Stabilizing terminal constraint-free nonlinear MPC via sliding mode-based terminal cost

Ji, Daxiong; Ren, Jie; Shi, Yang; Shi, Yang

doi:10.1016/j.automatica.2021.109898

Cited by 10 publications

(3 citation statements)

References 42 publications

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“…Compared with the above-mentioned anti-saturation method, model predictive control (MPC), which solves constrained optimization issues online, can generate quality control performance for coupled and saturated nonlinear systems (Ji et al, 2021). The adoption of numerous practical industrial platforms demonstrates that MPC has a vast range of development potential and practical applications.…”

Section: Introductionmentioning

confidence: 99%

Adaptive model predictive control based on neural networks for Hover attitude tracking with input saturation and unknown disturbances

Yang

et al. 2023

Transactions of the Institute of Measurement and Control

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The anti-disturbance control of quadrotor attitude tracking under saturation constraints is a difficult problem. In this paper, a neural network-based model predictive controller for quadrotor systems with input saturation and external disturbances is developed. The unmodeled dynamics and external disturbances of the system are simplified to the disturbance superimposed on the nominal system, and the gradient descent neural networks are used to complete the estimation and compensation of the disturbance. The adaptive model predictive controller is designed based on the nominal system. The disturbance value estimated by the neural network adaptively adjusts the control constraints in the model predictive controller. The robustness and anti-disturbance of the designed controller are analyzed. The experiments show that, compared to the robust model predictive control, the algorithm proposed in this paper reduces the steady-state mean errors of the yaw, pitch, and roll attitude channels of the Hover system. Specifically, the algorithm results in a decrease of 2.622%, 2.292%, and 1.192% without external disturbances and 2.056%, 4.17%, and 0.956% with outside disturbances. Experimental results confirm the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Adaptive model predictive control based on neural networks for Hover attitude tracking with input saturation and unknown disturbances

Yang

et al. 2023

Transactions of the Institute of Measurement and Control

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“…Garcia-Gabin combining SMC design techniques with MPC for air-conditioning solar power plants (Garcia-Gabin, Zambrano, & Camacho, 2009). Shi proposed a new terminal unconstrained MPC scheme by embedding an implicit SMC law in the designed terminal cost function (Ji, Ren, Liu, & Shi, 2021). Above works on MPC+SMC, the advantages of MPC and SMC are combined to improve the robustness of the system.…”

Section: Introductionmentioning

confidence: 99%

Distributed sliding-mode cloud predictive formation control of networked multi-agent systems with application to air-bearing spacecraft simulators

Chi

Lin

Ionescu

et al. 2023

Control Engineering Practice

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“…The proposed approach provides Input-to-State practically Stable properties. Meanwhile, in [14], an MPC is proposed for nonlinear systems based on a terminal cost function characterized by an implicit SMC law. However, in order to guarantee the asymptotic stability of the closed-loop system, a linearized model is required.…”

Section: Introductionmentioning

confidence: 99%

An Interval Predictor-Based Robust Control for a Class of Constrained Nonlinear Systems

Gutiérrez,

Ríos,

Mera

et al. 2023

2023 62nd IEEE Conference on Decision and Control (CDC)

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This paper proposes the design of a robust sampled-time controller to stabilize continuous-time nonlinear systems, taking into account state and input constraints. The proposed controller comprises the design of a robust control law, which is based on an interval predictor-based state-feedback controller and a Model Predictive Control (MPC) approach, which deals with the state and input constraints. The interval predictor-based state-feedback controller is designed based on a Lyapunov function approach that provides a safe set, where the state constraints are not transgressed. Out this set, the MPC is activated guaranteeing the fulfillment of the state and input constraints. The proposed switched control strategy guarantees the practical Uniform Asymptotic Stability of the considered nonlinear systems. A constructive method, based on linear matrix inequalities (LMIs), is proposed to compute the controller gains and the state of the system is not required. Some simulation results illustrate the feasibility of the proposed scheme.

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Stabilizing terminal constraint-free nonlinear MPC via sliding mode-based terminal cost

Cited by 10 publications

References 42 publications

Adaptive model predictive control based on neural networks for Hover attitude tracking with input saturation and unknown disturbances

Adaptive model predictive control based on neural networks for Hover attitude tracking with input saturation and unknown disturbances

Distributed sliding-mode cloud predictive formation control of networked multi-agent systems with application to air-bearing spacecraft simulators

An Interval Predictor-Based Robust Control for a Class of Constrained Nonlinear Systems

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