Nonlinear Model Predictive Control: Theory and Algorithms

By Lars Grne, J. Pannek. 2011

806 citations recorded by the source corpus.

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Finite Control Set Model Predictive Current Control for Grid-Connected Voltage-Source Converters With LCL Filters: A Study Based on Different State Feedbacks (2016, methodology): “The control loop is closed by repeating the algorithm each sampling instant (the receding horizon principle) [38]”
Trajectory-Tracking Control of Mobile Robot Systems Incorporating Neural-Dynamic Optimized Model Predictive Approach (2016, background): “Remark 3: Consider suitable N, Nu, and Q, the closed-loop stability can be assured, which can be found in [32] and [33].”
Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function (2020, background): “Recursive feasibility is generally not guaranteed for MPCDC defined in (2) [18], [20] and other general NLP [21, Chap.”
Exponential sensitivity and turnpike analysis for linear quadratic optimal control of general evolution equations (2020, methodology): “We refer to [13] for an in-depth introduction to MPC and to [11] for an overview of its approximation properties.”
On the control through leadership of the Hegselmann–Krause opinion formation model (2015, methodology): “This term has also the property to stabilize the MPC scheme[18].”
Reinforcement learning for control: Performance, stability, and deep approximators (2018, background): “Postoyan et al. (2017) also highlight novel connections between MPC (Grimm et al., 2005; Gr¨une and Pannek, 2016; Borrelli et al., 2017) and RL.”