Computationally Efficient Predictive Robot Control

Duchaine, Vincent; Bouchard, Samuel; Gosselin, Clément

doi:10.1109/tmech.2007.905722

Cited by 62 publications

(41 citation statements)

References 20 publications

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“…Finally, the concept of preview as next state prediction is available in the domain of systems and control. For instance, Model Predictive Control (MPC) [21] relies on system models to predict possible future behaviors of the system, from which the optimal input control signals are selected.…”

Section: Related Workmentioning

confidence: 99%

Preview coordination: An enhanced execution model for online scheduling of mobile manipulation tasks

Scioni

Klotzbuecher

Laet

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Task specification models define the activities to be executed by a robot in order to achieve its goal. Classical examples are the sequences involved in assembly or pick and place tasks. This work introduces the preview coordination execution model, an extension to the traditional way in which the execution of such task specifications is coordinated at runtime. Instead of taking activities one-by-one as defined in the task specification model, preview coordination optimizes the task scheduling based on knowledge about the likelihood that not just the activities required by the current state can be executed, but that also one or more of those related to future states of the system can be activated. An experiment with mobile manipulation tasks illustrates the benefits of preview coordination.

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Section: Related Workmentioning

confidence: 99%

Preview coordination: An enhanced execution model for online scheduling of mobile manipulation tasks

Scioni

Klotzbuecher

Laet

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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show abstract

“…Hence, the online optimization typically can lead to an increased computation time with respect to "classical" control laws. For this reason, MPC has been efficiently used in several industrial processes, such as chemical plants or oil refineries [7], but its application to robotic systems in a true industrial environment, in which unavoidable modelling uncertainties and external disturbances affect the system, is still limited [8]- [11].…”

Section: Introductionmentioning

confidence: 99%

MPC for Robot Manipulators With Integral Sliding Modes Generation

Incremona

Ferrara

Magni

2017

IEEE/ASME Trans. Mechatron.

140

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Abstract-This paper deals with the design of a robust hierarchical multi-loop control scheme to solve motion control problems for robot manipulators. The key elements of the proposed control approach are the inverse dynamics-based feedback linearized robotic MIMO system and the combination of a Model Predictive Control (MPC) module with an Integral Sliding Mode (ISM) controller. The ISM internal control loop has the role to compensate the matched uncertainties due to unmodelled dynamics, which are not rejected by the inverse dynamics approach. The external loop is closed relying on the MPC, which guarantees an optimal evolution of the controlled system while fulfilling state and input constraints. The motivation for using ISM, apart from its property of providing robustness to the scheme with respect to a wide class of uncertainties, is also given by its capability of enforcing sliding modes of the controlled system since the initial time instant, allowing one to solve the model predictive control optimization problem relying on a set of linearized decoupled SISO systems which are not affected by uncertain terms. The proposal has been verified and validated in simulation, relying on a model of a COMAU Smart3-S2 industrial robot manipulator, identified on the basis of real data.Index Terms-Model predictive control, integral sliding mode, robot manipulators, uncertain systems.

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“…Generally, traditional proportional-differential (PD) and augmented PD controllers are the preferred control schemes in actual parallel manipulator [10] . However, they cannot always ensure the better controller performance by virtue of strong coupling and high nonlinearity of such a system [11] . On the other hand, some advanced control algorithms for parallel manipulator have been studied by many researchers.…”

Section: Introductionmentioning

confidence: 99%

Dynamics and Control of a Novel 3 -DOF Parallel Manipulator with Actuation Redundancy

Xuemei

Gao

Liu

et al. 2013

Int. J. Autom. Comput.

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Abstract:This paper deals with the dynamics and control of a novel 3-degrees-of-freedom (DOF) parallel manipulator with actuation redundancy. According to the kinematics of the redundant manipulator, the inverse dynamic equation is formulated in the task space by using the Lagrangian formalism, and the driving force is optimized by utilizing the minimal 2-norm method. Based on the dynamic model, a synchronized sliding mode control scheme based on contour error is proposed to implement accurate motion tracking control. Additionally, an adaptive method is introduced to approximate the lumped uncertainty of the system and provide a chattering-free control. The simulation results indicate the effectiveness of the proposed approaches and demonstrate the satisfactory tracking performance compared to the conventional controller in the presence of the parameter uncertainties and un-modelled dynamics for the motion control of manipulators.

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Computationally Efficient Predictive Robot Control

Cited by 62 publications

References 20 publications

Preview coordination: An enhanced execution model for online scheduling of mobile manipulation tasks

Preview coordination: An enhanced execution model for online scheduling of mobile manipulation tasks

MPC for Robot Manipulators With Integral Sliding Modes Generation

Dynamics and Control of a Novel 3 -DOF Parallel Manipulator with Actuation Redundancy

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