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Chun-yi Su - One of the best experts on this subject based on the ideXlab platform.
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Adaptive Control for the Systems Preceded by Hysteresis
IEEE Transactions on Automatic Control, 2008Co-Authors: Xinkai Chen, Chun-yi Su, Toshio FukudaAbstract:Hysteresis hinders the effectiveness of smart materials in sensors and actuators. It is a challenging task to Control the systems with hysteresis. This note discusses the Adaptive Control for discrete time linear dynamical systems preceded with hysteresis described by the Prandtl-Ishlinskii model. The time delay and the order of the linear dynamical system are assumed to be known. The contribution of the note is the fusion of the hysteresis model with Adaptive Control techniques without constructing the inverse hysteresis nonlinearity. Only the parameters (which are generated from the parameters of the linear system and the density function of the hysteresis) directly needed in the formulation of the Controller are Adaptively estimated online. The proposed Control law ensures the global stability of the closed-loop system, and the output tracking error can be Controlled to be as small as required by choosing the design parameters. Simulation results show the effectiveness of the proposed algorithm.
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model reference Adaptive Control of continuous time systems with an unknown input dead zone
IEE Proceedings - Control Theory and Applications, 2003Co-Authors: Xingsong Wang, Henry Hong, Chun-yi SuAbstract:The Adaptive Control of continuous-time linear dynamic systems preceded by an unknown dead-zone in state space form is discussed. A lemma to simplify the error equation between the plant and the matching reference model is introduced which allows the development of a robust Adaptive Control scheme by involving the dead-zone inverse terms. This Adaptive Control law ensures global stability of the entire system and achieves the desired tracking precision even when the slopes of the dead-zone are unequal. Simulations performed on a typical linear system illustrate and clarify the validity of this approach.
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robust Adaptive Control of a class of nonlinear systems with unknown dead zone
Conference on Decision and Control, 2001Co-Authors: Xingsong Wang, Chun-yi Su, Henry HongAbstract:This paper deals with the Adaptive Control of a class of continuous-time nonlinear dynamic systems preceded by an unknown dead-zone. By using a new description of a dead-zone and by exploring the properties of this dead-zone model intuitively and mathematically, a robust Adaptive Control scheme is developed without constructing the dead-zone inverse. The new Adaptive Control law ensures global stability of the Adaptive system and achieves desired tracking precision. Simulations performed on a typical nonlinear system illustrate and clarify the validity of this approach.
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robust Adaptive Control of a class of nonlinear systems with unknown backlash like hysteresis
IEEE Transactions on Automatic Control, 2000Co-Authors: Chun-yi Su, Yury Stepanenko, J Svoboda, T P LeungAbstract:Deals with Adaptive Control of a class of nonlinear dynamic systems preceded by unknown backlash-like hysteresis nonlinearities, where the hysteresis is modeled by a differential equation. By exploiting solution properties of the differential equation and combining those properties with Adaptive Control techniques, a robust Adaptive Control algorithm is developed without constructing a hysteresis inverse. The new Control law ensures global stability of the Adaptive system and achieves both stabilization and tracking to within a desired precision. Simulations performed on a nonlinear system illustrate and clarify the approach.
Itzhak Barkana - One of the best experts on this subject based on the ideXlab platform.
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simple Adaptive Control a stable direct model reference Adaptive Control methodology brief survey
International Journal of Adaptive Control and Signal Processing, 2014Co-Authors: Itzhak BarkanaAbstract:SUMMARY In spite of successful proofs of stability and even successful demonstrations of performance, the eventual use of model reference Adaptive Control methodologies in practical real-world systems has met a rather strong resistance from practitioners and has remained very limited. Apparently, the practitioners have a hard time understanding the conditions that can guarantee stable operations of Adaptive Control systems under realistic operational environments. Besides, it is difficult to measure the robustness of Adaptive Control system stability and allow it to be compared with the common and widely used measure of phase margin and gain margin that is utilized by present, mainly LTI, Controllers. Furthermore, recent counterexamples seem to show that Adaptive systems may diverge even when all required conditions are fulfilled. This paper attempts to revisit the fundamental qualities of the common direct model reference Adaptive Control methodology based on gradient and to show that some of its basic drawbacks have been addressed and eliminated within the so-called simple Adaptive Control methodology. The sufficient conditions that guarantee stability are clearly stated and lead to similarly clear proofs of stability. The main claim of the paper is that if sufficient information exists for a robust classical design Control, same information can be used to implement robust simple Adaptive Controllers. As many real world applications show, the added value of using add-on Adaptive Control techniques, the use is pushing the desired performance beyond any previous limits. The paper also shows that the previous counterexamples to model reference Adaptive Control become just simple, successful, and stable applications of simple Adaptive Control. Copyright © 2013 John Wiley & Sons, Ltd.
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improving the performance of existing missile autopilot using simple Adaptive Control
International Journal of Adaptive Control and Signal Processing, 2014Co-Authors: Ilan Rusnak, Haim Weiss, Itzhak BarkanaAbstract:SUMMARY A simple add-on Adaptive Control algorithm is presented. The paper demonstrates via example that the performance of existing missile autopilot can be improved. The algorithm involves the synthesis of parallel feedforward, which guarantees that the Controlled plant is almost strictly positive real. It is proved in the paper that such a parallel feedforward always exists. The proof is based on the parameterization of a set of stabilizing Controllers. This parameterization enables straightforward design and implementation of the add-on simple Adaptive Control algorithm. Copyright © 2014 John Wiley & Sons, Ltd.
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improving the performance of existing missile autopilot using simple Adaptive Control
IFAC Proceedings Volumes, 2011Co-Authors: Ilan Rusnak, Haim Weiss, Itzhak BarkanaAbstract:Abstract A simple add-on Adaptive Control algorithm is presented. It is demonstrated via example that the performance of existing missile autopilot can be improved. The algorithm involves the synthesis of parallel feedforward which guarantees that the Controlled plant is almost strictly positive real (ASPR). It is proved in the paper that such a parallel feedforward always exists. The proof is based on the parameterization of a set of stabilizing Controllers. This parameterization enables straight-forward design and implementation of the add-on simple Adaptive Control (SAC) algorithm.
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simple Adaptive Control a stable direct model reference Adaptive Control methodology brief survey
IFAC Proceedings Volumes, 2007Co-Authors: Itzhak BarkanaAbstract:Abstract In spite of successful proofs of stability and even successful demonstrations of performance, the use of Model Reference Adaptive Control (MRAC) methodologies in practical real world systems has met a rather strong resistance from practitioners and has remained very limited. Apparently, the practitioners have a hard time understanding the conditions for stable operations of Adaptive Control systems under realistic operational environments. Besides, it is difficult to measure the robustness of Adaptive Control system stability when compared with the common measure of phase margin and gain margin that is utilized by present, mainly LTI, Controllers. This paper attempts to revisit the fundamental qualities of the common gradient-based MRAC methodology and to show that some of its basic drawbacks have been addressed and eliminated within the so-called Simple Adaptive Control methodology. Sufficient conditions that guarantee stability are clearly stated and lead to similarly clear proofs of stability and to successful and stable applications of SAC.
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technical communique mitigation of symmetry condition in positive realness for Adaptive Control
Automatica, 2006Co-Authors: Itzhak Barkana, Marcelo C M TeixeiraAbstract:Feasibility of nonlinear and Adaptive Control methodologies in multivariable linear time-invariant systems with state-space realization {A,B,C} is apparently limited by the standard strictly positive realness conditions that imply that the product CB must be positive definite symmetric. This paper expands the applicability of the strictly positive realness conditions used for the proofs of stability of Adaptive Control or Control with uncertainty by showing that the not necessarily symmetric CB is only required to have a diagonal Jordan form and positive eigenvalues. The paper also shows that under the new condition any minimum-phase systems can be made strictly positive real via constant output feedback. The paper illustrates the usefulness of these extended properties with an Adaptive Control example.
Gang Tao - One of the best experts on this subject based on the ideXlab platform.
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a multiple model Adaptive Control scheme for multivariable systems with uncertain actuation signs
Advances in Computing and Communications, 2017Co-Authors: Chang Tan, Gang Tao, Hui YangAbstract:A new multiple-model Adaptive switching Control scheme, using a direct Adaptive Control approach to avoid Control gain estimate singularity, is developed for multivariable systems with uncertain actuation signs. Such an Adaptive Controller is capable of handling the Adaptive Control design condition uncertainty caused by actuation sign uncertainty. Multiple direct Adaptive Controllers are designed for all possible patterns of actuation signs, and each of them is updated by an Adaptive law corresponding to one particular actuation sign pattern. A Control switching mechanism is designed with multiple modified performance indexes based on normalized estimation errors, which is desirable for selecting the best Controller. An aircraft flight Control example is presented to show the effectiveness of the proposed Adaptive Control scheme.
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Adaptive Control of uncertain nonlinear aircraft systems using combined linearized models
IEEE Chinese Guidance Navigation and Control Conference, 2016Co-Authors: Yanjun Zhang, Gang Tao, Mou ChenAbstract:This paper proposes a new Adaptive Control scheme for uncertain non-canonical nonlinear aircraft systems, which expands the capacity of linearization-based Adaptive Control from local to semi-global. Local linearization-based design has been used for Adaptive Control of non-canonical nonlinear aircraft systems, due to certain desired capacity to handle complexity of non-canonical aircraft system dynamics for which most existing nonlinear Control methods are not applicable. As a benchmark study, this paper addresses a general longitudinal dynamic motion of rigid aircraft systems in a non-canonical form with unparametrizable uncertainties. A semi-global linearization-based Control method is developed for such aircraft systems by constructing a new reparametrization procedure based on T-S fuzzy system approximation using local linearized models and a relative degree formulation, and then developing an Adaptive Controller to ensure closed-loop stability and asymptotic output tracking for T-S fuzzy approximation aircraft systems. Simulation results verify the effectiveness of the proposed Control scheme.
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multivariable Adaptive Control
Automatica, 2014Co-Authors: Gang TaoAbstract:Adaptive Control is a Control methodology capable of dealing with uncertain systems to ensure desired Control performance. This paper provides an overview of some fundamental theoretical aspects and technical issues of multivariable Adaptive Control, and a thorough presentation of various Adaptive Control schemes for multi-input-multi-output systems, literature reviews on Adaptive Control foundations and multivariable Adaptive Control methods, and related technical problems. It covers some basic concepts and issues such as certainty equivalence, stability, tracking, robustness, and parameter convergence. It discusses some of the most important topics of Adaptive Control: plant uncertainty parametrization, stable Controller adaptation, and design conditions for different Adaptive Control schemes. The paper also presents a detailed study of well-developed multivariable model reference Adaptive Control theory and design techniques. It provides an introduction to multivariable Adaptive pole placement and Adaptive nonlinear Control, and it concludes by identifying some open research problems.
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modeling and model reference Adaptive Control of aircraft with asymmetric damage
Journal of Guidance Control and Dynamics, 2009Co-Authors: Yu Liu, Gang Tao, S M JoshiAbstract:This paper addresses some fundamental issues in Adaptive Control of aircraft with struc- tural damage. It presents a thorough study of linearized aircraft models with damage to obtain new details of system descriptions, such as coupling and partial derivatives of lateral and longitudinal dynamics. A detailed study of system invariance under damage conditions is performed for generic aircraft models to obtain key system characterizations for model reference Adaptive Control (MRAC), such as infinite zero structure and signs of high frequency gain matrices. A comprehensive study of multivariable MRAC systems in the presence of damage is performed to obtain critical design specifications for Adaptive flight Control, such as system and Controller parametrizations and Adaptive parameter up- date laws. Both analytical and simulation results are given to illustrate the design and performance of Adaptive Control systems for aircraft flight Control. Adaptive Control of aircraft in the presence of damage has been an important topic in the research of flight Control design for aircraft safety. Damage can cause uncertain parametric and structural variations, which requires new aircraft modeling and Control approaches. In Reference (1), a study of aircraft dynamics with damage is presented, and a neural network based Adaptive Control algorithm is introduced for Control of aircraft in the presence of structure uncertainties. In (2), equations of motion are introduced in detail for aircraft with asymmetric mass loss. In (3), we introduced a nonlinear aircraft model with partial wing damage, and illustrated linearization of such a model. In (4), real time identification of a damaged aircraft model is studied. A two-step identification process is introduced, which consists of an aircraft state estimation phase and an aerodynamic model identification step. With such a two-step process, the nonlinear part of the model identification is isolated in the first phase, and the aerodynamic parameter identification procedure is simplified to a linear one. A hybrid Adaptive Control method is proposed in (5) for Control of aircraft with damage. The Control design is based on a neural network parameter estimation blended with a direct Adaptive law. A stability and convergence analysis is presented for this Adaptive Control methodology. For accommodating unknown changes in the structure and parameters, multivariable MRAC designs offer many advantages. In (6), we introduced an MRAC design based on the LDS decomposition of the high frequency gain matrix for the Control of aircraft with multiple wing damage. The key design conditions are that, both the nominal and post-damage systems should have a uniform known modified interactor matrix, and the leading principal minors of their high frequency gain matrices should be nonzero with their signs unchanged. In (7), we studied linearization of nonlinear aircraft models under damage conditions and designed a multivariable MRAC scheme which does not require the knowledge of the signs of the high frequency gain matrix. Potential extension to aircraft flight Control systems with changing signs of the high frequency gain matrix remains a topic of future research.
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Adaptive Control design and analysis
2003Co-Authors: Gang TaoAbstract:Preface. 1. Introduction. 2. Systems Theory. 3. Adaptive Parameter Estimation. 4. Adaptive State Feedback Control. 5. Continuous-Time Model Reference Adaptive Control. 6. Discrete-Time Model Reference Adaptive Control. 7. Indirect Adaptive Control. 8. A Comparison Study. 9. Multivariable Adaptive Control. 10. Adaptive Control of Systems with Nonlinearities. Bibliography. Index.
Xingsong Wang - One of the best experts on this subject based on the ideXlab platform.
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model reference Adaptive Control of continuous time systems with an unknown input dead zone
IEE Proceedings - Control Theory and Applications, 2003Co-Authors: Xingsong Wang, Henry Hong, Chun-yi SuAbstract:The Adaptive Control of continuous-time linear dynamic systems preceded by an unknown dead-zone in state space form is discussed. A lemma to simplify the error equation between the plant and the matching reference model is introduced which allows the development of a robust Adaptive Control scheme by involving the dead-zone inverse terms. This Adaptive Control law ensures global stability of the entire system and achieves the desired tracking precision even when the slopes of the dead-zone are unequal. Simulations performed on a typical linear system illustrate and clarify the validity of this approach.
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robust Adaptive Control of a class of nonlinear systems with unknown dead zone
Conference on Decision and Control, 2001Co-Authors: Xingsong Wang, Chun-yi Su, Henry HongAbstract:This paper deals with the Adaptive Control of a class of continuous-time nonlinear dynamic systems preceded by an unknown dead-zone. By using a new description of a dead-zone and by exploring the properties of this dead-zone model intuitively and mathematically, a robust Adaptive Control scheme is developed without constructing the dead-zone inverse. The new Adaptive Control law ensures global stability of the Adaptive system and achieves desired tracking precision. Simulations performed on a typical nonlinear system illustrate and clarify the validity of this approach.
Bernstein, Dennis S. - One of the best experts on this subject based on the ideXlab platform.
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Data-Driven Retrospective Cost Adaptive Control for Flight Control Application
2021Co-Authors: Syed Aseem Ul Islam, Nguyen, Tam W., Kolmanovsky Ilya, Bernstein, Dennis S.Abstract:Unlike fixed-gain robust Control, which trades off performance with modeling uncertainty, direct Adaptive Control uses partial modeling information for online tuning. The present paper combines retrospective cost Adaptive Control (RCAC), a direct Adaptive Control technique for sampled-data systems, with online system identification based on recursive least squares (RLS) with variable-rate forgetting (VRF). The combination of RCAC and RLS-VRF constitutes data-driven RCAC (DDRCAC), where the online system identification is used to construct the target model, which defines the retrospective performance variable. This paper investigates the ability of RLS-VRF to provide the modeling information needed for the target model, especially nonminimum-phase (NMP) zeros. DDRCAC is applied to single-input, single-output (SISO) and multiple-input, multiple-output (MIMO) numerical examples with unknown NMP zeros, as well as several flight Control problems, namely, unknown transition from minimum-phase to NMP lateral dynamics, flexible modes, flutter, and nonlinear planar missile dynamics.Comment: 60 pages, 28 figures, accepted by AIAA Journal of Guidance, Control, and Dynamic
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Data-Driven Retrospective Cost Adaptive Control for Flight Control Application
2021Co-Authors: Syed Aseem Ul Islam, Nguyen, Tam W., Kolmanovsky Ilya, Bernstein, Dennis S.Abstract:Unlike fixed-gain robust Control, which trades off performance with modeling uncertainty, direct Adaptive Control uses partial modeling information for online tuning. The present paper combines retrospective cost Adaptive Control (RCAC), a direct Adaptive Control technique for sampled-data systems, with online system identification based on recursive least squares (RLS) with variable-rate forgetting (VRF). The combination of RCAC and RLS-VRF constitutes data-driven RCAC (DDRCAC), where the online system identification is used to construct the target model, which defines the retrospective performance variable. This paper investigates the ability of RLS-VRF to provide the modeling information needed for the target model, especially nonminimum-phase (NMP) zeros. DDRCAC is applied to single-input, single-output (SISO) and multiple-input, multiple-output (MIMO) numerical examples with unknown NMP zeros, as well as several flight Control problems, namely, unknown transition from minimum-phase to NMP lateral dynamics, flexible modes, flutter, and nonlinear planar missile dynamics.Comment: 60 pages, 28 figures, submitted for review to AIAA Journal of Guidance, Control, and Dynamic
