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Wei Chen - One of the best experts on this subject based on the ideXlab platform.

  • Smooth controller design for non-linear systems using multiple fixed models
    IET Control Theory & Applications, 2017
    Co-Authors: Jie Chen, Wei Chen
    Abstract:

    Multiple model adaptive control (MMAC) with second-Level Adaptation is a recently proposed methodology for dealing with systems where the parametric uncertainty is large. Compared with the multiple model switching scheme, the new scheme can lead to significant improvements in performance. Some research has been conducted using the new scheme, but all of the results concern linear systems with an adaptive identification model set. In this study, MMAC with second-Level Adaptation scheme is extended to non-linear systems in strict feedback form, and the fixed identification model set is under consideration. This is motivated by the fact that a smooth controller can lead to smooth performance and the fixed identification model set gains potential advantages over the adaptive identification model set, especially for the case that the parameters of the system change over the time. Design details are presented and the stability of MMAC with second-Level Adaptation using a fixed identification model set for non-linear systems is given, which has not been discussed before. Finally, two simulations are performed to show that this scheme performs much better than conventional schemes, including adaptive control and multiple-model switching schemes, in terms of convergence speed and transient performance.

  • The Rationale for Second Level Adaptation
    arXiv: Systems and Control, 2015
    Co-Authors: Kumpati S. Narendra, Yu Wang, Wei Chen
    Abstract:

    Recently, a new approach to the adaptive control of linear time-invariant plants with unknown parameters (referred to as second Level Adaptation), was introduced by Han and Narendra in [1]. Based on $N (\geq m+1)$ fixed or adaptive models of the plant, where $m$ is the dimension of the unknown parameter vector, an unknown parameter vector $\alpha\in R^{N}$ is estimated in the new approach, and in turn, is used to control the overall system. Simulation studies were presented in [1] to demonstrate that the new method is significantly better than those that are currently in use. In this paper, we undertake a more detailed examination of the theoretical and practical advantages claimed for the new method. In particular, the need for many models, the proof of stability, and the improvement in performance and robustness are explored in depth both theoretically and experimentally.

  • Extension of second Level Adaptation using multiple models to SISO systems
    2015 American Control Conference (ACC), 2015
    Co-Authors: Kumpati S. Narendra, Yu Wang, Wei Chen
    Abstract:

    Second Level Adaptation using multiple models for the adaptive control of linear systems with unknown parameters was introduced in [1], and its robustness properties were discussed in [2]. In both cases, the plant, the identification models, and the reference model were assumed to be described by state equations in companion form, with all state variables accessible.

  • ACC - Extension of second Level Adaptation using multiple models to SISO systems
    2015 American Control Conference (ACC), 2015
    Co-Authors: Kumpati S. Narendra, Yu Wang, Wei Chen
    Abstract:

    Second Level Adaptation using multiple models for the adaptive control of linear systems with unknown parameters was introduced in [1], and its robustness properties were discussed in [2]. In both cases, the plant, the identification models, and the reference model were assumed to be described by state equations in companion form, with all state variables accessible.

  • A combination of second Level Adaptation and switching scheme for uncertain linear system
    Proceedings of the 33rd Chinese Control Conference, 2014
    Co-Authors: Wei Chen, Jie Chen
    Abstract:

    This paper deals with the stability issues of multiple model adaptive control (MMAC) with second Level Adaptation at first, which was proposed as a new concept of adaptive control using multiple models. The basic structure of MMAC with second Level Adaptation is presented and the proof of stability for multiple fixed model set is available. Then a new approach about the combination of second Level Adaptation and switching is proposed for the systems where only a little prior information is known. The stability of the proposed approach is given and a simulation is used to show the feasibility of the approach.

Kumpati S. Narendra - One of the best experts on this subject based on the ideXlab platform.

  • The Rationale for Second Level Adaptation
    arXiv: Systems and Control, 2015
    Co-Authors: Kumpati S. Narendra, Yu Wang, Wei Chen
    Abstract:

    Recently, a new approach to the adaptive control of linear time-invariant plants with unknown parameters (referred to as second Level Adaptation), was introduced by Han and Narendra in [1]. Based on $N (\geq m+1)$ fixed or adaptive models of the plant, where $m$ is the dimension of the unknown parameter vector, an unknown parameter vector $\alpha\in R^{N}$ is estimated in the new approach, and in turn, is used to control the overall system. Simulation studies were presented in [1] to demonstrate that the new method is significantly better than those that are currently in use. In this paper, we undertake a more detailed examination of the theoretical and practical advantages claimed for the new method. In particular, the need for many models, the proof of stability, and the improvement in performance and robustness are explored in depth both theoretically and experimentally.

  • Extension of second Level Adaptation using multiple models to SISO systems
    2015 American Control Conference (ACC), 2015
    Co-Authors: Kumpati S. Narendra, Yu Wang, Wei Chen
    Abstract:

    Second Level Adaptation using multiple models for the adaptive control of linear systems with unknown parameters was introduced in [1], and its robustness properties were discussed in [2]. In both cases, the plant, the identification models, and the reference model were assumed to be described by state equations in companion form, with all state variables accessible.

  • ACC - Extension of second Level Adaptation using multiple models to SISO systems
    2015 American Control Conference (ACC), 2015
    Co-Authors: Kumpati S. Narendra, Yu Wang, Wei Chen
    Abstract:

    Second Level Adaptation using multiple models for the adaptive control of linear systems with unknown parameters was introduced in [1], and its robustness properties were discussed in [2]. In both cases, the plant, the identification models, and the reference model were assumed to be described by state equations in companion form, with all state variables accessible.

  • Stability, robustness, and performance issues in second Level Adaptation
    2014 American Control Conference, 2014
    Co-Authors: Kumpati S. Narendra, Yu Wang, Wei Chen
    Abstract:

    A new approach, described as second Level Adaptation, was introduced in [1] for the control of unknown linear time-invariant plants using multiple identification models. If θp ∈ Rn, the unknown parameter vector of an LTI system lies in the convex hull P(t0) of (n+1) vectors θi(t0) (initial values of adaptive vectors) in parameter space, it was shown that it lies also in the convex hull of θi(t)(i = 1, 2, ..., n+1), of the adaptive parameters of the identification models. If the representations of the plants and models are in companion form, and all the state variables are accessible, simulation results were presented to demonstrate that the new method would result in much better performance than conventional adaptive control. In this paper, all aspects of second Level Adaptation are critically reviewed. Following this, an analysis of the stability and robustness of the approach is undertaken, and detailed reasons are provided for the observed improvement in performance. Due to space limitations, most of the results described in the paper pertain to plants in companion form, with all state variables accessible. Towards the end of the paper, an effort is made to indicate how the same concepts can be extended to more general cases. These include plants whose matrices are not in companion form, and systems in which only the input and the output of the plant are accessible. The details of the latter problems will be included in a forthcoming paper [7]. The authors believe that the two papers, together, will make a convincing case for the use of multiple models in adaptive control.

  • ACC - Stability, robustness, and performance issues in second Level Adaptation
    2014 American Control Conference, 2014
    Co-Authors: Kumpati S. Narendra, Yu Wang, Wei Chen
    Abstract:

    A new approach, described as second Level Adaptation, was introduced in [1] for the control of unknown linear time-invariant plants using multiple identification models. If θ p ∈ R n , the unknown parameter vector of an LTI system lies in the convex hull P(t 0 ) of (n+1) vectors θ i (t 0 ) (initial values of adaptive vectors) in parameter space, it was shown that it lies also in the convex hull of θ i (t)(i = 1, 2, ..., n+1), of the adaptive parameters of the identification models. If the representations of the plants and models are in companion form, and all the state variables are accessible, simulation results were presented to demonstrate that the new method would result in much better performance than conventional adaptive control. In this paper, all aspects of second Level Adaptation are critically reviewed. Following this, an analysis of the stability and robustness of the approach is undertaken, and detailed reasons are provided for the observed improvement in performance. Due to space limitations, most of the results described in the paper pertain to plants in companion form, with all state variables accessible. Towards the end of the paper, an effort is made to indicate how the same concepts can be extended to more general cases. These include plants whose matrices are not in companion form, and systems in which only the input and the output of the plant are accessible. The details of the latter problems will be included in a forthcoming paper [7]. The authors believe that the two papers, together, will make a convincing case for the use of multiple models in adaptive control.

B.j. Mccarragher - One of the best experts on this subject based on the ideXlab platform.

  • Task-Level Adaptation using a discrete event controller for robotic assembly
    Proceedings of 1993 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS '93), 1993
    Co-Authors: B.j. Mccarragher
    Abstract:

    A task-Level adaptive controller is presented for the discrete event control of robotic assembly tasks. Using a Petri net model of the assembly task, velocity constraints are derived from which desired velocity commands are obtained. Due to modeling errors and uncertainties, the velocity commands may result in a suboptimal, unwanted contacts between the workpiece and the environment. Simple vector addition is used to adapt the velocity commands so as to avoid the unwanted contacts. Gram-Schmidt orthogonalization is used to ensure that the Adaptation does not violate any geometric constraints. An example is given with experimental results, demonstrating the viability and success of the Adaptation method.

  • IROS - Task-Level Adaptation using a discrete event controller for robotic assembly
    Proceedings of 1993 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS '93), 1993
    Co-Authors: B.j. Mccarragher
    Abstract:

    A task-Level adaptive controller is presented for the discrete event control of robotic assembly tasks. Using a Petri net model of the assembly task, velocity constraints are derived from which desired velocity commands are obtained. Due to modeling errors and uncertainties, the velocity commands may result in a suboptimal, unwanted contacts between the workpiece and the environment. Simple vector addition is used to adapt the velocity commands so as to avoid the unwanted contacts. Gram-Schmidt orthogonalization is used to ensure that the Adaptation does not violate any geometric constraints. An example is given with experimental results, demonstrating the viability and success of the Adaptation method.

Yu Wang - One of the best experts on this subject based on the ideXlab platform.

  • The Rationale for Second Level Adaptation
    arXiv: Systems and Control, 2015
    Co-Authors: Kumpati S. Narendra, Yu Wang, Wei Chen
    Abstract:

    Recently, a new approach to the adaptive control of linear time-invariant plants with unknown parameters (referred to as second Level Adaptation), was introduced by Han and Narendra in [1]. Based on $N (\geq m+1)$ fixed or adaptive models of the plant, where $m$ is the dimension of the unknown parameter vector, an unknown parameter vector $\alpha\in R^{N}$ is estimated in the new approach, and in turn, is used to control the overall system. Simulation studies were presented in [1] to demonstrate that the new method is significantly better than those that are currently in use. In this paper, we undertake a more detailed examination of the theoretical and practical advantages claimed for the new method. In particular, the need for many models, the proof of stability, and the improvement in performance and robustness are explored in depth both theoretically and experimentally.

  • Extension of second Level Adaptation using multiple models to SISO systems
    2015 American Control Conference (ACC), 2015
    Co-Authors: Kumpati S. Narendra, Yu Wang, Wei Chen
    Abstract:

    Second Level Adaptation using multiple models for the adaptive control of linear systems with unknown parameters was introduced in [1], and its robustness properties were discussed in [2]. In both cases, the plant, the identification models, and the reference model were assumed to be described by state equations in companion form, with all state variables accessible.

  • ACC - Extension of second Level Adaptation using multiple models to SISO systems
    2015 American Control Conference (ACC), 2015
    Co-Authors: Kumpati S. Narendra, Yu Wang, Wei Chen
    Abstract:

    Second Level Adaptation using multiple models for the adaptive control of linear systems with unknown parameters was introduced in [1], and its robustness properties were discussed in [2]. In both cases, the plant, the identification models, and the reference model were assumed to be described by state equations in companion form, with all state variables accessible.

  • Stability, robustness, and performance issues in second Level Adaptation
    2014 American Control Conference, 2014
    Co-Authors: Kumpati S. Narendra, Yu Wang, Wei Chen
    Abstract:

    A new approach, described as second Level Adaptation, was introduced in [1] for the control of unknown linear time-invariant plants using multiple identification models. If θp ∈ Rn, the unknown parameter vector of an LTI system lies in the convex hull P(t0) of (n+1) vectors θi(t0) (initial values of adaptive vectors) in parameter space, it was shown that it lies also in the convex hull of θi(t)(i = 1, 2, ..., n+1), of the adaptive parameters of the identification models. If the representations of the plants and models are in companion form, and all the state variables are accessible, simulation results were presented to demonstrate that the new method would result in much better performance than conventional adaptive control. In this paper, all aspects of second Level Adaptation are critically reviewed. Following this, an analysis of the stability and robustness of the approach is undertaken, and detailed reasons are provided for the observed improvement in performance. Due to space limitations, most of the results described in the paper pertain to plants in companion form, with all state variables accessible. Towards the end of the paper, an effort is made to indicate how the same concepts can be extended to more general cases. These include plants whose matrices are not in companion form, and systems in which only the input and the output of the plant are accessible. The details of the latter problems will be included in a forthcoming paper [7]. The authors believe that the two papers, together, will make a convincing case for the use of multiple models in adaptive control.

  • ACC - Stability, robustness, and performance issues in second Level Adaptation
    2014 American Control Conference, 2014
    Co-Authors: Kumpati S. Narendra, Yu Wang, Wei Chen
    Abstract:

    A new approach, described as second Level Adaptation, was introduced in [1] for the control of unknown linear time-invariant plants using multiple identification models. If θ p ∈ R n , the unknown parameter vector of an LTI system lies in the convex hull P(t 0 ) of (n+1) vectors θ i (t 0 ) (initial values of adaptive vectors) in parameter space, it was shown that it lies also in the convex hull of θ i (t)(i = 1, 2, ..., n+1), of the adaptive parameters of the identification models. If the representations of the plants and models are in companion form, and all the state variables are accessible, simulation results were presented to demonstrate that the new method would result in much better performance than conventional adaptive control. In this paper, all aspects of second Level Adaptation are critically reviewed. Following this, an analysis of the stability and robustness of the approach is undertaken, and detailed reasons are provided for the observed improvement in performance. Due to space limitations, most of the results described in the paper pertain to plants in companion form, with all state variables accessible. Towards the end of the paper, an effort is made to indicate how the same concepts can be extended to more general cases. These include plants whose matrices are not in companion form, and systems in which only the input and the output of the plant are accessible. The details of the latter problems will be included in a forthcoming paper [7]. The authors believe that the two papers, together, will make a convincing case for the use of multiple models in adaptive control.

Jun Wu - One of the best experts on this subject based on the ideXlab platform.

  • Architecture for Dynamic Protocol-Level Adaptation to Enhance Network Service Performance
    2006 IEEE IFIP Network Operations and Management Symposium NOMS 2006, 2006
    Co-Authors: K. Ravindran, Jun Wu
    Abstract:

    The paper describes a protocol management architecture to enhance the performance of network service provisioning to clientele. In our model, a service provider (SP) maintains multiple protocol modules to exercise the infrastructure resources, with each protocol exhibiting a certain degree of cost optimality in distinct operating regions of the network infrastructure. During run-time, the SP selects one of the protocol modules that can meet the client-requested service obligation against the current resource-Level operating conditions. The goal is to offer a sustained access to the service with a resource-optimal and QoS-compliant service offering. Accordingly, the protocol selection by SP needs to consider the tradeoff between `service sustainability' and `resource optimality' in various operating regions of the network. Often, a robust protocol is less efficient in infrastructure resource usage, and vice versa. Our model allows a `dynamic switching' from one protocol module to another at run-time based on the changing resource conditions. The paper describes `protocol switching' as an architectural foundation for building cost-effective network services. A sample network service: adaptive `content distribution network', is also described along with a simulation study

  • NOMS - Architecture for Dynamic Protocol-Level Adaptation to Enhance Network Service Performance
    2006 IEEE IFIP Network Operations and Management Symposium NOMS 2006, 2006
    Co-Authors: K. Ravindran, Jun Wu
    Abstract:

    The paper describes a protocol management architecture to enhance the performance of network service provisioning to clientele. In our model, a service provider (SP) maintains multiple protocol modules to exercise the infrastructure resources, with each protocol exhibiting a certain degree of cost optimality in distinct operating regions of the network infrastructure. During run-time, the SP selects one of the protocol modules that can meet the client-requested service obligation against the current resource-Level operating conditions. The goal is to offer a sustained access to the service with a resource-optimal and QoS-compliant service offering. Accordingly, the protocol selection by SP needs to consider the tradeoff between 'service sustainability' and 'resource optimality' in various operating regions of the network. Often, a robust protocol is less efficient in infrastructure resource usage, and vice versa. Our model allows a 'dynamic switching' from one protocol module to another at run-time based on the changing resource conditions. The paper describes 'protocol switching' as an architectural foundation for building cost-effective network services. A sample network service: adaptive 'content distribution network', is also described along with a simulation study.