In-Vehicle Realization of Nonlinear MPC for Gasoline Two-Stage Turbocharging Airpath Control

Albin, Thivaharan; Ritter, Daniel; Liberda, Norman; Quirynen, Rien; Diehl, Moritz

doi:10.1109/tcst.2017.2724020

Cited by 39 publications

(14 citation statements)

References 25 publications

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“…The GN method has the advantage that the Hessian approximation is guaranteed to be positive semidefinite, so the QP subproblems can be solved reliably. Because of this, the GN method is widely used in practice, see e.g., [1,7,19,20,24,52]. The GN approximation error satisfies…”

Section: The Gauss-newton (Gn) Methodsmentioning

confidence: 99%

Time-distributed optimization for real-time model predictive control: Stability, robustness, and constraint satisfaction

2020

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Time distributed optimization is an implementation strategy that can significantly reduce the computational burden of model predictive control by exploiting its robustness to incomplete optimization. When using this strategy, optimization iterations are distributed over time by maintaining a running solution estimate for the optimal control problem and updating it at each sampling instant. The resulting controller can be viewed as a dynamic compensator which is placed in closedloop with the plant. This paper presents a general systems theoretic analysis framework for time distributed optimization. The coupled plant-optimizer system is analyzed using input-to-state stability concepts and sufficient conditions for stability and constraint satisfaction are derived. When applied to time distributed sequential quadratic programming, the framework significantly extends the existing theoretical analysis for the real-time iteration scheme. Numerical simulations are presented that demonstrate the effectiveness of the scheme.

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Section: The Gauss-newton (Gn) Methodsmentioning

confidence: 99%

Time-distributed optimization for real-time model predictive control: Stability, robustness, and constraint satisfaction

2020

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“…Ref. [17] describes the use of a physical model for two stage turbocharging. In this work the the physical model is used within a NMPC based on multiple-shooting.…”

Section: Two Stage Turbochargingmentioning

confidence: 99%

Benefits of model predictive control for gasoline airpath control

Albin¹

2018

Sci. China Inf. Sci.

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One effective possibility to reduce pollutant emissions and fuel consumption of an internal combustion engine is the use of improved process control. It is made viable by the implementation of additional actuators and sensors which allow to operate the process more flexible. For full exploitation of the setup an appropriate control algorithm is necessary. Classical engine control structures rely on the use of many calibration parameters which result in high demands on the calibration time. Model predictive control (MPC) is an advanced control algorithm which is able to overcome this drawback. It allows to use a mathematical plant model for control synthesis which reduces calibration time and makes reusability possible. The present paper introduces the MPC algorithm and discusses the benefits of MPC for engine control. A special emphasis is put on the application for gasoline airpath control. For clarification, the benefits are demonstrated by numerical simulation studies. For the example of gasoline two stage turbocharging, the advantage concerning control performance are shown as well as the possibility to easily adapt to changed specifications for the closed-loop control dynamics.

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“…Then numerical integration techniques (e.g. the Euler method) are applied to satisfy the differential constraints [10]. Another well-known methodology is the collocation method (e.g.…”

Section: Introductionmentioning

confidence: 99%

Solving Multiobjective Constrained Trajectory Optimization Problem by an Extended Evolutionary Algorithm

Chai

Tsourdos

Xia

et al. 2020

IEEE Trans. Cybern.

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Highly constrained trajectory optimization problems are usually difficult to solve. Due to some real-world requirements, a typical trajectory optimization model may need to be formulated containing several objectives. Because of the discontinuity or nonlinearity in the vehicle dynamics and mission objectives, it is challenging to generate a compromised trajectory that can satisfy constraints and optimize objectives. To address the multi-objective trajectory planning problem, this study applies a specific multiple-shooting discretization technique with the newest NSGA-III optimization algorithm and constructs a new evolutionary optimal control solver. In addition, three constraint handling algorithms are incorporated in this evolutionary optimal control framework. The performance of using different constraint handling strategies is detailed and analyzed. The proposed approach is compared with other well-developed multiobjective techniques. Experimental studies demonstrate that the present method can outperform other evolutionary-based solvers investigated in this paper with respect to convergence ability and distribution of the Pareto-optimal solutions. Therefore, the present evolutionary optimal control solver is more attractive and can offer an alternative for optimizing multi-objective continuous-time trajectory optimization problems.

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In-Vehicle Realization of Nonlinear MPC for Gasoline Two-Stage Turbocharging Airpath Control

Cited by 39 publications

References 25 publications

Time-distributed optimization for real-time model predictive control: Stability, robustness, and constraint satisfaction

Time-distributed optimization for real-time model predictive control: Stability, robustness, and constraint satisfaction

Benefits of model predictive control for gasoline airpath control

Solving Multiobjective Constrained Trajectory Optimization Problem by an Extended Evolutionary Algorithm

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