Predictive control : with constraints

By Jan M. Maciejowski. 2002

5,756 citations recorded by the source corpus.

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In Robotics & Control

Citations in the corpus

From Pixels to Torques: Policy Learning with Deep Dynamical Models (2015, background): “The textbooks by Maciejowski (2002) and Rawlings & Mayne (2009) also provide good introductions to MPC.”
Speed Control for PMSM Servo System Using Predictive Functional Control and Extended State Observer (2012, background): “Model predictive control (MPC) is one of the most practical advanced control technique in industrial applications [16].”
Closed-loop and activity-guided optogenetic control. (2015, positive): “…model in situ, suggest that fast model predictive control (MPC) with constraints (Wang and Boyd, 2010; Bemporad, 2006; Qin and Badgwell, 2003; Maciejowski, 2002; Camacho and Bordons, 2004) may be important to achieve control at the speeds allowed by optogenetic tools while utilizing the…”
Finite constraint set receding horizon quadratic control (2004, methodology): “On the other hand, model predictive control schemes constitute a useful tool for controlling constrained systems [18, 19].”
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning (2022, methodology): “Planning Following (Lee et al., 2020; Seo et al., 2020), we use the model predictive model (MPC) (Maciejowski, 2002) to select actions based on learned dynamics prediction model, and assume that the reward functions of environments are known.”
Predictive Cruise Control: Utilizing Upcoming Traffic Signal Information for Improving Fuel Economy and Reducing Trip Time (2011, methodology): “According to the standard MPC design, only the first entry of the control sequence , is applied to the vehicle; the optimization horizon is moved one step forward, the model and constraints are updated if necessary, and the optimization process is repeated to obtain the next optimal control sequence”
Multi-Parametric Toolbox (MPT) (2004, background): “For a good overview of receding horizon control we refer the reader to [24, 22].”
Model learning for robot control: a survey (2011, methodology): “Further application of forward models can be found in the wide class of model predictive control (MPC) (Maciejowski 2002).”
Power and performance management of virtualized computing environments via lookahead control (2008, methodology): “The LLC method is conceptually similar to model predictive control (MPC) [23], with some key differences.”
“Predictive control in power electronics and drives” (2008, background): “Note that, generally, the selection of the terminal state constraint is related to the stability issues [38], [39].”
Real-Time Convex Optimization in Signal Processing (2010, methodology): “We will use a standard technique from control, called model predictive control [29], in which at time t we solve an optimization problem to ‘plan’ our input signal over the next T steps, and use only the ﬁrst sample of our plan as the actual equalizer output.”
Model Predictive Control of an Inverter With Output $LC$ Filter for UPS Applications (2009, methodology): “Another approach is the model predictive control (MPC), also known as receding-horizon control; it uses a model of the system to predict the behavior of the variables until a certain horizon of time, and a cost function is used as criterion to select the optimal future actions [28]–[31].”