Fast Model Predictive Control Using Online Optimization

By Yang Wang, Stephen P. Boyd. IEEE Transactions on Control Systems Technology, 2010

1,596 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/e1ff249fc709c3a9a8a362d947a6ced087d9f55c

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In Control and Systems Engineering

Citations in the corpus

Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control (2018, background): “Recent progress on convex optimization [21] and applications to model predictive control [22] has led to the development of many open source solvers such as ECOS [23] and qpOASES [24] that enable model predictive control problems to be solved rapidly and reliably.”
Approximate dynamic programming via iterated Bellman inequalities (2015, neutral): “2], [58].”
Real-Time Convex Optimization in Signal Processing (2010, background): “(We believe the same trend will take place in automatic control; see, e.g. , [27], [28].)”
A Splitting Method for Optimal Control (2013, neutral): “…(x̃0, ũ0) and (z0, y0), for k = 0, 1, . . . (xk+1, uk+1) : = argmin (x,u) ( ID(x, u)+ φ(x, u)+ (ρ/2) ‖(x, u)− (x̃ k, ũk)− (zk, yk)‖22 ) (3) (x̃ k+1, ũk+1) : = argmin (x̃,ũ) ( ψ(x̃, ũ)+ (ρ/2) ‖(x̃, ũ)− (xk+1, uk+1)+ (zk, yk)‖22 ) (4) (zk+1, yk+1) := (zk, yk)+(x̃ k+1, ũk+1)−(xk+1, uk+1).”
A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP (2015, background): “As such, in this section, we address these two key practical issues by developing a fast and low-overhead FastMPC design that does not require any explicit solver capabilities in the video player [48].”
Operation and Configuration of a Storage Portfolio via Convex Optimization (2011, background): “A recent application for energy storage devices is buffering the output power flow of intermittent, renewable energy sources including wind (Zeng et al. [2006], Arulampalam et al.”
Performance bounds for linear stochastic control (2009, methodology): “This is the same example as described in [25].”
HPIPM: a high-performance quadratic programming framework for model predictive control (2020, background): “The linear mass spring system is a scalable benchmark commonly employed to evaluate linear MPC solvers (Wang and Boyd, 2010).”
ECOS: An SOCP solver for embedded systems (2013, background): “[16], [17]), all existing implementations are limited to QPs, including efficient primal-barrier methods for model predictive control [18], primal-dual interior-point methods by C code-generation for small QPs with micro to millisecond solution times (CVXGEN, [13]) and active set strategies with goo”
Min-max approximate dynamic programming (2011, background): “Much progress has been made in solving structured convex programs efficiently (see, e.g., [17], [18], [19], [20]).”
Autonomous Trajectory Planning for Rendezvous and Proximity Operations by Conic Optimization (2012, background): “Wang and Boyd [21] and Mattingley andBoyd [22] show some recentwork in pushing for realtime applications of a certain class of convex optimization to which SOCP belongs.”
Robust MPC for tracking zone regions based on nominal predictions (2012, neutral): “Since all the ingredients (functions and sets) of the optimization problem P N ( x , (cid:2) t ) are convex, then it derives that P N ( x , (cid:2) t ) is a convex mathematical programming problem that can be efﬁciently solved in polynomial time by specialized Algorithms [26,27].”