Classical and modern methods for time-constrained energy optimal motion – Application to a badminton robot

Wang, Xin; Stoev, Julian; Pinte, Gregory; Swevers, Jan

doi:10.1016/j.mechatronics.2013.07.001

Cited by 7 publications

(17 citation statements)

References 14 publications

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“…In this section, the basic formulation of EOMPC is taken from our own previous work [5] and is repeated here for convenience. EOMPC is a MPC approach for linear timeinvariant (LTI) systems aiming at performing energy-optimal point-to-point motions within a required motion time T [s].…”

Section: Energy-optimal Mpcmentioning

confidence: 99%

“…The EOMPC optimization problem is formulated as a two-layer optimization problem [5]. The top layer is called 'Problem B' and determines the settling time.…”

Section: Energy-optimal Mpcmentioning

confidence: 99%

“…Nowadays, an important application for this type of system is energy-optimal point-to-point motion control due to the growing social awareness to achieve an environmentally sustainable future. Our previous work EOMPC [4]- [5] has been developed for this type of applications. A main drawback of EOMPC is that a mechanisms is lacking to reduce or eliminate steadystate positioning errors caused by unmodelled disturbances or plant-model mismatch.…”

Section: Introductionmentioning

confidence: 99%

“…These works only consider slow dynamic systems, sampling time of which is several seconds or even several minutes. This paper discusses the EOMPC algorithm for mechatronic systems [5] in combination with the general disturbance model strategy proposed in [9], yielding the socalled offset-free EOMPC approach for linear-time invariant (LTI) systems. Offset-free EOMPC is able to perform time-constrained energy-optimal point-to-point motions with high positioning accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Outline of the paper: Section II and III briefly discuss the offset-free MPC approach [9] and EOMPC approach [5], respectively. Section IV explains the formulation of the developed offset-free EOMPC approach.…”

Section: Introductionmentioning

confidence: 99%

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Offset-free energy-optimal model predictive control for point-to-point motions with high positioning accuracy

Wang

Swevers

2015

2015 IEEE International Conference on Mechatronics (ICM)

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This paper discusses Offset-free Energy-optimal Model Predictive Control (offset-free EOMPC) which is a MPC algorithm to realize time-constrained energy-optimal point-topoint motion control with high positioning accuracy for linear time-invariant (LTI) systems. The offset-free EOMPC approach is developed based on our previous research -Energy-optimal Model Predictive Control (EOMPC) -which aims at performing energy-optimal point-to-point motions within a given motion time. A drawback of the EOMPC method is that it cannot achieve high positioning accuracy in the presence of unmodelled disturbances or model-plant mismatch. In order to cope with this problem, a 'disturbance model' strategy is adopted: the system state is augmented with disturbance variables. Based on the 'disturbance model', the disturbances are estimated and the effect of which is cancelled. Numerical validation of the offsetfree EOMPC using a model of a linear motor with coulomb friction and cogging disturbances has been performed and the results show that time-constrained energy-optimal pointto-point motion with high positioning accuracy is achieved.

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Section: Energy-optimal Mpcmentioning

confidence: 99%

“…The EOMPC optimization problem is formulated as a two-layer optimization problem [5]. The top layer is called 'Problem B' and determines the settling time.…”

Section: Energy-optimal Mpcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Offset-free energy-optimal model predictive control for point-to-point motions with high positioning accuracy

Wang

Swevers

2015

2015 IEEE International Conference on Mechatronics (ICM)

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Constrained Online Optimization Using Evolutionary Operation: A Case Study About Energy‐Optimal Robot Control

Rutten

Baerdemaeker

Stoev

et al. 2014

Quality & Reliability Eng

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Optimization of full-scale processes during regular production is a challenge that is often encountered in practice, requiring specialized approaches that only introduce small perturbations so that production does not need to be interrupted. Based on a case study, we discuss the potential of Evolutionary Operation (EVOP) derived methods. The case study relates to a badminton robot that has to perform point-to-point motions during a fixed time interval, based on two operation modes: time-optimal motion, which ensures maximum precision but highest energy consumption, and energy-optimal motion, which decreases the energy consumption, but as a trade-off also lowers the precision. The current standard mode of operation is the energy-optimal mode that is constructed from off-line optimization on simulations. An online EVOP steepest ascent optimization to further reduce the energy consumption by fine-tuning the implemented energy-optimal mode was implemented. The constrained nature of the problem, where energy needs to be minimized subject to a time constraint, was transformed to an unconstrained single-objective optimization using Derringer desirability functions. Two important contributions were made: (i) the online optimization of the energy-optimal motion lowered the energy consumption by 4.7% while keeping the precision constant and (ii) the more stringent time-constraints implemented in desirability functions lead to an operation mode with maximum precision and 51.7% less energy consumption than the current time-optimal motion.

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A discrete-time framework for proximate time-optimal performance of damped servomechanisms

et al. 2016

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Classical and modern methods for time-constrained energy optimal motion – Application to a badminton robot

Cited by 7 publications

References 14 publications

Offset-free energy-optimal model predictive control for point-to-point motions with high positioning accuracy

Offset-free energy-optimal model predictive control for point-to-point motions with high positioning accuracy

Constrained Online Optimization Using Evolutionary Operation: A Case Study About Energy‐Optimal Robot Control

A discrete-time framework for proximate time-optimal performance of damped servomechanisms

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