The Experts below are selected from a list of 43293 Experts worldwide ranked by ideXlab platform
Dennis S. Bernstein - One of the best experts on this subject based on the ideXlab platform.
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adaptive control of uncertain hammerstein systems with hysteretic nonlinearities
Conference on Decision and Control, 2014Co-Authors: Mohammad Al Janaideh, Dennis S. BernsteinAbstract:We numerically investigate the sense in which an adaptive control law achieves internal model control of Hammerstein plants with Prandtl-Ishlinskii hysteresis. We apply retrospective cost adaptive control (RCAC) to a command-following problem for uncertain Hammerstein systems with hysteretic input nonlinearities. The only required modeling information of the linear plant is a single Markov Parameter. Describing functions are used to determine whether the adaptive controller inverts the plant at the exogenous frequencies.
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a numerical investigation of phase and magnitude compensation in adaptive control of uncertain hammerstein systems with hysteretic nonlinearities
Advances in Computing and Communications, 2014Co-Authors: Mohammad Al Janaideh, Dennis S. BernsteinAbstract:We apply retrospective cost adaptive control (RCAC) to a command-following problem for uncertain Hammerstein systems with Duhem hysteresis nonlinearities. The only required modeling information of the linear plant is a single Markov Parameter. We numerically investigate the sense in which RCAC achieves internal model control. The properties of the asymptotic controller are analyzed by using phase shift calculations.
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adaptive control of uncertain linear systems with uncertain hysteretic input nonlinearities
ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference DSCC 2012-MOVIC 2012, 2012Co-Authors: Al M Janaideh, Anthony Damato, D Sumer, B Drincic, Khaled F Aljanaideh, Dennis S. BernsteinAbstract:We apply retrospective cost adaptive control (RCAC) with auxiliary nonlinearities to a command-following problem for uncertain Hammerstein systems with rate-dependent hysteretic input nonlinearities. The only required modeling information of the linear plant is a single Markov Parameter. To account for the hysteretic input nonlinearity, RCAC uses auxiliary nonlinearities that reflect the monotonicity properties of the input nonlinearity. The hysteresis nonlinearity is modeled using the rate-dependent Prandtl-Ishlinskii model.Copyright © 2012 by ASME
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frequency domain stability analysis of retrospective cost adaptive control for systems with unknown nonminimum phase zeros
Conference on Decision and Control, 2011Co-Authors: Anthony Damato, Dogan E Sumer, Dennis S. BernsteinAbstract:We develop a multi-input, multi-output direct adaptive controller for discrete-time, possibly nonminimum-phase, systems with unknown nonminimum-phase zeros. The adaptive controller requires limited modeling information about the system, specifically, Markov Parameters from the control input to the performance variables. Often, only a single Markov Parameter is required, even in the nonminimum-phase case. We analysis the stability of the algorithm using a time-and-frequency-domain approach. We demonstrate the algorithm on disturbance-rejection problems, where the disturbance spectra are unknown. This controller is based on a retrospective performance objective, where the controller is updated using either batch or recursive least squares.
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robustness of retrospective cost adaptive control to Markov Parameter uncertainty
Conference on Decision and Control, 2011Co-Authors: Dogan E Sumer, Anthony Damato, Alexey V Morozov, Jesse B Hoagg, Dennis S. BernsteinAbstract:In this paper we investigate the robustness of an extended version of retrospective cost adaptive control (RCAC), in which less modeling information is required than in prior versions of this method. RCAC is applicable to MIMO possibly nonminimum-phase (NMP) plants without the need to know the locations of the NMP zeros. The only required modeling information is an FIR approximation of the plant, which may be based on a limited number of Markov Parameters. In this paper we investigate the effect of phase mismatch between the true plant and the FIR approximation. Numerical examples demonstrate the relationship between phase mismatch at the command and disturbance frequencies as well as the required level of regularization in the controller update.
Jesse B Hoagg - One of the best experts on this subject based on the ideXlab platform.
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robustness of retrospective cost adaptive control to Markov Parameter uncertainty
Conference on Decision and Control, 2011Co-Authors: Dogan E Sumer, Anthony Damato, Alexey V Morozov, Jesse B Hoagg, Dennis S. BernsteinAbstract:In this paper we investigate the robustness of an extended version of retrospective cost adaptive control (RCAC), in which less modeling information is required than in prior versions of this method. RCAC is applicable to MIMO possibly nonminimum-phase (NMP) plants without the need to know the locations of the NMP zeros. The only required modeling information is an FIR approximation of the plant, which may be based on a limited number of Markov Parameters. In this paper we investigate the effect of phase mismatch between the true plant and the FIR approximation. Numerical examples demonstrate the relationship between phase mismatch at the command and disturbance frequencies as well as the required level of regularization in the controller update.
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retrospective cost adaptive control for nonminimum phase discrete time systems part 1 the ideal controller and error system
Conference on Decision and Control, 2010Co-Authors: Jesse B Hoagg, Dennis S. BernsteinAbstract:We present a direct adaptive controller for discrete-time (and thus sampled-data) systems that are possibly nonminimum phase. The adaptive control algorithm requires limited model information, specifically, knowledge of the first nonzero Markov Parameter and the nonminimum-phase zeros (if any) of the transfer function from the control to the performance. This adaptive control algorithm is effective for stabilization as well as for command following and disturbance rejection, where the command and disturbance spectra are unknown. The novel aspect of this controller is the use of a retrospective performance, which is minimized using either an instantaneous or cumulative retrospective cost function.
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Discrete-Time Adaptive Command Following and Disturbance Rejection With Unknown Exogenous Dynamics
IEEE Transactions on Automatic Control, 2008Co-Authors: Jesse B Hoagg, Mario A. Santillo, Dennis S. BernsteinAbstract:We present an adaptive controller that requires limited model information for stabilization, command following, and disturbance rejection for mult-input multi-output minimum-phase discrete-time systems. Specifically, the controller requires knowledge of the open-loop system's relative degree as well as a bound on the first nonzero Markov Parameter. Notably, the controller does not require knowledge of the command or the disturbance spectrum as long as the command and disturbance signals are generated by a Lyapunov-stable linear system. Thus, the command and disturbance signals are combinations of discrete-time sinusoids and steps. In addition, the Markov-Parameter-based adaptive controller uses feedback action only, and thus does not require a direct measurement of the command or disturbance signals. Using a logarithmic Lyapunov function, we prove global asymptotic convergence for command following and disturbance rejection as well as Lyapunov stability of the adaptive system when the open-loop system is asymptotically stable.
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Discrete-Time Adaptive Command Following and Disturbance Rejection with Unknown Exogenous Dynamics
Proceedings of the 45th IEEE Conference on Decision and Control, 2006Co-Authors: Jesse B Hoagg, Dennis S. BernsteinAbstract:We present an adaptive controller that requires limited model information for stabilization, command following, and disturbance rejection for multi-input, multi-output minimum-phase discrete-time systems. Specifically, the controller requires knowledge of the open-loop system's relative degree and a bound on the first nonzero Markov Parameter. Notably, the controller does not require knowledge of the command or disturbance spectrum as long as the command and disturbance signals are generated by Lyapunov-stable linear systems. Thus, the command and disturbance signals are combinations of discrete-time sinusoids and steps. In addition, the controller uses feedback action only and thus does not require a direct measurement of the command or disturbance signals. We prove global asymptotic convergence for command following and disturbance rejection
J C Geromel - One of the best experts on this subject based on the ideXlab platform.
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cal h _ infty filtering of discrete time Markov jump linear systems through linear matrix inequalities
IEEE Transactions on Automatic Control, 2009Co-Authors: Alim P C Goncalves, Andre R Fioravanti, J C GeromelAbstract:This technical note addresses the discrete-time Markov jump linear systems H infin filtering design problem. First, under the assumption that the Markov Parameter is measurable, the main contribution is the linear matrix inequality (LMI) characterization of all linear filters such that the estimation error remains bounded by a given H infin norm level, yielding the complete solution of the mode-dependent filtering design problem. Based on this result, a robust filter design able to deal with polytopic uncertainty is considered. Second, from the same LMI characterization, a design procedure for mode-independent filtering is proposed. Some examples are solved for illustration and comparisons.
Jeng-shyang Pan - One of the best experts on this subject based on the ideXlab platform.
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State estimation for discrete-time Markov jump linear systems with time-correlated and mode-dependent measurement noise
Automatica, 2017Co-Authors: Wei Liu, Peng Shi, Jeng-shyang PanAbstract:Abstract The state estimation problem for discrete-time Markov jump linear systems corrupted by time-correlated and mode-dependent measurement noise is considered where the time-correlated and mode-dependent measurement noise is described via a discrete-time stochastic system with Markov Parameter and Kronecker delta function. By defining the measurement noise in this manner, both time-correlation and periodic step change caused by the change of system environment or structure can be embodied in the measurement noise. A novel “distributed measurement differencing method” is applied to the problem of state estimation under consideration so that two algorithms are obtained using some results presented in this paper. The first algorithm is optimal in the sense of minimum mean-square error, which can exactly compute the minimum mean-square error estimate of system state. The second algorithm is suboptimal and the suboptimality of the algorithm is caused by using some Gaussian hypotheses. The two proposed algorithms are recursive and the proposed suboptimal algorithm has a time-independent complexity. The performance of the proposed suboptimal algorithm is illustrated using computer simulations.
Marcos G Todorov - One of the best experts on this subject based on the ideXlab platform.
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a detector based approach for the h_ 2 control of Markov jump linear systems with partial information
IEEE Transactions on Automatic Control, 2015Co-Authors: O L V Costa, Marcelo D Fragoso, Marcos G TodorovAbstract:In this paper, we study the $H_{2} $ -control for discrete-time Markov Jump Linear Systems (MJLS) with partial information . We consider the case in which we do not have access to the Markov jump Parameter but, instead, there is a detector that emits signals which provides information on this Parameter. A salient feature of our formulation is that it encompasses, for instance, the cases with perfect information, no information and cluster observations of the Markov Parameter, which were previously analyzed in the Markov jump control literature. The goal is to derive a feedback linear control using the information provided by the detector in order to stochastically stabilize the closed loop system. We present two Lyapunov like equations for the stochastic stability of the system. In addition, we show that a Linear Matrix Inequalities (LMI) formulation can be obtained in order to design a stochastically stabilizing feedback control. In the sequel we deal with the $H_{2} $ control problem and we show that, again, an LMI optimization problem can be formulated in order to design a stochastically stabilizing feedback control with guaranteed $H_{2} $ -cost. We also present two special cases, one of them always satisfied for the limit case in which the detector provides perfect information on the Markov Parameter, and the Bernoulli jump case, under which LMI conditions become necessary and sufficient for the stochastic stabilizability of the system and the LMI optimization problems provide the optimal $H_{2} $ cost. For the Bernoulli jump case we show that our formulation generalizes previous ones. The case with convex polytopic uncertainty on the Parameters of the system and on the transition probability matrix is also considered. The paper is concluded with some numerical examples.