Continuous Control Set Nonlinear Model Predictive Control of Reluctance Synchronous Machines

Zanelli, Andrea; Kullick, Julian; Eldeeb, Hisham; Frison, Gianluca; Hackl, Christoph M.; Diehl, Moritz

doi:10.1109/tcst.2020.3043956

Cited by 38 publications

(26 citation statements)

References 51 publications

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“…In these papers, an upper bound of the computation time in the worst-case scenario has been demonstrated. Moreover, a nonlinear CS-MPC was presented for the Synchronous Reluctance Motor (SyRM) in [20]. The work proved the flexibility of the MPC framework in tackling the nonlinear flux-current characteristics of such motors.…”

Section: Introductionmentioning

confidence: 93%

Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives

Favato¹,

Carlet²,

Toso³

et al. 2021

Preprint

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<div>This paper proposes a fast and accurate solver for implicit Continuous Set Model Predictive Control for the current control loop of synchronous motor drives with input constraints, allowing for reaching the maximum voltage feasible set. The related control problem requires an iterative solver to find the optimal solution. The real-time certification of the algorithm is of paramount importance to move the technology toward industrial-scale applications.</div><div>A relevant feature of the proposed solver is that the total number of operations can be computed in the worst-case scenario. Thus, the maximum computational time is known a priori. The solver is deeply illustrated, showing its feasibility for real-time applications in the microseconds range by means of experimental tests.</div><div>The proposed method outperforms general-purpose algorithms in terms of computation time, while keeping the same accuracy.</div>

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Section: Introductionmentioning

confidence: 93%

Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives

Favato¹,

Carlet²,

Toso³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The problem structure (1) arises in various contexts and applications, e.g., nonlinear least-squares formulations for estimation and tracking [5,24,34], nonlinear matrix inequalities for reduced order controller design [12,38], nonlinear model predictive control with ellipsoidal feasible sets [7,18,41,44], robustified inequalities in nonlinear optimization [9,29], tube-following optimal control problems [39], non-smooth composite minimization [2,23], as well as training of deep neural networks [25-27, 33, 42].…”

Section: Introductionmentioning

confidence: 99%

Survey of sequential convex programming and generalized Gauss-Newton methods

2021

Self Cite

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We provide an overview of a class of iterative convex approximation methods for nonlinear optimization problems with convex-over-nonlinear substructure. These problems are characterized by outer convexities on the one hand, and nonlinear, generally nonconvex, but differentiable functions on the other hand. All methods from this class use only first order derivatives of the nonlinear functions and sequentially solve convex optimization problems. All of them are different generalizations of the classical Gauss-Newton (GN) method. We focus on the smooth constrained case and on three methods to address it: Sequential Convex Programming (SCP), Sequential Convex Quadratic Programming (SCQP), and Sequential Quadratically Constrained Quadratic Programming (SQCQP). While the first two methods were previously known, the last is newly proposed and investigated in this paper. We show under mild assumptions that SCP, SCQP and SQCQP have exactly the same local linear convergence – or divergence – rate. We then discuss the special case in which the solution is fully determined by the active constraints, and show that for this case the KKT conditions are sufficient for local optimality and that SCP, SCQP and SQCQP even converge quadratically. In the context of parameter estimation with symmetric convex loss functions, the possible divergence of the methods can in fact be an advantage that helps them to avoid some undesirable local minima: generalizing existing results, we show that the presented methods converge to a local minimum if and only if this local minimum is stable against a mirroring operation applied to the measurement data of the estimation problem. All results are illustrated by numerical experiments on a tutorial example.

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“…An example of continuous-set (CS) predictive control applied to SynRMs is given in [27], with much less current ripple than a finite-set predictive controller.…”

Section: Introductionmentioning

confidence: 99%

“…When nonlinear constraints are included, finding the optimal voltage vector to apply in the next control step is problematic, since it involves finding the minimum multivariable function, and the computational effort can be very high [27]. The applicability of the continuous-set predictive control algorithm on low-cost applications, such as electric pumps, is therefore undermined by the need for expensive microprocessors.…”

Section: Introductionmentioning

confidence: 99%

Model-Free Predictive Current Control of Synchronous Reluctance Motor Drives for Pump Applications

et al. 2021

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Climate changes and the lack of running water across vast territories require the massive use of pumping systems, often powered by solar energy sources. In this context, simple drives with high-efficiency motors can be expected to take hold. It is important to emphasise that simplicity does not necessarily lie in the control algorithm itself, but in the absence of complex manual calibration. These characteristics are met by synchronous reluctance motors provided that the calibration of the current loops is made autonomous. The goal of the present research was the development of a current control algorithm for reluctance synchronous motors that does not require an explicit model of the motor, and that self-calibrates in the first moments of operation without the supervision of a human expert. The results, both simulated and experimental, confirm this ability. The proposed algorithm adapts itself to different motor types, without the need for any initial calibration. The proposed technique is fully within the paradigm of smarter electrical drives, which, similarly to today’s smartphones, offer advanced performance by making any technological complexity transparent to the user.

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Continuous Control Set Nonlinear Model Predictive Control of Reluctance Synchronous Machines

Cited by 38 publications

References 51 publications

Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives

Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives

Survey of sequential convex programming and generalized Gauss-Newton methods

Model-Free Predictive Current Control of Synchronous Reluctance Motor Drives for Pump Applications

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