Integral Control of Stable MIMO Nonlinear Systems with Input Constraints

Lorenzetti, Pietro; Weiss, George H.

doi:10.1016/j.ifacol.2021.10.354

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…The saturation of the control inputs is approximated by tanh functions, and the radial basis function neural networks are exploited to deal with unknown nonlinear functions in MIMO system [3]. The projected dynamical system theory is adopted in [7] to design an anti-windup MIMO low-gain integral controller, where the integrator state is forced to stay in a permitted region. The unknown actuator nonlinearity included the saturation is investigated by a powerful Nussbaum analysis tool, which is solved by the adaptive control method [8].…”

Section: Introductionmentioning

confidence: 99%

Bounded UDE-based Robust Control for MIMO Systems with Application to Vector Control of a PMSM Drive

Wang¹

2023

Preprint

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<p>In this paper, two open problems of the existing bounded UDE-based robust control method are investigated: the input constraint with multiple control inputs, and the performance degradation caused by the introduced additional time-varying control variable. A modified bounded UDE-based robust control method is developed for the MIMO system with the inputs constrained by a general positive defined matrix, which is a more general condition of the norm constraint for control inputs. A nonlinear designed is further proposed to modified the direct multiplication of the additional time-varying variable to mitigate the performance degradation of the existing bounded UDE-based robust control, and the guidance of the nonlinear design procedures is provided. The proposed design is applied to the vector control of a PMSM drive system, and the simulation studies are provided to validate the proposed design under voltage constraint of the vector control with both normal operation and voltage constraint triggered.</p>

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Section: Introductionmentioning

confidence: 99%

Bounded UDE-based Robust Control for MIMO Systems with Application to Vector Control of a PMSM Drive

Wang¹

2023

Preprint

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“…3. A preliminary version of this paper, considering only integral control (τ p = 0), without the block N , and with no numerical example, has been presented in our recent conference paper Lorenzetti and Weiss (2021). Here, we introduce an additional degree of freedom in the controller design, namely, the block N from Fig.…”

Section: Introductionmentioning

confidence: 99%

PI control of stable nonlinear plants using projected dynamical systems

Lorenzetti¹,

Weiss²

2022

Preprint

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This paper presents a novel anti-windup proportional-integral controller for stable multi-input multi-output nonlinear plants. We use tools from projected dynamical systems theory to force the integrator state to remain in a desired (compact and convex) region, such that the plant input steady-state values satisfy the operational constraints of the problem. Under suitable monotonicity assumptions on the plant steady-state input-output map, we use singular perturbation theory results to prove the existence of a sufficiently small controller gain ensuring closed-loop (local) exponential stability and reference tracking for a feasible set of constant references. We suggest a particular controller design, which embeds (when possible) the right inverse of the plant steady-state input-output map. The relevance of the proposed controller scheme is validated through an application in the power systems domain, namely, the output (active and reactive) power regulation for a grid-connected synchronverter.

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PI control of stable nonlinear plants using projected dynamical systems

Lorenzetti

Weiss

2022

Automatica

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Integral Control of Stable MIMO Nonlinear Systems with Input Constraints

Cited by 7 publications

References 13 publications

Bounded UDE-based Robust Control for MIMO Systems with Application to Vector Control of a PMSM Drive

Bounded UDE-based Robust Control for MIMO Systems with Application to Vector Control of a PMSM Drive

PI control of stable nonlinear plants using projected dynamical systems

PI control of stable nonlinear plants using projected dynamical systems

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