which can be extended to an adaptive control scheme to account for varying resonance frequencies, as a new functionality achieving fast, well-damped responses. Copyright © 2008 John Wiley & Sons, Ltd. [source] Neural network-based adaptive control of piezoelectric actuators with unknown hysteresisINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 1 2009Wen-Fang Xie Abstract This paper proposes a neural network (NN)-based adaptive control of piezoelectric actuators with unknown hysteresis. Based on the classical Duhem model described by a differential equation, the explicit solution to the equation is explored and a new hysteresis model is constructed as a linear model in series with a piecewise continuous nonlinear function. An NN-based dynamic pre-inversion compensator is designed to cancel out the effect of the hysteresis. With the incorporation of the pre-inversion compensator, an adaptive control scheme is proposed to have the position of the piezoelectric actuator track the desired trajectory. This paper has three distinct features. First, it applies the NN to online approximate complicated piecewise continuous unknown nonlinear functions in the explicit solution to Duhem model. Second, an observer is designed to estimate the output of hysteresis of piezoelectric actuator based on the system input and output. Third, the stability of the controlled piezoelectric actuator with the observer is guaranteed. Simulation results for a practical system validate the effectiveness of the proposed method in this paper. Copyright © 2008 John Wiley & Sons, Ltd. [source] Modeling and control for a Gough-Stewart platform CNC machineJOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 11 2004Yung 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] Creep and hysteresis compensation for nanomanipulation using atomic force microscopeASIAN JOURNAL OF CONTROL, Issue 2 2009Qinmin Yang Abstract In this paper, a novel scheme is presented to simultaneously compensate the inherent creep and hysteresis nonlinearities of a piezoelectric actuator while positioning the Atomic Force Microscope (AFM) tip. In order to mitigate these nonlinearities, creep and hysteresis phenomenon are first modeled separately by using the classical Prandtl-Ishlinskii (PI) operator. Then, a linear time-invariant (LTI) representation is obtained to identify the creep uncertainty and subsequently an adaptive control scheme is devised for the piezoelectric actuator to track a desired path in the presence of creep. An additional dynamic inversion loop is utilized by using an online approximator to offset the hysteresis effects without the need of identifying the parameters within the hysteresis model. Rigorous performance analysis is conducted using standard Lyapunov stability approach along with simulation results. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] Adaptive tracking control for electrically-driven robots without overparametrizationINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 2 2002Yeong-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] A levenberg,marquardt learning applied for recurrent neural identification and control of a wastewater treatment bioprocessINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 11 2009Ieroham S. Baruch The paper proposed a new recurrent neural network (RNN) model for systems identification and states estimation of nonlinear plants. The proposed RNN identifier is implemented in direct and indirect adaptive control schemes, incorporating a noise rejecting plant output filter and recurrent neural or linear-sliding mode controllers. For sake of comparison, the RNN model is learned both by the backpropagation and by the recursive Levenberg,Marquardt (L,M) learning algorithm. The estimated states and parameters of the RNN model are used for direct and indirect adaptive trajectory tracking control. The proposed direct and indirect schemes are applied for real-time control of wastewater treatment bioprocess, where a good, convergence, noise filtering, and low mean squared error of reference tracking is achieved for both learning algorithms, with priority of the L,M one. © 2009 Wiley Periodicals, Inc. [source] Observer-based adaptive robust control of a class of nonlinear systems with dynamic uncertainties,INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 4 2001Bin Yao Abstract In this paper, a discontinuous projection-based adaptive robust control (ARC) scheme is constructed for a class of nonlinear systems in an extended semi-strict feedback form by incorporating a nonlinear observer and a dynamic normalization signal. The form allows for parametric uncertainties, uncertain nonlinearities, and dynamic uncertainties. The unmeasured states associated with the dynamic uncertainties are assumed to enter the system equations in an affine fashion. A novel nonlinear observer is first constructed to estimate the unmeasured states for a less conservative design. Estimation errors of dynamic uncertainties, as well as other model uncertainties, are dealt with effectively via certain robust feedback control terms for a guaranteed robust performance. In contrast with existing conservative robust adaptive control schemes, the proposed ARC method makes full use of the available structural information on the unmeasured state dynamics and the prior knowledge on the bounds of parameter variations for high performance. The resulting ARC controller achieves a prescribed output tracking transient performance and final tracking accuracy in the sense that the upper bound on the absolute value of the output tracking error over entire time-history is given and related to certain controller design parameters in a known form. Furthermore, in the absence of uncertain nonlinearities, asymptotic output tracking is also achieved. Copyright © 2001 John Wiley & Sons, Ltd. [source] | ||||||||||