Fast model predictive control based on sensitivity analysis strategy

Kalantari, Hossein; Mojiri, M.; Dubljević, Stevan; Zamani, Najmeh

doi:10.1049/iet-cta.2019.0556

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…In the previous work [21], we have introduced a new algorithm based on the hot‐starting strategy to solve

l_1

MPC by sensitivity analysis and now we want to extend this strategy for QMPC. For this purpose, the Wolfe method would be used along with the sensitivity analysis.…”

Section: Introductionmentioning

confidence: 99%

Fast quadratic model predictive control based on sensitivity analysis and Wolfe method

Kalantari,

Mojiri,

Askari

et al. 2024

IET Control Theory & Appl

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This paper proposes a new algorithm based on sensitivity analysis and the Wolfe method to solve a sequence of parametric quadratic programming (QP) problems such as those that arise in quadratic model predictive control (QMPC). The Wolfe method, based on Karush–Kuhn–Tucker conditions, has been used to convert parametric QP problems to parametric linear programming (LP) problems, and then the sensitivity analysis is applied to solve the sequence of the parametric LP problems. This strategy obtains sensitivity analysis‐based QMPC (SA‐QMPC) algorithm. It is proved that the computational complexity of SA‐QMPC is for a region of the initial conditions and for sufficiently small sampling time and all initial conditions, where and are the horizon time and dimension of the state vector, respectively. Numerical results indicate the potential and properties of the proposed algorithm.

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“…In the previous work [21], we have introduced a new algorithm based on the hot‐starting strategy to solve

l_1

MPC by sensitivity analysis and now we want to extend this strategy for QMPC. For this purpose, the Wolfe method would be used along with the sensitivity analysis.…”

Section: Introductionmentioning

confidence: 99%

Fast quadratic model predictive control based on sensitivity analysis and Wolfe method

Kalantari,

Mojiri,

Askari

et al. 2024

IET Control Theory & Appl

Self Cite

View full text Add to dashboard Cite

show abstract

Fast Adaptation of Manipulator Trajectories to Task Perturbation by Differentiating through the Optimal Solution

Srikanth

Babu

Masnavi

et al. 2022

Sensors

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Joint space trajectory optimization under end-effector task constraints leads to a challenging non-convex problem. Thus, a real-time adaptation of prior computed trajectories to perturbation in task constraints often becomes intractable. Existing works use the so-called warm-starting of trajectory optimization to improve computational performance. We present a fundamentally different approach that relies on deriving analytical gradients of the optimal solution with respect to the task constraint parameters. This gradient map characterizes the direction in which the prior computed joint trajectories need to be deformed to comply with the new task constraints. Subsequently, we develop an iterative line-search algorithm for computing the scale of deformation. Our algorithm provides near real-time adaptation of joint trajectories for a diverse class of task perturbations, such as (i) changes in initial and final joint configurations of end-effector orientation-constrained trajectories and (ii) changes in end-effector goal or way-points under end-effector orientation constraints. We relate each of these examples to real-world applications ranging from learning from demonstration to obstacle avoidance. We also show that our algorithm produces trajectories with quality similar to what one would obtain by solving the trajectory optimization from scratch with warm-start initialization. Most importantly, however, our algorithm achieves a worst-case speed-up of 160x over the latter approach.

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Fast model predictive control based on sensitivity analysis strategy

Cited by 2 publications

References 25 publications

Fast quadratic model predictive control based on sensitivity analysis and Wolfe method

Fast quadratic model predictive control based on sensitivity analysis and Wolfe method

Fast Adaptation of Manipulator Trajectories to Task Perturbation by Differentiating through the Optimal Solution

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