Symbolic Observer-Based Controller for Uncertain Nonlinear Systems

Apaza-Perez, W. A.; Girard, Antoine; Combastel, Christophe; Zolghadri, Ali

doi:10.1109/lcsys.2020.3034274

Cited by 12 publications

(3 citation statements)

References 34 publications

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“…To the knowledge of the authors, no works presenting a contract theory framework for perturbed systems currently exist. We also note that standard formal theory methods usually require special treatment when applied to perturbed or uncertain systems [33]- [35].…”

Section: B Contributionsmentioning

confidence: 99%

Verifying Contracts for Perturbed Control Systems using Linear Programming

Sharf¹,

Besselink²,

Johansson³

2021

Preprint

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Verifying specifications for large-scale control systems is of utmost importance, but can be hard in practice as most formal verification methods can not handle high-dimensional dynamics. Contract theory has been proposed as a modular alternative to formal verification in which specifications are defined by assumptions on the inputs to a component and guarantees on its outputs. In this paper, we present linear-programmingbased tools for verifying contracts for control systems. We first consider the problem of verifying contracts defined by time-invariant inequalities for unperturbed systems. We use kinduction to show that contract verification can be achieved by considering a collection of implications between inequalities, which are then recast as linear programs. We then move our attention to perturbed systems. We present a comparison-based framework, verifying that a perturbed system satisfies a contract by checking that the corresponding unperturbed system satisfies a robustified (and -approximated) contract. In both cases, we present explicit algorithms for contract verification, proving their correctness and analyzing their complexity. We also demonstrate the verification process for two case studies, one considering a two-vehicle autonomous driving scenario, and one considering formation control of a multi-agent system.

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Section: B Contributionsmentioning

confidence: 99%

Verifying Contracts for Perturbed Control Systems using Linear Programming

Sharf¹,

Besselink²,

Johansson³

2021

Preprint

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“…Due to its simplicity, obvious performance, and performance, the proportional-integral (PI) regulator constitutes one of the most commonly employed methods of regulation in electrical machines [5]. An efcient nonlinear control may nevertheless enhance activity in an environment of issues and ambiguity since the IM operates within a nonlinear system [6,7]. Several researchers have used a variety of cutting-edge control strategies to manage the power electronics and drive sector in this respect [8].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Based Experimental Investigations for Sliding Mode Control of an Induction Motor in Power Steering Applications

Parimalasundar,

Senthilkumar,

Kumar

et al. 2023

International Journal of Intelligent Systems

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Sliding mode control (SMC) of induction motor is a new concept in the current scenario, as it seeks to improve torque control accuracy and power steering efficiency through the use of pulse width modulation schemes. Furthermore, artificial neural network-based sliding mode control is applied to a squirrel cage induction motor, which is used in the steering control of automobiles. The artificial neural network (ANN)-based SMC is more popular due to its robustness and good stability in external parameter variation. Additionally, an SMC and an ANN-based SMC are employed to compute the torque and flux, improving performance for power steering applications. The performance of the designed model is validated through MATLAB/Simulink and experimental models with different controllers under various operating conditions. The controller has been embedded into a TMS320F28335 controller, and performances have been evaluated. The performance analyses of induction motor using different controllers are performed. The transient performances of induction motor such as delay time, rise time, settling time, and steady-state error are investigated. The proposed work is analysed by using a mathematical model and implemented in a test-bench model for validation.

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“…A symbolic observer-based controller was developed in [14] for uncertain nonlinear continuous or hybrid systems. The presented scheme was based on a dynamical system description of the symbolic models with a finite description of the transition relation between its states.…”

Section: -Introductionmentioning

confidence: 99%

Control-based observer for discrete-time nonlinear systems

Hassan

Aljuwaiser

2023

Nonlinear Dyn

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Observer-based controllers are well-known techniques and have been used to generate state feedback control strategies for incomplete state measured systems. However, the reverse process, namely control-based observers, has never been introduced as a tool to design observers for either linear or nonlinear systems. The main objective of this paper is to introduce such a technique as a tool to design observers for nonlinear discrete-time systems. The proposed approach, besides leading to very satisfactory estimation results for nonlinear systems, is unlike other widely used estimation techniques in the literature. More specifically, it has no limitations and/or restrictions, needs no state transformation, the overshoots at the start of the estimation process are either zero or very small, and last but not least the estimation errors converge to the zero steady states within a very short transient period. The stability of the proposed estimator is rigorously analyzed. Finally, the state estimation of the Permanent Magnet Synchronous Motor and a Hyperchaotic system are presented to illustrate the applicability and the efficiency of the developed approach.

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Symbolic Observer-Based Controller for Uncertain Nonlinear Systems

Cited by 12 publications

References 34 publications

Verifying Contracts for Perturbed Control Systems using Linear Programming

Verifying Contracts for Perturbed Control Systems using Linear Programming

Artificial Neural Network-Based Experimental Investigations for Sliding Mode Control of an Induction Motor in Power Steering Applications

Control-based observer for discrete-time nonlinear systems

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