This paper introduces a novel approach for state-space representation of linear time invariant (LTI) systems, so-called Future Inputs Elimination (FIE) method. It can be applied to single-input-single-output (SISO) or...
This paper introduces a novel approach for state-space representation of linear time invariant (LTI) systems, so-called Future Inputs Elimination (FIE) method. It can be applied to single-input-single-output (SISO) or multiple-input-multiple-output (MIMO) systems, continuous-time or discrete-time systems, whose dynamic equations are coupled or separated (uncoupled) in terms of their inputs and outputs. The FIE method closely parallels to the controllable canonical method when restricted to a class of SISO LTI systems. Moreover, it retains an easy implementation and effortless computation even for a class of MIMO LTI systems. The proposed approach may be used for representation of LTI systems with multiple or complex-conjugate poles. Many representative numerical examples are provided in order to illustrate the effectiveness of the elimination state-space method for representation of both SISO and MIMO LTI systems.
Multipliers can be used to guarantee both the Lyapunov stability and input-output stability of Lurye systems with time-invariant memoryless slope-restricted nonlinearities. If a dynamic multiplier is used there is no ...
In this paper, we propose a distributed Kalman filter(DKF) for the dynamical system with general random coefficients. In the proposed method, each estimator shares local innovation pairs with its neighbors to collecti...
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In this paper, we propose a distributed Kalman filter(DKF) for the dynamical system with general random coefficients. In the proposed method, each estimator shares local innovation pairs with its neighbors to collectively complete the estimation task. Further, we introduce a collective random observability condition by which the Lp-stability of the covariance matrix and the Lp-exponential stability of the homogeneous part of the estimation error equation can be established. In contrast, the stringent conditions on the coefficient matrices, such as independency and stationarity are not required. Besides, the stability of the DKF, i.e., the boundedness of the filtering errors, can be established. Finally, from the simulation result,we demonstrate the cooperative effect of the sensors.
Formal safety guarantees on the synthesis of controllers for stochastic systems can be obtained using correct-by-design approaches. These approaches often use abstractions as finite-state Markov Decision Processes (MD...
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Given the massive movement towards hybrid power sources and electrification in the marine industry, power conversion systems are increasingly needed to support the flexibility and efficiency required by shipboard elec...
Given the massive movement towards hybrid power sources and electrification in the marine industry, power conversion systems are increasingly needed to support the flexibility and efficiency required by shipboard electrical grid. Electromagnetic compatibility has been considered as a matter of concern for special ships and for navy designs. The increase in the volume of power electronics systems relative to the installed power onboard the ship brings EMC compliance as a key factor for the good operation of the electrical installation. This paper addresses some EMC aspects that need to be considered in the design and installation of electricalsystems incorporating power electronics equipment. Examples are used to highlight some of the solutions for limiting the electromagnetic interference that may occur during the operation of this equipment.
This paper studies the stabilizability and stabilization of continuous-time systems in the presence of stochastic multiplicative *** authors consider multi-input,multi-output(MIMO)linear time-invariant systems subject...
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This paper studies the stabilizability and stabilization of continuous-time systems in the presence of stochastic multiplicative *** authors consider multi-input,multi-output(MIMO)linear time-invariant systems subject to multiple static,structured stochastic uncertainties,and seek to derive fundamental conditions to ensure that a system can be stabilized under a mean-square *** the stochastic control framework,this problem can be considered as one of optimal control under state-or input-dependent random noises,while in the networked control setting,a problem of networked feedback stabilization over lossy communication *** authors adopt a mean-square small gain analysis approach,and obtain necessary and sufficient conditions for a system to be meansquare stabilizable via output *** single-input,single-output(SISO)systems,the condition provides an analytical bound,demonstrating explicitly how plant unstable poles,nonminimum phase zeros,and time delay may impose a limit on the uncertainty variance required for mean-square *** MIMO minimum phase systems with possible delays,the condition amounts to solving a generalized eigenvalue problem,readily solvable using linear matrix inequality optimization techniques.
Iris segmentation and localization in unconstrained environments are challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. Some existing methods in the literature h...
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Accurate parameter estimation has been a long-pursued objective in battery modeling and control practice. To this end, optimization of excitation to improve the estimation accuracy has been an emerging topic, since th...
Accurate parameter estimation has been a long-pursued objective in battery modeling and control practice. To this end, optimization of excitation to improve the estimation accuracy has been an emerging topic, since the quality of data critically determines the accuracy of estimation. However, there are several major drawbacks with existing approaches. First, the commonly used criterion for optimization, e.g., Fisher information, is limited in performance due to not considering the estimation bias caused by inevitable system uncertainties. In addition, alternative existing methods rely on a good a priori knowledge of the parameter to be estimated, which is intrinsically contradictory to the goal of estimation. To address these issues, we propose a reinforcement learning (RL) framework to learn the optimal policy for excitation generation that is robust to system uncertainties. In particular, the framework involves a non-additive objective/reward associated with the newly established optimization criterion, and a state augmentation technique is applied to address the ensuing challenge. It is shown that, when applied to estimate a key health-related battery electrochemical parameter, the RL-based approach achieves significantly higher objective value under nominal conditions, and reduces the estimation error by one-order-of-magnitude in the presence of uncertainties compared with the baseline in existing approaches.
In this work, a tube-based nearly optimal solution to motion planning in unknown workspaces is presented. The advantages of reactive motion planning are combined with a Policy Iteration Reinforcement Learning scheme t...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
In this work, a tube-based nearly optimal solution to motion planning in unknown workspaces is presented. The advantages of reactive motion planning are combined with a Policy Iteration Reinforcement Learning scheme to yield a novel solution for unknown workspaces that inherits provable safety, convergence and optimality. Moreover, in simply-connected workspaces, our method is proven to asymptotically provide the globally optimal path. Our method is compared against a provably asymptotically optimal RRT
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method, as well as a relevant reactive method and provides satisfactory performance, closely matching or outperforming the former.
A single study has addressed actuator failure reconstruction for the One-sided Lipschitz (OSL) family of nonlinear systems. The predicted fault vector in that work does not provide any insight into the underlying prob...
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