Constrained robust model predictive control embedded with a new data‐driven technique

Yang, Lingyi; Lu, Jiuwei; Xu, Yang; Li, D.; Xi, Yugeng

doi:10.1049/iet-cta.2019.1349

Cited by 19 publications

(9 citation statements)

References 39 publications

(59 reference statements)

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“…The ML methods make predictions by the data-driven methods [53,54]. The ML methods build statistical models on the basis of training data without model identification and use historical data to derive control strategy [55][56][57]. The ILC is applied to improve the predictive performance of the controller [58].…”

Section: Learning-based On Model Predictive Controlmentioning

confidence: 99%

A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms

et al. 2022

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The learning-based model predictive control (LB-MPC) is an effective and critical method to solve the path tracking problem in mobile platforms under uncertain disturbances. It is well known that the machine learning (ML) methods use the historical and real-time measurement data to build data-driven prediction models. The model predictive control (MPC) provides an integrated solution for control systems with interactive variables, complex dynamics, and various constraints. The LB-MPC combines the advantages of ML and MPC. In this work, the LB-MPC technique is summarized, and the application of path tracking control in mobile platforms is discussed by considering three aspects, namely, learning and optimizing the prediction model, the controller design, and the controller output under uncertain disturbances. Furthermore, some research challenges faced by LB-MPC for path tracking control in mobile platforms are discussed.

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Section: Learning-based On Model Predictive Controlmentioning

confidence: 99%

A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms

et al. 2022

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“…To ensure the stability of system (24), it is expected that z en (∞|0) = 0. Accumulate (45) from i = 0 to i = ∞, there is J ∞ ≤ 𝛾, where 𝛾 is the upper bound of J ∞ .…”

Section: Controller Designmentioning

confidence: 99%

“…where Proof. Define V n (k) = z en (k) T Pz en (k) as the Lyapunov function of system (24) and satisfy:…”

Section: Controller Designmentioning

confidence: 99%

See 1 more Smart Citation

Robust model predictive control for hypersonic vehicle with state‐dependent input constraints and parameter uncertainty

Yang

Wei

et al. 2021

Intl J Robust & Nonlinear

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In this article, a robust model predictive control strategy based on sum-of-squares (SOS-RMPC) is proposed for hypersonic vehicle with state-dependent input constraints and uncertain parameters. Based on feedback linearization model of hypersonic vehicle, the polytopic linear parameter varying error model with bounded disturbance is established for reference trajectory tracking. The real limits of the actual control inputs are transformed into the constraints of the virtual inputs equivalently with the state-dependent nonlinear functions. Further, the multivariate linear fitting is used to approximate the state-dependent input constraints into polynomials. A weighted composite virtual control is formulated as the combination of unconstrained control and auxiliary control. SOS technique is introduced to cast the polynomial constraints into the convex matrix SOS conditions via linear matrix inequality. The virtual control law can be obtained by solving a SOS-RMPC convex optimization with infinite horizon. The invariant set is designed for the undisturbed error states and the norm-bounding theory is applied to ensure that the predictive error states with disturbance can remain in the same invariant set. The real control law is obtained by inverse control. The simulation verifies the performance of the designed controller.

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“…Note that the model information of the system is implicated in its input-output data. Therefore, an inputmapping technique was proposed in [22,23] to online compensate for the impact of unknown dynamic by a linear combination of past data. Inspired by the inputmapping strategy, this paper develops an input-mapping sliding mode control (IMSMC) strategy for the system with unknown dynamic.…”

Section: Introductionmentioning

confidence: 99%

A co-design method of online learning SMC law via an input-mappping strategy

Yu¹,

Li²,

Zhao³

et al. 2022

Preprint

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The research on sliding mode control strategy is generally based on the robust approach. The larger parameter space consideration will inevitably sacrifice part of the performance. Recently, the data-driven sliding mode control method attracts much attention and shows excellent benefits in the fact that data is introduced to compensate the controller. Nevertheless, most of the research on data-driven sliding mode control relied on identification techniques, which limits its online applications due to the special requirements of the data. In this paper, an input-mapping technique is inserted into the design framework of sliding mode control to compensate for the influence generated by the unknown dynamic of the system. The major novelty of the proposed input-mapping sliding mode control strategy lies in that the sliding mode surface and the sliding mode controller are co-designed through online learning from historical input-output data to minimize an objective function. Then, the convergence rate of the system is improved significantly based on the method designed in this work. Finally, some simulations are provided to show the effectiveness and superiority of the proposed methods.

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Constrained robust model predictive control embedded with a new data‐driven technique

Cited by 19 publications

References 39 publications

A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms

A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms

Robust model predictive control for hypersonic vehicle with state‐dependent input constraints and parameter uncertainty

A co-design method of online learning SMC law via an input-mappping strategy

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