Embedded Model Predictive Control With Certified Real-Time Optimization for Synchronous Motors

Cimini, Gionata; Bernardini, Daniele; Levijoki, Stephen; Bemporad, Alberto

doi:10.1109/tcst.2020.2977295

Cited by 68 publications

(44 citation statements)

References 33 publications

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“…Another advantage of qpOASES is that it is suitable for nonlinear QPs owing to available abstraction tools that tailor the solver to the QP at hand and generate an optimized code that can also take the processor architecture into account, see, e.g., [190]- [192]. Finally, ODYS, a QP solver with low computational and memory requirements that employs an AS method, showed promising results with a drive system [102].…”

Section: A Solversmentioning

confidence: 99%

See 1 more Smart Citation

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Καραμανάκος

Liegmann

Geyer

et al. 2020

IEEE Open J. Ind. Applicat.

276

130

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Model predictive control (MPC) has established itself as a promising control methodology in power electronics. This survey paper highlights the most relevant MPC techniques for power electronic systems. These can be classified into two major groups, namely, MPC without modulator, referred to as direct MPC, and MPC with a subsequent modulation stage, known as indirect MPC. Design choices and parameters that affect the system performance, closed-loop stability and controller robustness are discussed. Moreover, solvers and control platforms that can be employed for the real-time implementation of MPC algorithms are presented. Finally, the MPC schemes in question are assessed, among others, in terms of design and computational complexity, along with their performance and applicability depending on the power electronic system at hand.

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Section: A Solversmentioning

confidence: 99%

“…Such systems include the aforementioned dSpace, and the realized sampling frequencies are in the order of 10 kHz [101], [103], [192]. In addition indirect MPC has also been implemented using OPAL RT, which combines multi-core CPUs linked to an FPGA, see [199], or even low-cost DSP-based platforms [102].…”

Section: B Systemsmentioning

confidence: 99%

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Καραμανάκος

Liegmann

Geyer

et al. 2020

IEEE Open J. Ind. Applicat.

276

130

View full text Add to dashboard Cite

show abstract

“…Standard techniques to linearise system dynamics include Taylor series expansion [68] up to a suitable degree of accuracy or taking the Jacobian of the system at piecewise locations to prove stability via quadratic functions. Nonlinear dynamics that can be approximated by linear piecewise varying functions are termed Continuous Piecewise Affine (CPA) [55], [69], [70]. In CPA techniques the state space is partitioned into polyhedral regions where system constraints and optimal linear approximations are stored as a lookup table.…”

Section: A Linearisation Techniquesmentioning

confidence: 99%

“…Recently QP solvers have been optimised for embedded applications [70] with sub-millisecond solver times. For nonlinear systems however, multivariable semi-infinite optimisation tasks are a significant challenge.…”

Section: Comparison Of Techniquesmentioning

confidence: 99%

A Review of Safe Online Learning for Nonlinear Control Systems

Osborne

Shin

Tsourdos

2021

2021 International Conference on Unmanned Aircraft Systems (ICUAS)

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Learning for autonomous dynamic control systems that can adapt to unforeseen environmental changes are of great interest but the realisation of a practical and safe online learning algorithm is incredibly challenging. This paper highlights some of the main approaches for safe online learning of stabilisable nonlinear control systems with a focus on safety certification for stability. We categorise a non-exhaustive list of salient techniques, with a focus on traditional control theory as opposed to reinforcement learning and approximate dynamic programming. This paper also aims to provide a simplified overview of techniques as an introduction to the field. It is the first paper to our knowledge that compares key attributes and advantages of each technique in one paper.

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“…To compensate the computational delay for the 3L-SNPC inverter, using a two-step horizon of prediction, a discrete set of 21 2 � 441 possible trajectories of the switching states has to be enumerated for the evaluation of the cost function, Figure 2(a). is proposition led to a large number of computational burden, which makes the real challenge to the actual digital system implementation [42]. To reduce the number of real-time evaluations, this paper proposes a modified two-step horizon prediction using a proposed "static voltage vector" (SVV), Figure 2(b), and "single state variation" (SSV), Figure 2(c), principles.…”

Section: Computational Efficiencymentioning

confidence: 99%

Novel Predictive Control for the IPMSM Fed by the 3L-SNPC Inverter for EVAs: Modified Lyapunov Function, Computational Efficiency, and Delay Compensation

Katkout

Nasser

Essadki

2020

Mathematical Problems in Engineering

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This paper proposes a novel predictive strategy based on a model predictive control (MPC) for the interior permanent magnet synchronous motors (IPMSMs) driven by a three-level simplified neutral-point clamped inverter (3L-SNPC) for electric vehicle applications (EVAs). Based on the prediction of the future behavior of the controlled variables, a predefined multiobjective cost function incorporates the control objectives which are evaluated for every sampling period to generate the optimal switching state applied directly to the inverter without the modulation stage. The control objectives in this paper are tracking current capacity, neutral-point voltage balancing, common-mode voltage control, and switching frequency reduction. The principal concepts of the novel scheme are summarized as follows: first, the delay compensation based on the long horizon of prediction is adopted by a multilevel power converter structure. Second, based on the modified Lyapunov candidate function, both stability and recursive feasibility are ensured of the proposed predictive scheme. Third, the practicability of the real-time implementation is improved by the proposed “static voltage vector” (SVV) and “single state variation” (SSV) principles. Finally, the proposed concepts are implemented in the novel predictive control formulation as additional constraints without compromising the complexity and the good performances of the predictive controller. Therefore, only the switching states that guarantee the stability and the reduction of calculation burden criteria are considered in the evaluation of cost function. The proposed predictive scheme based on the “SVV” principle has demonstrated superior performance in simulation compared with the proposed scheme with the “SSV” principle. The computational burden and switching frequency rates are reduced by 35% and 56.22%, respectively.

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Embedded Model Predictive Control With Certified Real-Time Optimization for Synchronous Motors

Cited by 68 publications

References 33 publications

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

A Review of Safe Online Learning for Nonlinear Control Systems

Novel Predictive Control for the IPMSM Fed by the 3L-SNPC Inverter for EVAs: Modified Lyapunov Function, Computational Efficiency, and Delay Compensation

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