Model Predictive Control System Design and Implementation Using MATLAB

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Model-Based Regularization for Deep Reinforcement Learning with Transcoder Networks (2018, methodology): “There are several ways environment models can be used in RL. Planning approaches use the environment model to solve a virtual RL problem and act accordingly in the real environment [25, 26, 27].”
Review on Advanced Control Technologies for Bidirectional DC/DC Converters in DC Microgrids (2020, methodology): “As a powerful technique for optimized control tracking with constraints, MPC has attracted much attention in power converter and motor drive systems [29].”
Robust finite frequency range iterative learning control design and experimental verification (2013, unknown): “For other work based on frequency decomposition of the reference signal, based on frequency sampling filters (Wang, 2009), see Bitmead and Anderson (1981) and the relevant cited references.”
Model-Based Stabilisation of Deep Reinforcement Learning (2018, background): “In general, there are four types of mode-based RL. (1) Planning approaches use the environment model to solve a virtual RL problem and act accordingly in the real environment (Wang 2009; Browne et al. 2012; Russell and Norvig 2016).”
Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints (2017, methodology): “The analysis presented here is based on [29] but adapted to suit the vehicle collision avoidance application.”