The Experts below are selected from a list of 28635 Experts worldwide ranked by ideXlab platform
Bayu Jayawardhana - One of the best experts on this subject based on the ideXlab platform.
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on the Internal Model principle in the coordination of nonlinear systems
IEEE Transactions on Control of Network Systems, 2014Co-Authors: Claudio De Persis, Bayu JayawardhanaAbstract:The role of the Internal Model principle is investigated in this paper for the coordination of relative-degree-one and relative-degree-two nonlinear systems. For relative-degree-one systems that are incrementally (output-feedback) passive, we propose Internal-Model-based distributed control laws which guarantee output synchronization to an invariant manifold driven by autonomous synchronized Internal Models. For relative-degree-two systems, we consider a different Internal-Model-based distributed control framework for solving a formation control problem where the agents have to track a reference signal available only to the leader agent. In both cases, the local controller is also able to reject the disturbance signals generated by a local exosystem.
Claudio De Persis - One of the best experts on this subject based on the ideXlab platform.
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on the Internal Model principle in the coordination of nonlinear systems
IEEE Transactions on Control of Network Systems, 2014Co-Authors: Claudio De Persis, Bayu JayawardhanaAbstract:The role of the Internal Model principle is investigated in this paper for the coordination of relative-degree-one and relative-degree-two nonlinear systems. For relative-degree-one systems that are incrementally (output-feedback) passive, we propose Internal-Model-based distributed control laws which guarantee output synchronization to an invariant manifold driven by autonomous synchronized Internal Models. For relative-degree-two systems, we consider a different Internal-Model-based distributed control framework for solving a formation control problem where the agents have to track a reference signal available only to the leader agent. In both cases, the local controller is also able to reject the disturbance signals generated by a local exosystem.
Zhicheng Zhao - One of the best experts on this subject based on the ideXlab platform.
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A new multi-Model Internal Model control scheme based on neural network
2008 7th World Congress on Intelligent Control and Automation, 2008Co-Authors: Zhicheng Zhao, Jianggang ZhangAbstract:Aiming at the practical plants with strong nonlinear characteristics, a new multi-Model Internal Model control (MIMC) strategy based on Gaussian potential function networks (GPFN) is proposed in this paper. The Internal Model is represented by GPFN and the corresponding controller can be got directly, which simplifies the control law design and analyses greatly. Meanwhile, the way of Model switch is developed based on fuzzy decision. This MIMC scheme avoids the complex calculation when adjusting the controller parameter and overcomes the switch vibration. Simulation results demonstrate that the strategy has advantage of Internal Model control (IMC) and multi-Model control and could achieve better system performance than the conventional IMC (CIMC).
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Adaptive Internal Model control of permanent magnet synchronous motor drive system
2005 International Conference on Electrical Machines and Systems, 2005Co-Authors: Xuejuan Shao, Jinggang Zhang, Zhicheng ZhaoAbstract:An adaptive Internal Model control method, which is formed by incorporating adaptive control and Internal Model control, is proposed to control the speed of the permanent magnet synchronous motor (PMSM) in this paper. The rule of adaptive law is obtained by using Lyapunov stability theory in order to make the parameters of plant Model approach the parameter of the controlled plant and gradually equal it. Meanwhile, the controller parameters can be adjusted online according to the Internal Model parameters. Simulation results show that the proposed adaptive Internal Model controller provides high-performance dynamic and static characteristics. Furthermore, comparing with the conventional Internal Model controller, it is more robust with regard to plant parameter variations and external load disturbance
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Fuzzy neural network Internal Model control
Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693), 2003Co-Authors: Jinggang Zhang, Zhicheng ZhaoAbstract:Aiming at general nonlinear system in industry process, this paper presents an algorithm on Internal Model control based on fuzzy neural network (FNN). The Internal Model and Internal Model controller respectively are represented by FNN and are analyzed. Simulation shows that the proposed algorithm has good properties in terms of robustness and tracking performance.
Aniruddha Datta - One of the best experts on this subject based on the ideXlab platform.
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Adaptive Internal Model control: the discrete-time case
International Journal of Adaptive Control and Signal Processing, 2001Co-Authors: Guillermo J. Silva, Aniruddha DattaAbstract:This paper considers the design and analysis of a discrete-time H2 optimal robust adaptive controller based on the Internal Model control structure. The certainty equivalence principle of adaptive control is used to combine a discrete-time robust adaptive law with a discrete-time H2 Internal Model controller to obtain a discrete-time adaptive H2 Internal Model control scheme with provable guarantees of stability and robustness. The approach used parallels the earlier results obtained for the continuous-time case. Nevertheless, there are some differences which, together with the widespread use of digital computers for controls applications, justifies a separate exposition. Copyright © 2001 John Wiley & Sons, Ltd.
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Adaptive Internal Model control: the discrete-time case
Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251), 1999Co-Authors: Guillermo J. Silva, Aniruddha DattaAbstract:This paper considers the design and analysis of a discrete-time H/sub 2/ optimal robust adaptive controller based on the Internal Model control structure. The certainty equivalence principle of adaptive control is used to combine a discrete-time robust adaptive law with a discrete-time H/sub 2/ Internal Model controller to obtain a discrete-time adaptive H/sub 2/ Internal Model control scheme with provable guarantees of stability and robustness. The approach used parallels the earlier results obtained for the continuous-time case. Nevertheless, there are some differences which, together with the widespread use of digital computers for controls applications, justifies a separate exposition.
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Adaptive Internal Model Control Schemes
Adaptive Internal Model Control, 1998Co-Authors: Aniruddha DattaAbstract:In the last two chapters, we have studied Internal Model control schemes and parameter estimators as two separate entities. Chapter 3 dealt with the Internal Model control of stable plants with known parameters while in Chapter 4 we developed on-line parameter estimation techniques for estimating the unknown parameters of a given plant. In this chapter, our main objective is to design Internal Model controllers for stable plants with unknown parameters. The intuitively obvious way of achieving such an objective is to design an on-line parameter estimator for estimating the unknown plant parameters as in Chapter 4, and then to use the techniques of Chapter 3 to design an Internal Model controller based on these parameter estimates. This approach of treating the estimated parameters as the true ones, and basing the control design on them is referred to in the adaptive literature as Certainty Equivalence. Although the estimated parameters in a certainty equivalence scheme rarely converge to the true values, nevertheless research in adaptive control theory over the last two decades has shown that many designs based on the certainty equivalence approach can be proven to be stable [19, 31, 32], Unfortunately, adaptive Internal Model control (AIMC) schemes were not included in this category, presumably because they arose in the context of industrial applications and consequently did not attract much attention from the theoreticians. Indeed, the literature on Adaptive Internal Model Control is replete with simulations and empirical studies showing the efficacy of certainty equivalence based adaptive Internal Model control schemes but hardly any instance exists where theoretical guarantees of stability and/or performance were obtained [40, 39]. Thus an important aspect of our treatment in this chapter will be the provable guarantees of stability, performance, etc. provided by the AIMC schemes to be designed.
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Adaptive Internal Model control
1998Co-Authors: Aniruddha DattaAbstract:From the Publisher: Written in a self-contained tutorial fashion, this research monograph successfully brings the latest theoretical advances in the design of robust adaptive systems to the realm of industrial applications. It provides a theoretical basis for analytically justifying some of the reported industrial successes of existing adaptive Internal Model control schemes, and enables the reader to synthesise adaptive versions of their own favourite robust Internal Model control scheme by combining it with a robust adaptive law. The net result is that earlier empirical IMC designs can now be systematically robustified or replaced altogether by new designs with assured guarantees of stability and robustness. Practising engineers, researchers and graduate students will find this book to be a valuable source of information on the subject.
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The theory and design of adaptive Internal Model control schemes
Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207), 1998Co-Authors: Aniruddha Datta, Lei XingAbstract:Develops a systematic theory for the design and analysis of adaptive Internal Model control schemes. The ubiquitous certainty equivalence principle of adaptive control is used to combine a robust adaptive law with robust Internal Model controllers to obtain adaptive Internal Model control schemes with provable guarantees of stability and robustness. Specific controller structures considered include those of the Model reference, "partial" pole placement, and H/sub 2/ and H/sub /spl infin// optimal control types. The results here not only provide a theoretical basis for analytically justifying some of the reported industrial adaptive Internal Model control schemes but also open up the possibility of synthesizing new ones by simply combining a robust adaptive law with a robust Internal Model controller structure.
Gong-you Tang - One of the best experts on this subject based on the ideXlab platform.
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IECON - Quasi-Internal Model-based vibration control for vehicle suspension systems
IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, 2013Co-Authors: Hao Su, Gong-you TangAbstract:This paper considers the vibration control for vehicle active suspension systems with road disturbances. A mathematical Model of road disturbances is established. Using the quasi-Internal Model control, a vibration control law is designed. The controller consists of a quasi-Internal Model compensator and a stabilization controller. The quasi-Internal Model compensator ensures control precision of the closed-loop system. Stabilization controller guarantees stability, robustness and other performance index of the closed-loop system. Numerical simulations illustrate the effectiveness of the control law.
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Quasi-Internal Model-based vibration control for vehicle suspension systems
IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, 2013Co-Authors: Hao Su, Gong-you TangAbstract:This paper considers the vibration control for vehicle active suspension systems with road disturbances. A mathematical Model of road disturbances is established. Using the quasi-Internal Model control, a vibration control law is designed. The controller consists of a quasi-Internal Model compensator and a stabilization controller. The quasi-Internal Model compensator ensures control precision of the closed-loop system. Stabilization controller guarantees stability, robustness and other performance index of the closed-loop system. Numerical simulations illustrate the effectiveness of the control law.
