Inverse optimal control on electric power conversion

Vega, Carlos J.; Alzate, Ricardo

doi:10.1109/ropec.2014.7036320

Cited by 13 publications

(17 citation statements)

References 11 publications

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“…Please note that the nonlinearities in the optimization and control stages in the operation and control design of HVDC systems motivate this research to propose a novel hierarchical control strategy that ensures the optimality and stability conditions in closedloop operation for this type of power system. There are several optimal control approaches that can adapt to this problem such as direct optimal control (DOC) [14], optimal LMI-based state feedback control (OFC-LMI) [15], inverse optimal control (IOC) [16,17], among others. The DOC method performs the optimization considering various limiting constraints, which are transcribed the several-dimensional problem to a finite-dimensional one [14].…”

Section: Motivationmentioning

confidence: 99%

“…The OFC-LMI approach is mainly used in problems with uncertain nonlinear systems, which can be represented as Lipschitz nonlinearities [15]. The IOC method is based on Lyapunov's control functions, guaranteeing asymptotic stability in nonlinear systems under optimal control law [16,17]. The problem with the first two approaches is that they may require much computation and not take advantage of the system topology to propose a simple control law.…”

Section: Motivationmentioning

confidence: 99%

“…The inverse optimal control design corresponds to a robust control theory from the family of control Lyapunov functions that allows ensuring asymptotic stability in nonlinear dynamical systems based on an optimal control law [16,17]. Let us consider a general nonlinear dynamical system as follows [29,30]:…”

Section: Global Stabilization Via Iocmentioning

confidence: 99%

“…where l(x) is a positive semidefinite function and R(x) is positive definite function on the states. Please note that a control law u fulfilling the requirement on J can be defined based on the following theorem [16], where l(x) and R −1 (x) are defined through the performance indicator; while the nonlinear functions f (x) and g(x) are provided by the dynamical system (7).…”

Section: Global Stabilization Via Iocmentioning

confidence: 99%

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Global Optimal Stabilization of MT-HVDC Systems: Inverse Optimal Control Approach

et al. 2021

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The stabilization problem of multi-terminal high-voltage direct current (MT-HVDC) systems feeding constant power loads is addressed in this paper using an inverse optimal control (IOC). A hierarchical control structure using a convex optimization model in the secondary control stage and the IOC in the primary control stage is proposed to determine the set of references that allows the stabilization of the network under load variations. The main advantage of the IOC is that this control method ensures the closed-loop stability of the whole MT-HVDC system using a control Lyapunov function to determine the optimal control law. Numerical results in a reduced version of the CIGRE MT-HVDC system show the effectiveness of the IOC to stabilize the system under large disturbance scenarios, such as short-circuit events and topology changes. All the simulations are carried out in the MATLAB/Simulink environment.

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Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Global Stabilization Via Iocmentioning

confidence: 99%

Section: Global Stabilization Via Iocmentioning

confidence: 99%

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Global Optimal Stabilization of MT-HVDC Systems: Inverse Optimal Control Approach

et al. 2021

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“…Formulation of CLF leading to the design of an optimal feedback controller for typical classes of systems have been discussed in literature (Freeman and Kokotovic, 1995; Freeman and Primbs, 1996; Galloway, 2015; Shamma and Cloutier, 2003; Yang and Lee, 2012). Further, CLF-based IOC design has been attempted for typical nonlinear systems with known CLFs (Horri et al, 2012; Vega and Alzate, 2008). However, the most challenging aspect of IOC via CLF is the determination of CLF itself, as there exists no explicit technique for the determination of CLF for general nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Inverse optimal control of a class of affine nonlinear systems

Prasanna

Jacob

Nandakumar

2019

Transactions of the Institute of Measurement and Control

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This paper proposes a systematic formulation of inverse optimal control (IOC) law based on a rather straightforward reduction of control Lyapunov function (CLF), applicable to a class of second-order nonlinear systems affine in the input. This method exploits the additional design degrees of freedom resulting from the non-uniqueness of the state dependent coefficient (SDC) formulation, which is widely used in pseudo-linear control techniques. The applicability of the proposed approach necessitates an apparently effortless SDC formulation satisfying an SDC matrix criterion in terms of the structure and characteristics of the state matrix, [Formula: see text]. Subsequently, a sufficient condition for the global asymptotic stability (g.a.s) of the closed-loop system is established. The SDC formulations conforming to the sufficient condition ensure the existence and determination of a smooth radially unbounded polynomial CLF of the form [Formula: see text], while offering a benevolent choice for the gain matrix [Formula: see text], in the CLF. The direct relationship between the gain matrix [Formula: see text] and state weighing matrix [Formula: see text] ensures optimization of an equivalent [Formula: see text]. This feature enables one to rightfully choose the gain matrix [Formula: see text] as per the performance requisites of the system. Finally, the application of the proposed methodology for the speed control of a permanent magnet synchronous motor validates the efficacy and design flexibility of the methodology.

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