Embedded MPC Controller Based on Interior‐Point Method with Convergence Depth Control

Ding, Yi; Xu, Zuhua; Zhao, Jun; Wang, Kexin; Shao, Zili

doi:10.1002/asjc.1299

Cited by 5 publications

(5 citation statements)

References 30 publications

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“…Compared with [20][21][22][23][24][25][26][27][28][29][30][31][32][33], there are three advantages. Firstly, output feedback is considered in this paper.…”

Section: Remarkmentioning

confidence: 99%

“…From this point of view, considerable amount of attention has been paid to the problem of stabilization and control of time-delay systems. Predictive control is a good method with the ability to handle constraints and time-delays [19][20][21][22][23]. Now, it has become one of the most popular control methodologies no matter in theory or the reality (see [24][25][26][27]).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural Network Predictive Control for Autonomous Underwater Vehicle with Input Delay

Zhao

2018

Journal of Control Science and Engineering

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A path tracking controller is designed for an autonomous underwater vehicle (AUV) with input delay based on neural network (NN) predictive control algorithm. To compensate for the time-delay in control system and realize the purpose of path tracking, a predictive control algorithm is proposed. An NN is used to estimate the nonlinear uncertainty of AUV induced by hydrodynamic coefficients and the coupling of the surge, sway, and yaw angular velocity. By Lyapunov theorem, stability analysis is also given. Simulation results show the effectiveness of the proposed control strategy.

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“…Compared with [20][21][22][23][24][25][26][27][28][29][30][31][32][33], there are three advantages. Firstly, output feedback is considered in this paper.…”

Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Network Predictive Control for Autonomous Underwater Vehicle with Input Delay

Zhao

2018

Journal of Control Science and Engineering

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show abstract

“…In [13], the authors proposed an interesting method to identify the worst-case number of iterations required for the primal and dual activeset algorithms to reach optimality. Authors in [14] introduced the convergence depth control method into the interior-point method to accelerate the QP solving process for embedded MPC implementation.…”

Section: Introductionmentioning

confidence: 99%

Embedded Model Predictive Control Using Robust Penalty Method

Sharma¹,

Jugade²,

Yawalkar³

et al. 2022

Preprint

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Model predictive control (MPC) has become a hot cake technology for various applications due to its ability to handle multi-input multi-output systems with physical constraints. The optimization solvers require considerable time, limiting their embedded implementation for real-time control. To overcome the bottleneck of traditional quadratic programming (QP) solvers, this paper proposes a robust penalty method (RPM) to solve an optimization problem in a linear MPC. The main idea of RPM is to solve an unconstrained QP problem using Broyden-Fletcher-Goldfarb-Shannon (BFGS) algorithm. The beauty of this method is that it can find optimal solutions even if initial conditions are in an infeasible region, which makes it robust. Moreover, the RPM is computationally inexpensive as compared to the traditional QP solvers. The proposed RPM is implemented on resource-limited embedded hardware (STM32 microcontroller), and its performance is validated with a case study of a citation aircraft control problem. We show the hardware-in-the-loop co-simulation results of the proposed RPM and compared them with the active set method (ASM) and interior point method (IPM) QP solvers. The performance of MPC with the aforementioned solvers is compared by considering the optimality, time complexity, and ease of hardware implementation. Presented results show that the proposed RPM gives the same optimality as ASM and IPM, and outperforms them in terms of speed.

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“…Active set methods, interior-point methods, and gradient projection methods stand out as families of solution algorithms for MPC (23). Variants of these methods have been developed as found in (16,(24)(25)(26)(27). Under the APS framework, active-set and interiorpoint methods were tested with different open-source packages for C language: qsOASES, CVXGEN, ECOS and QPC in (2), primal-dual interior-point based on the predicator-correction algorithm was used in (28), a newton projection method was implemented in (29), and in (22) CVXOPT package for python was utilized.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Different Embedded Systems and Open-Source Optimization Packages Towards an Impulsive MPC Artificial Pancreas

et al. 2021

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Current technological advances have brought closer to reality the project of a safe, portable, and efficient artificial pancreas for people with type 1 diabetes (T1D). Among the developed control strategies for T1D, model predictive control (MPC) has been emphasized in literature as a promising control for glucose regulation. However, these control strategies are commonly designed in a computer environment, regardless of the limitations of a portable device. In this paper, the performances of six embedded platforms and three open-source optimization solver algorithms are assessed for T1D treatment. Their advantages and limitations are clarified using four MPC formulations of increasing complexity and a hardware-in-the-loop methodology to evaluate glucose control in virtual adult subjects. The performance comparison includes the execution time, the difference concerning the evolution obtained in MATLAB, the processor temperature, energy consumption, time percentage in normoglycemia, and the number of hypo- and hyperglycemic events. Results show that Quadprog is the package that faithfully follows the results obtained with control strategies designed and tuned on a computer with the MATLAB software. In addition, the Raspberry Pi 3 and the Tinker Board S embedded systems present the appropriate characteristics to be implemented as portable devices in the artificial pancreas application according to the criteria set out in this work.

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Embedded MPC Controller Based on Interior‐Point Method with Convergence Depth Control

Cited by 5 publications

References 30 publications

Neural Network Predictive Control for Autonomous Underwater Vehicle with Input Delay

Neural Network Predictive Control for Autonomous Underwater Vehicle with Input Delay

Embedded Model Predictive Control Using Robust Penalty Method

Performance Analysis of Different Embedded Systems and Open-Source Optimization Packages Towards an Impulsive MPC Artificial Pancreas

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