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Learning in the Loop

On Neural Network-based Model Predictive Control and Cooperative System Identification

Time: Wed 2023-11-22 13.00

Location: Q2, Malvinas väg 10, Stockholm

Video link:

Language: English

Subject area: Electrical Engineering

Doctoral student: Rebecka Winqvist , Reglerteknik

Opponent: Assistant Professor Axel Ringh, Chalmers telkniska högskola, Göteborg, Sverige

Supervisor: Professor Bo Wahlberg, Optimeringslära och systemteori, Reglerteknik; Professor Cristian R. Rojas, Reglerteknik


In the context of control systems, the integration of machine learning mechanisms has emerged as a key approach for improving performance and adaptability. Notable progress has been made across several aspects of the control loop, including learning-based techniques for system identification and estimation, filtering and denoising, and controller design. This thesis delves into the rapidly expanding domain of learning in control, with a particular focus placed on learning-based controllers and learning-based identification methods.

The first part of this thesis is devoted to the investigation of Neural Network approximations of Model Predictive Control (MPC). Model-agnostic neural network structures are compared to networks employing MPC-specific information, and evaluated in terms of two performance metrics. The main novel aspect lies in the incorporation of gradient data in the training process, which is shown to enhance the accuracy of the network generated control inputs. Furthermore, experimental results reveal that MPC-informed networks outperform the agnostic counterparts in scenarios when training data is limited.

In acknowledgement of the crucial role accurate system models play in in the control loop, the second part of this thesis lends its focus to learning-based identification methods. This line of work addresses the important task of characterizing and modeling dynamical systems, by introducing cooperative system identification techniques to enhance estimation performance. Specifically, it presents a novel and generalized formulation of the Correctional Learning framework, leveraging tools from Optimal Transport. The correctional learning framework centers around a teacher-student model, where an expert agent (teacher) modifies the sampled data used by the learner agent (student), to improve the student's estimation process. By formulating correctional learning as an optimal transport problem, a more adaptable framework is achieved, better suited for estimating complex system characteristics and accommodating alternative intervention strategies.