Machine Learning Based Adaptive Prediction Horizon in Finite Control Set Model Predictive Control

Gardezi, Muhammad Saleh Murtaza; Hasan, Ammar

doi:10.1109/access.2018.2839519

Cited by 30 publications

(14 citation statements)

References 21 publications

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“…For example, robotics, process control, and heating, ventilation, and air conditioning used (i) learning dynamic modeling for MPC by adjusting the model structure of MPC [192,[200][201][202][214][215][216], (ii) the controller design of MPC [193,[217][218][219][220], (iii) optimization of MPC solvers [196], (iv) imitation of MPC [195,195,221], and (v) MPC-based safe-learning of ML [194,222,223]. These methods seem promising for future implementation in ICE applications but must be comprehensively assessed.…”

Section: Integration Of Ai and Mpcmentioning

confidence: 99%

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

et al. 2021

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An internal combustion engine (ICE) is a highly nonlinear dynamic and complex engineering system whose operation is constrained by operational limits, including emissions, noise, peak in-cylinder pressure, combustion stability, and actuator constraints. To optimize today’s ICEs, seven to ten control actuators and 10–20 feedback sensors are often used, depending on the engine applications and target emission regulations. This requires extensive engine experimentation to calibrate the engine control module (ECM), which is both cumbersome and costly. Despite these efforts, optimal operation, particularly during engine transients and to meet real driving emission (RDE) targets for broad engine speed and load conditions, has still not been obtained. Methods of model predictive control (MPC) have shown promising results for real-time multi-objective optimal control of constrained multi-variable nonlinear systems, including ICEs. This paper reviews the application of MPC for ICEs and analyzes the recent developments in MPC that can be utilized in ECMs. ICE control and calibration can be enhanced by taking advantage of the recent developments in the field of Artificial Intelligence (AI) in applying Machine Learning (ML) to large-scale engine data. Recent developments in the field of ML-MPC are investigated, and promising methods for ICE control applications are identified in this paper.

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Section: Integration Of Ai and Mpcmentioning

confidence: 99%

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

et al. 2021

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“…Generally and as the above work, the prediction horizon of FCS-MPC is kept constant and is chosen by the trade-off between the performance and the computation burden. Authors of [21] present an adaptive prediction horizon for the FCS-MPC of power converters in which a NN is trained to optimize the optimal prediction horizon of the FCS-MPC. In [22], an artificial neural network-based approach is applied to optimize the weighting factors of the MPC controller.…”

Section: Literature Reviewmentioning

confidence: 99%

Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters

et al. 2021

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An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC.

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“…There exists several suggestions in the literature on how to design these criteria, e.g. terminal conditions [9,10], as decision variables of the OCP [11], and learning approaches [12,13]. Other parameters of the MPC scheme are also subject to tuning, e.g.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning

Bøhn¹,

Gros²,

Moe³

et al. 2021

Preprint

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Model predictive control (MPC) is increasingly being considered for control of fast systems and embedded applications. However, the MPC has some significant challenges for such systems. Its high computational complexity results in high power consumption from the control algorithm, which could account for a significant share of the energy resources in battery-powered embedded systems. The MPC parameters must be tuned, which is largely a trial-and-error process that affects the control performance, the robustness and the computational complexity of the controller to a high degree. In this paper we propose a novel framework in which any parameter of the control algorithm can be jointly tuned using reinforcement learning (RL), with the goal of simultaneously optimizing the control performance and the power usage of the control algorithm. We propose the novel idea of optimizing the meta-parameters of MPC with RL, i.e. parameters affecting the structure of the MPC problem as opposed to the solution to a given problem. Our control algorithm is based on an event-triggered MPC where we learn when the MPC should be re-computed, and a dual mode MPC and linear state feedback control law applied in between MPC computations. We formulate a novel mixturedistribution policy, and show that with joint optimization we achieve improvements that do not present themselves when optimizing the same parameters in isolation. We demonstrate our framework on the inverted pendulum control task, reducing the total computation time of the control system by 36% while also improving the control performance by 18.4% over the bestperforming MPC baseline.

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Machine Learning Based Adaptive Prediction Horizon in Finite Control Set Model Predictive Control

Cited by 30 publications

References 21 publications

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters

Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning

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