scholarly journals Reducing the computational effort of min-max model predictive control with regional feedback laws

2021 ◽  
Vol 54 (6) ◽  
pp. 58-63
Author(s):  
Kai König ◽  
Martin Mönnigmann
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Computational Effort ◽  
Feedback Laws

Probabilistic model predictive control for extended prediction horizons

2021 ◽  
Vol 69 (9) ◽  
pp. 759-770
Author(s):  
Tim Brüdigam ◽  
Johannes Teutsch ◽  
Dirk Wollherr ◽  
Marion Leibold ◽  
Martin Buss
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Prediction Models ◽  
Computational Effort ◽  
Probabilistic Constraints ◽  
Detailed Model ◽  
Control Approach ◽  
Proposed Model ◽  
Sampling Times

Abstract Detailed prediction models with robust constraints and small sampling times in Model Predictive Control yield conservative behavior and large computational effort, especially for longer prediction horizons. Here, we extend and combine previous Model Predictive Control methods that account for prediction uncertainty and reduce computational complexity. The proposed method uses robust constraints on a detailed model for short-term predictions, while probabilistic constraints are employed on a simplified model with increased sampling time for long-term predictions. The underlying methods are introduced before presenting the proposed Model Predictive Control approach. The advantages of the proposed method are shown in a mobile robot simulation example.

scholarly journals Robust Two-Layer Model Predictive Control for Full-Bridge NPC Inverter-Based Class-D Voltage Mode Amplifier

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1346 ◽  
Author(s):  
Xinwei Wei ◽  
Hongliang Wang ◽  
Kangliang Wang ◽  
Kui Li ◽  
Minying Li ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Dynamic Performance ◽  
Tracking Error ◽  
Computational Effort ◽  
Multiple Control ◽  
Lumped Model ◽  
Voltage Mode ◽  
Class D ◽  
Lc Filter

Finite control set model predictive control (FCS-MPC) is able to handle multiple control objectives and constraints simultaneously with good dynamic performance. However, its industrial application is limited by its high dependence on system model and the huge computational effort. In this paper, a novel robust two-layer MPC (RM-MPC) with strong robustness is proposed for the full-bridge neutral-point clamped (NPC) voltage mode Class-D amplifier (CDA) aiming at this problem. The errors caused by the parameter mismatches or uncertainties of the LC filter and the load current are regarded as lumped disturbance and estimated by the designed Luenberger observer. The robust control can be achieved by compensating the estimated disturbance to the used predictive model. In order to reduce computation of the controller, a two-layer MPC is proposed for the full-bridge NPC inverter with an LC filter. The first layer is used to calculate the optimal output level which minimizes the tracking error of the output voltage. The second layer is used to determine the switching state for the purpose of capacitor voltage balancing. The experimental results show that the lumped model error is observed centrally through only one observer with low complexity. The two-layer MPC further reduced the computation without affecting the dynamic performance.

Physics-based linear model predictive control strategy for three-way catalyst air/fuel ratio control

2021 ◽  
pp. 095440702110212
Author(s):  
Abdullah-al Mamun ◽  
Qilun Zhu ◽  
Mark Hoffman ◽  
Simona Onori
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Computational Effort ◽  
Oxygen Storage ◽  
Pid Controllers ◽  
Storage Model ◽  
Fuel Ratio ◽  
Vehicle Test ◽  
Three Way Catalyst

The Current practice of air-fuel ratio control relies on empirical models and traditional PID controllers, which require extensive calibration to maintain the post-catalyst air-fuel ratio close to stoichiometry. In contrast, this work utilizes a physics-based Three-Way Catalyst (TWC) model to develop a model predictive control (MPC) strategy for air-fuel ratio control based on internal TWC oxygen storage dynamics. In this paper, parameters of the physics-based temperature and oxygen storage models of the TWC are identified using vehicle test data for a catalyst aged to 150,000 miles. A linearized oxygen storage model is then developed from the identified nonlinear model, which is shown via simulation to follow the nonlinear model with minimal error during nominal operation. This motivates the development of a Linear MPC (LMPC) framework using the linearized TWC oxygen storage model, reducing the requisite computational effort relative to a nonlinear MPC strategy. In this work, the LMPC utilizing a linearized physics-based TWC model is proven suitable for tracking a desired oxygen storage level by controlling the commanded engine air-fuel ratio, which is also a novel contribution. The offline simulation results show successful tracking performance of the developed LMPC framework.

scholarly journals Performance Improvements of a Permanent Magnet Synchronous Machine via Functional Model Predictive Control

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Ahmed M. Kassem ◽  
A. A. Hassan
Keyword(s):  
Model Predictive Control ◽  
Permanent Magnet ◽  
Predictive Control ◽  
Permanent Magnet Synchronous Motor ◽  
Functional Model ◽  
Computational Effort ◽  
Permanent Magnet Synchronous Machine ◽  
Performance Improvements ◽  
Prediction Horizon ◽  
Functional Model Predictive Control

This paper investigates the application of the model predictive control (MPC) approach to control the speed of a permanent magnet synchronous motor (PMSM) drive system. The MPC is used to calculate the optimal control actions including system constraints. To alleviate computational effort and to reduce numerical problems, particularly in large prediction horizon, an exponentially weighted functional model predictive control (FMPC) is employed. In order to validate the effectiveness of the proposed FMPC scheme, the performance of the proposed controller is compared with a classical PI controller through simulation studies. Obtained results show that accurate tracking performance of the PMSM has been achieved.

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