The Experts below are selected from a list of 102 Experts worldwide ranked by ideXlab platform
Munther A. Dahleh - One of the best experts on this subject based on the ideXlab platform.
-
Signal reconstruction in the presence of finite‐rate measurements: finite‐horizon control applications
International Journal of Robust and Nonlinear Control, 2010Co-Authors: Sridevi V. Sarma, Munther A. DahlehAbstract:In this paper, we study finite-length signal reconstruction over a finite-rate noiseless channel. We allow the class of signals to belong to a bounded ellipsoid and derive a universal lower bound on a worst-case reconstruction error. We then compute upper bounds on the error that arise from different coding schemes and under different causality assumptions. When the encoder and decoder are noncausal, we derive an upper bound that either achieves the universal lower bound or is comparable to it. When the decoder and encoder are both causal operators, we show that within a very broad class of causal coding schemes, memoryless coding prevails as optimal, imposing a hard limitation on reconstruction. Finally, we map our general reconstruction problem into two important control problems in which the plant and controller are local to each other, but are together driven by a remote reference signal that is transmitted through a finite-rate noiseless channel. The first problem is to minimize a finite-horizon weighted tracking error between the remote system output and a reference command. The second problem is to navigate the state of the remote system from a Nonzero Initial Condition to as close to the origin as possible in finite-time. Our analysis enables us to quantify the tradeoff between time horizon and performance accuracy, which is not well studied in the area of control with limited information as most works address infinite-horizon control objectives (e.g. stability, disturbance rejection). Copyright © 2009 John Wiley & Sons, Ltd.
-
Signal reconstruction under finite-rate measurements: Finite-horizon navigation application
2009Co-Authors: Sridevi V. Sarma, Munther A. DahlehAbstract:In this paper, we study finite-length signal reconstruction over a finite-rate noiseless channel. We allow the class of signals to belong to a bounded ellipsoid and derive a universal lower bound on a worst-case reconstruction error. We then compute upper bounds on the error that arise from different coding schemes and under different causality assumptions. We then map our general reconstruction problem into an important control problem in which the plant and controller are local to each other, but are together driven by a remote reference signal that is transmitted through a finite-rate noiseless channel. The problem is to navigate the state of the remote system from a Nonzero Initial Condition to as close to the origin as possible in finite-time. Our analysis enables us to quantify the tradeoff between time horizon and performance accuracy which is not well-studied in the area of control with limited information as most works address infinite-horizon control objectives (eg. stability, disturbance rejection).
Sridevi V. Sarma - One of the best experts on this subject based on the ideXlab platform.
-
Signal reconstruction in the presence of finite‐rate measurements: finite‐horizon control applications
International Journal of Robust and Nonlinear Control, 2010Co-Authors: Sridevi V. Sarma, Munther A. DahlehAbstract:In this paper, we study finite-length signal reconstruction over a finite-rate noiseless channel. We allow the class of signals to belong to a bounded ellipsoid and derive a universal lower bound on a worst-case reconstruction error. We then compute upper bounds on the error that arise from different coding schemes and under different causality assumptions. When the encoder and decoder are noncausal, we derive an upper bound that either achieves the universal lower bound or is comparable to it. When the decoder and encoder are both causal operators, we show that within a very broad class of causal coding schemes, memoryless coding prevails as optimal, imposing a hard limitation on reconstruction. Finally, we map our general reconstruction problem into two important control problems in which the plant and controller are local to each other, but are together driven by a remote reference signal that is transmitted through a finite-rate noiseless channel. The first problem is to minimize a finite-horizon weighted tracking error between the remote system output and a reference command. The second problem is to navigate the state of the remote system from a Nonzero Initial Condition to as close to the origin as possible in finite-time. Our analysis enables us to quantify the tradeoff between time horizon and performance accuracy, which is not well studied in the area of control with limited information as most works address infinite-horizon control objectives (e.g. stability, disturbance rejection). Copyright © 2009 John Wiley & Sons, Ltd.
-
Signal reconstruction under finite-rate measurements: Finite-horizon navigation application
2009Co-Authors: Sridevi V. Sarma, Munther A. DahlehAbstract:In this paper, we study finite-length signal reconstruction over a finite-rate noiseless channel. We allow the class of signals to belong to a bounded ellipsoid and derive a universal lower bound on a worst-case reconstruction error. We then compute upper bounds on the error that arise from different coding schemes and under different causality assumptions. We then map our general reconstruction problem into an important control problem in which the plant and controller are local to each other, but are together driven by a remote reference signal that is transmitted through a finite-rate noiseless channel. The problem is to navigate the state of the remote system from a Nonzero Initial Condition to as close to the origin as possible in finite-time. Our analysis enables us to quantify the tradeoff between time horizon and performance accuracy which is not well-studied in the area of control with limited information as most works address infinite-horizon control objectives (eg. stability, disturbance rejection).
Victor A Boichenko - One of the best experts on this subject based on the ideXlab platform.
-
Anisotropy-Based Analysis for the Case of Nonzero Initial Condition
Automation and Remote Control, 2019Co-Authors: Victor A BoichenkoAbstract:Usually in the context of the anisotropy-based robust performance analysis stochastic systems with zero Initial Condition are investigated. In this paper we extend this analysis and consider a linear discrete time invariant system under random disturbances and with the Nonzero Initial Condition. In accordance with the basic postulates of the anisotropy-based control theory the disturbance attenuation capabilities of system are quantified by the anisotropic generalized gain which is defined as the largest root mean square gain of the system with respect to a random input and the Nonzero Initial Condition, anisotropy of which is bounded by a given nonnegative parameter a .
-
on stochastic gain of linear systems with Nonzero Initial Condition
Mediterranean Conference on Control and Automation, 2017Co-Authors: Victor A Boichenko, Alexey A BelovAbstract:In this paper we consider a linear discrete time invariant system under random disturbances and with the Nonzero Initial Condition. By the analogy with the anisotropic norm of a stochastic system the disturbance attenuation capabilities of system are quantified by the anisotropic generalized gain which is defined as the largest root mean square gain of the system with respect to a random input and the Nonzero Initial Condition, anisotropy of which is bounded by a given nonnegative parameter a. A numerical example is given.
-
MED - On stochastic gain of linear systems with Nonzero Initial Condition
2017 25th Mediterranean Conference on Control and Automation (MED), 2017Co-Authors: Victor A Boichenko, Alexey A BelovAbstract:In this paper we consider a linear discrete time invariant system under random disturbances and with the Nonzero Initial Condition. By the analogy with the anisotropic norm of a stochastic system the disturbance attenuation capabilities of system are quantified by the anisotropic generalized gain which is defined as the largest root mean square gain of the system with respect to a random input and the Nonzero Initial Condition, anisotropy of which is bounded by a given nonnegative parameter a. A numerical example is given.
Guang-she Zhao - One of the best experts on this subject based on the ideXlab platform.
-
Iterative identification of block-oriented nonlinear systems based on biconvex optimization
Systems & Control Letters, 2015Co-Authors: Changyun Wen, Wei Xing Zheng, Guang-she ZhaoAbstract:Abstract We investigate the identification of a class of block-oriented nonlinear systems which is represented by a common model in this paper. Then identifying the common model is formulated as a biconvex optimization problem. Based on this, a normalized alterative convex search (NACS) algorithm is proposed under a given arbitrary Nonzero Initial Condition. It is shown that we only need to find the unique partial optimum point of a biconvex cost function in order to obtain its global minimum point. Thus, the convergence property of the proposed algorithm is established under arbitrary Nonzero Initial Conditions. By applying the results to Hammerstein–Wiener systems with an invertible nonlinear function, the long-standing problem on the convergence of iteratively identifying such systems under arbitrary Nonzero Initial Conditions is also now solved.
-
Iterative Method in the Identification of Block-Oriented Systems Based on Biconvex Optimization
IFAC Proceedings Volumes, 2012Co-Authors: Changyun Wen, Wei Xing Zheng, Guang-she ZhaoAbstract:Abstract In this paper, we investigate the identification of the class of block-oriented nonlinear systems presented by Li et al. [2011] by using an iterative method. Firstly a common model is proposed to represent such block-oriented systems. Then identifying the common model is formulated as a biconvex optimization problem. Based on this, a normalized alterative convex search (NACS) algorithm is proposed under a given arbitrary Nonzero Initial Condition. It is shown that we only need to find the unique partial optimum point of a biconvex cost function in the formulated optimization problem in order to obtain its global minimum point. Thus, the convergence property of the proposed algorithm is established under arbitrary Nonzero Initial Conditions. The approach presented in this paper provides a unified framework for the identification of block-oriented systems.
Alexey A Belov - One of the best experts on this subject based on the ideXlab platform.
-
on stochastic gain of linear systems with Nonzero Initial Condition
Mediterranean Conference on Control and Automation, 2017Co-Authors: Victor A Boichenko, Alexey A BelovAbstract:In this paper we consider a linear discrete time invariant system under random disturbances and with the Nonzero Initial Condition. By the analogy with the anisotropic norm of a stochastic system the disturbance attenuation capabilities of system are quantified by the anisotropic generalized gain which is defined as the largest root mean square gain of the system with respect to a random input and the Nonzero Initial Condition, anisotropy of which is bounded by a given nonnegative parameter a. A numerical example is given.
-
MED - On stochastic gain of linear systems with Nonzero Initial Condition
2017 25th Mediterranean Conference on Control and Automation (MED), 2017Co-Authors: Victor A Boichenko, Alexey A BelovAbstract:In this paper we consider a linear discrete time invariant system under random disturbances and with the Nonzero Initial Condition. By the analogy with the anisotropic norm of a stochastic system the disturbance attenuation capabilities of system are quantified by the anisotropic generalized gain which is defined as the largest root mean square gain of the system with respect to a random input and the Nonzero Initial Condition, anisotropy of which is bounded by a given nonnegative parameter a. A numerical example is given.
