Constrained Sliding-Mode Control: A Survey

Zambelli, Massimo; Ferrara, Antonella

doi:10.1007/978-3-030-36621-6_5

Cited by 7 publications

(3 citation statements)

References 55 publications

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“…where y, z, and 𝛼 respectively denote horizontal, vertical, and angular displacements, g is the gravitational acceleration, d ∈ R 3×1 is the vector of lumped force and moment disturbances, and the net thrust and moment acting on the fixed-rotor helicopter are proportional to inputs u 1 and u 2 . In this problem, the input constraints are |u 1 | < 10 m∕s 2 and |u 2 | < 20 rad∕s 2 .…”

Section: Simulation Studymentioning

confidence: 99%

“…In control systems implementations, to prevent certain critical conditions associated with the violation of constraints, the use of safe control methods has become an increasingly common practice, 1,2 and several methodologies have been extended in order to cope with constraints. For instance, sliding‐mode control (SMC), a nonlinear control method valued for its properties of robustness versus relevant classes of uncertainties, 3,4 has been coupled with barrier functions to provide barrier function‐based SMC approaches capable of controlling state constrained dynamical systems 5‐8 .…”

Section: Introductionmentioning

confidence: 99%

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A reinforcement learning‐based approach for near‐optimal sliding‐mode control of output‐constrained uncertain nonlinear systems

Mousavi

Markazi

Ferrara

2023

Intl J Robust & Nonlinear

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This study presents a near-optimal learning-based approach for sliding-mode control of uncertain nonsquare nonlinear systems subject to output constraints.To achieve a compromise between safety and optimality, a reinforcement learning algorithm is proposed to compute the near-optimal values of the sliding manifold coefficients. In the reinforcement learning algorithm, only measured input-output data obtained by experiments or simulations are employed. Furthermore, the presented method does not require partial knowledge of the system dynamics to initialize the reinforcement learning process. By employing the tuned sliding vector, an adaptive fuzzy sliding-mode control (AFSMC) input, including a fuzzy term and a robust term, is generated. The fuzzy term is used to approximate an unknown nonlinear function and the robust term is designed for mismatch compensation. To guarantee that the output constraints are not violated, the adaptation laws for obtaining the fuzzy singletons and the bounds of the approximation errors are designed based on the barrier Lyapunov functions (BLF) theorem. The closed loop asymptotic stability is theoretically analyzed in the article, while the effectiveness of the method is assessed in simulation.

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Section: Simulation Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A reinforcement learning‐based approach for near‐optimal sliding‐mode control of output‐constrained uncertain nonlinear systems

Mousavi

Markazi

Ferrara

2023

Intl J Robust & Nonlinear

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“…In addition, it should be mentioned that constrained sliding-mode control is an active research area, and we are not considering it in the proposed work. However, for completeness, we refer the reader to [35,36,55,56]. x 1 (t) and y 1 (t)…”

Section: A Static Sliding Modementioning

confidence: 99%

On the Control of the 2D Navier–Stokes Equations with Kolmogorov Forcing

2021

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This paper is devoted to the control problem of a nonlinear dynamical system obtained by a truncation of the two-dimensional (2D) Navier–Stokes (N-S) equations with periodic boundary conditions and with a sinusoidal external force along the x-direction. This special case of the 2D N-S equations is known as the 2D Kolmogorov flow. Firstly, the dynamics of the 2D Kolmogorov flow which is represented by a nonlinear dynamical system of seven ordinary differential equations (ODEs) of a laminar steady state flow regime and a periodic flow regime are analyzed; numerical simulations are given to illustrate the analysis. Secondly, an adaptive controller is designed for the system of seven ODEs representing the approximation of the dynamics of the 2D Kolmogorov flow to control its dynamics either to a steady-state regime or to a periodic regime; the value of the Reynolds number is determined using an update law. Then, a static sliding mode controller and a dynamic sliding mode controller are designed for the system of seven ODEs representing the approximation of the dynamics of the 2D Kolmogorov flow to control its dynamics either to a steady-state regime or to a periodic regime. Numerical simulations are presented to show the effectiveness of the proposed three control schemes. The simulation results clearly show that the proposed controllers work well.

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On the control allocation of fully actuated multirotor aerial vehicles

Santos

Bezerra

2022

Aerospace Science and Technology

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Constrained Sliding-Mode Control: A Survey

Cited by 7 publications

References 55 publications

A reinforcement learning‐based approach for near‐optimal sliding‐mode control of output‐constrained uncertain nonlinear systems

A reinforcement learning‐based approach for near‐optimal sliding‐mode control of output‐constrained uncertain nonlinear systems

On the Control of the 2D Navier–Stokes Equations with Kolmogorov Forcing

On the control allocation of fully actuated multirotor aerial vehicles

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