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In this paper, a new approach is proposed to design the stable closed-loop system with uncertainties to satisfy the pre-defined performance constraint. Performance-constrained stabilization of control systems is studied in this pa...
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In this paper, a new approach is proposed to design the stable closed-loop system with uncertainties to satisfy the pre-defined performance constraint. Performance-constrained stabilization of control systems is studied in this paper. The relationship between uncertainties and the performance of the system is established. When the uncertainties in the system satisfy certain conditions, our design method will provide a constant feedback gain to stabilize the system and to meet the performance requirement. Two examples are given to illustrate the usefulness of the method.
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In this paper, the design problem of dynamic output feedback controller for asymptotic stabilization of a class of neutral systems have been considered. A criterion for the existence of such controllers is derived based on the lin...
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In this paper, the design problem of dynamic output feedback controller for asymptotic stabilization of a class of neutral systems have been considered. A criterion for the existence of such controllers is derived based on the linear matrix inequality (LMI) approach combined with the Lyapunov method. A parameterized characterization of the controllers is given in terms of the feasible solutions to the LMIs, which can be solved by various convex optimization algorithms. A numerical example is given to illustrate the proposed design method. (C) 2004 Elsevier Ltd. All rights reserved.
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A list of 22 definitions of stability of a functional equation and 5 definitions of stability of the alternation of two functional equations is given as well as some simple examples.
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In this work, strategies to devise an optimal feedback control of probabilistic Boolean control networks (PBCNs) are discussed. Reinforcement learning (RL) based control is explored in order to minimize model design efforts and re...
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In this work, strategies to devise an optimal feedback control of probabilistic Boolean control networks (PBCNs) are discussed. Reinforcement learning (RL) based control is explored in order to minimize model design efforts and regulate PBCNs with high complexities. A Q-learning random forest ( QLRF) algorithm is proposed; by making use of the algorithm, state feedback controllers are designed to stabilize the PBCNs at a given equilibrium point. Further, by adopting QLRF stabilized closed-loop PBCNs, a Lyapunov function is defined, and a method to construct the same is presented. By utilizing such Lyapunov functions, a novel self-triggered control (STC) strategy is proposed, whereby the controller is recomputed according to a triggering schedule, resulting in an optimal control strategy while retaining the closed-loop PBCN stability. Finally, the results are verified using computer simulations. (c) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
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Conditions for stability of underwater vehicle dynamics are established as a function of vehicle shape, mass distribution and equilibrium velocity using methods from geometric mechanics. [References: 23]
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There has been recent interest in fast calculations of the tokamak axisymmetric vertical instability for real time feedback control purposes. It is shown that the maximum eigenvalue for the basic rigid version of this stability pr...
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There has been recent interest in fast calculations of the tokamak axisymmetric vertical instability for real time feedback control purposes. It is shown that the maximum eigenvalue for the basic rigid version of this stability problem can be obtained by finding the positive root to a simple scalar function. This function can be generalized to include plasma mass and has complexity linear in the number of conductive elements. The formulation is based on standard matrix decompositions of the fixed-geometry part of the eigenproblem. The calculation bottleneck is the summary of mutual inductances from the reconstructed equilibrium current density. The with-mass spectrum can be made fully real-valued by the addition of a critical amount of damping with negligible effect on the vertical growth rate. The calculation has been implemented in the plasma control system at the DIII-D tokamak and used in experiments.
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This research addresses the problem of finite-time tracking error constrained control for a class of non-strict stochastic nonlinear systems with unknown time-varying powers and multiple power terms. Based on the conversion from c...
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This research addresses the problem of finite-time tracking error constrained control for a class of non-strict stochastic nonlinear systems with unknown time-varying powers and multiple power terms. Based on the conversion from constrained tracking error to an unconstrained signal with the same effect, by adopting the backstepping technique together with adaptive neural network control, a controller with upper and lower time-varying power bounds is designed to meet the prescribed performance control scheme in finite-time. Finally, two simulation examples are shown to verify the effectiveness of the commendatory control method.& COPY; 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.
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A geometrical test for the unconditional stability of three-port networks is presented. The derivation is presented in full detail, starting from the concept of input mapping region as an indicator of unconditional stability. A no...
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A geometrical test for the unconditional stability of three-port networks is presented. The derivation is presented in full detail, starting from the concept of input mapping region as an indicator of unconditional stability. A novel set of (equivalent) parameters is then defined, which can serve to rapidly check the unconditional stability of a three-port network under test. Although the derivation is rather lengthy, the final part of the contribution shows that the practical application of the test is actually straightforward.
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The finite-time stochastic boundedness (FTSB) via the sliding mode control (SMC) approach is analyzed for Markovian jumping systems (MJSs) with time-delays. First, an integral switching surface is constructed. And to make sure the...
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The finite-time stochastic boundedness (FTSB) via the sliding mode control (SMC) approach is analyzed for Markovian jumping systems (MJSs) with time-delays. First, an integral switching surface is constructed. And to make sure the reachability of the sliding mode surface in a finite-time, an SMC law is designed. In addition, the delay-dependent criteria for FTSB are obtained over the reaching phase and the sliding motion phase. Furthermore, in line with linear matrix inequalities (LMIs), sufficient conditions are provided to guarantee the FTSB of systems over the whole finite-time interval. Lastly, an example is given to indicate the validity of the proposed approach. (c) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
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The problem of assigning all the poles of a closed-loop system in the specific disc D(alpha, r) by composite state feedback control is considered for discrete multiparameter singularly perturbed systems. A pole assignment of this ...
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The problem of assigning all the poles of a closed-loop system in the specific disc D(alpha, r) by composite state feedback control is considered for discrete multiparameter singularly perturbed systems. A pole assignment of this kind is referred to as a D-pole placement. A D-stability criterion for discrete systems is first proposed; a two-stage method is then developed to analyse the stability relationship between discrete multiparameter singularly perturbed systems and their corresponding slow and fast subsystems. Thereafter, the upper bound of mu, the ratio of the maximum eigenvalue in the fast subsystem to the minimum eigenvalue in the slow subsystem, is derived such that D-stability of the slow and fast subsystems implies that of the original system, provided that mu is within this bound. Finally, we propose an algorithm to design a composite feedback control law (i.e. the sum of slow control and fast control) to render the controlled discrete multiparameter singularly perturbed system D(alpha, r)-stable. [References: 21]
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