Robust constrained model predictive control

By Arthur G. Richards. 2005

Subfields: Mathematical Optimization, Robotics & Control

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Citations in the corpus

Learning Latent Dynamics for Planning from Pixels (2018, methodology): “We use model-predictive control (MPC; Richards, 2005) to allow the agent to adapt its plan based on new observations, meaning we replan at each step.”
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning (2017, methodology): “resulting models can then be used for model-based control, which we perform using model predictive control (MPC) with a simple random-sampling shooting method [11].”
Exploring Model-based Planning with Policy Networks (2019, methodology): “In this section, we compare POPLIN with existing reinforcement learning algorithms including PETS [5], GPS [22], RS [31], MBMF [29], TD3 [9] METRPO [20], PPO [34, 16], TRPO [33] and SAC [14], which includes the most recent progress of both model-free and model-based algorithms.”
Planning from Pixels using Inverse Dynamics Models (2020, methodology): “…where α controls the relative weight of the losses when the two models share parameters: While there are many heuristic search algorithms that could be used to optimize Equation 8, we opt to use random shooting (RS) (Richards, 2005) due to ease of parallelization and strong empirical performance.”
Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search (2015, unknown): “quently used to control autonomous aerial vehicles such as quadrotors [38], [35], [34], [3], [2], [4], [29].”
Chance-Constrained Optimal Path Planning With Obstacles (2011, background): “nonconvex feasible reg ions, was handled in [9].”