Model predictive control for time-optimal point-to-point motion control

Broeck, Lynn Van den; Diehl, Moritz; Swevers, Jan

doi:10.3182/20110828-6-it-1002.01784

Cited by 11 publications

(3 citation statements)

References 9 publications

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“…In the context of MPC, [24] proposed sampling-based MPC method for robotic manipulation, and [25], [26] further advanced the state in time-optimal MPC, but these works do not consider controlling through a sequence of switching constraints as they appear in TAMP plans.…”

Section: B Timing Optimization and Mpcmentioning

confidence: 99%

Sequence-of-Constraints MPC: Reactive Timing-Optimal Control of Sequential Manipulation

Toussaint¹,

Harris²,

Ha³

et al. 2022

Preprint

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Task and Motion Planning has made great progress in solving hard sequential manipulation problems. However, a gap between such planning formulations and control methods for reactive execution remains. In this paper we propose a model predictive control approach dedicated to robustly execute a single sequence of constraints, which corresponds to a discrete decision sequence of a TAMP plan. We decompose the overall control problem into three subproblems (solving for sequential waypoints, their timing, and a short receding horizon path) that each is a non-linear program solved online in each MPC cycle. The resulting control strategy can account for long-term interdependencies of constraints and reactively plan for a timing-optimal transition through all constraints. We additionally propose phase backtracking when running constraints are missed, leading to a fluent re-initiation behavior that is robust to perturbations and interferences by an experimenter.

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Section: B Timing Optimization and Mpcmentioning

confidence: 99%

Sequence-of-Constraints MPC: Reactive Timing-Optimal Control of Sequential Manipulation

Toussaint¹,

Harris²,

Ha³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Methods to improve the speed of MPC using online optimization methods are explained in Wang and Boyd. 30 A timeoptimal MPC for motion systems and an active vibration control device is presented in Van den Broeck et al 31 The paper Zmeu and Shipitko 32 presents the method for flexible beam control using the MPC approach with a discrete time-regression predictive model of the plant based on linear neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a model predictive controller for an active torsional vibration damper to suppress torsional vibrations in vehicle powertrains

Yüceşan

Muğan

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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The pressure of exhaust emission regulations on automotive manufacturers to reduce environmental pollution and fuel consumption of internal combustion engines (ICEs) have stimulated the works on the downsizing, downspeeding, and turbo supercharging concepts which result in boosted engine torsional vibrations. Despite significant momentum in the implementation of those concepts in modern ICEs in recent decades, similar progress has not taken place in parallel at torsional vibration isolation systems. To this end, this article centers on the development and implementation of a model predictive controller (MPC) on a novel active torsional vibration damper (ATVD) in which inertia, stiffness rate, and damping rate parameters can be varied to minimize torsional vibration transmission to the vehicle powertrain. Dynamic response of the ATVD is examined using an MPC inside a closed-loop control architecture with predicted variables. The MPC structure, state-space plant model, and physical constraint definitions are composed to be utilized in prediction models at various engine operating points. The MPC performance is evaluated in a co-simulation environment using Simcenter Amesim, NX Motion, and Matlab Simulink software, and are compared with that of the fuzzy logic controller (FLC). The simulation results clearly indicate that the MPC applied to the ATVD system has certain advantages over the FLC and is able to provide satisfactory isolation of the powertrain from engine-borne torsional vibrations while satisfying the physical constraints.

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“…This is because, in order to be time optimal, the complete task has to be considered in the optimization problem, which results in a variable time prediction horizon. This variable time horizon can either be implemented by varying the number of discrete time steps, which is done in [1], or by varying the step lengths of a fixed number of discrete time steps, or a combination thereof [2]. This work focuses on approximating the TMPC with variable time step lengths, or at least certain aspects of it, by deep neural networks (DNN) in order to decrease the calculation time.…”

Section: Introductionmentioning

confidence: 99%

Approximate time optimal control by deep neural networks trained with numerically obtained optimal trajectories

Zauner,

Gattringer,

Müller

2023

Proc Appl Math and Mech

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This paper focuses on online time optimal control of nonlinear systems. This is achieved by approximating the results of time optimal control problems (TOCP) with deep neural networks (DNN) depending on the initial and terminal system state. In general, solving a TOCP for nonlinear systems is a computationally challenging task. Especially in the context of time optimal nonlinear model predictive control (TMPC) with hard real time constraints successful termination of a TOCP within sample times suitable for controlling mechanical systems cannot be guaranteed. Therefore, our approach is to train three DNNs with different aspects of numerical solutions of TOCPs with random initial and terminal state. These networks can then be used to approximate the TMPC by a one step model predictive control scheme with a significantly simpler structure and decreased calculation time. In order to verify this procedure by simulation, it is applied to a prove of concept example as well as the model of an industrial robot.

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Model predictive control for time-optimal point-to-point motion control

Cited by 11 publications

References 9 publications

Sequence-of-Constraints MPC: Reactive Timing-Optimal Control of Sequential Manipulation

Sequence-of-Constraints MPC: Reactive Timing-Optimal Control of Sequential Manipulation

Development of a model predictive controller for an active torsional vibration damper to suppress torsional vibrations in vehicle powertrains

Approximate time optimal control by deep neural networks trained with numerically obtained optimal trajectories

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