del Río et al., 2025 - Google Patents
Adaptive Koopman Model Predictive Control of Simple Serial Robotsdel Río et al., 2025
View PDF- Document ID
- 9852591903498688538
- Author
- del Río A
- Stoeffler C
- Publication year
- Publication venue
- arXiv preprint arXiv:2503.17902
External Links
Snippet
Approximating nonlinear systems as linear ones is a common workaround to apply control tools tailored for linear systems. This motivates our present work where we developed a data- driven model predictive controller (MPC) based on the Koopman operator framework …
- 230000003044 adaptive effect 0 title abstract description 24
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/1607—Calculation of inertia, jacobian matrixes and inverses
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1628—Programme controls characterised by the control loop
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Johannsmeier et al. | A framework for robot manipulation: Skill formalism, meta learning and adaptive control | |
| Arcari et al. | Bayesian multi-task learning mpc for robotic mobile manipulation | |
| Wilson et al. | Non-linear model predictive control schemes with application on a 2 link vertical robot manipulator | |
| Mitrovic et al. | Adaptive optimal feedback control with learned internal dynamics models | |
| Kronander et al. | Incremental motion learning with locally modulated dynamical systems | |
| Farshidian et al. | Learning of closed-loop motion control | |
| Jamone et al. | Incremental learning of context-dependent dynamic internal models for robot control | |
| Kurtz et al. | Control barrier functions for singularity avoidance in passivity-based manipulator control | |
| US20250326117A1 (en) | Nonlinear adaptive control method and system for mechanical arm motion control | |
| De La Cruz et al. | Online learning of inverse dynamics via gaussian process regression | |
| Moore et al. | Adaptive control design for underactuated systems using sums-of-squares optimization | |
| Zhou et al. | Knowledge transfer between robots with similar dynamics for high-accuracy impromptu trajectory tracking | |
| Parag et al. | Value learning from trajectory optimization and sobolev descent: A step toward reinforcement learning with superlinear convergence properties | |
| Sabirov | Lyapunov method in the synthesis of intelligent adaptive systems | |
| del Río et al. | Adaptive Koopman Model Predictive Control of Simple Serial Robots | |
| Desaraju et al. | Leveraging experience for computationally efficient adaptive nonlinear model predictive control | |
| de FPA Taveira et al. | Adaptive nonlinear H∞ controllers applied to a free-floating space manipulator | |
| Prado et al. | Intelligent Swing-Up and Robust Stabilization via Tube-based Nonlinear Model Predictive Control for A Rotational Inverted-Pendulum System: Intelligent Swing-Up and Robust Stabilization via Tube-based Nonlinear Model Predictive Control for A Rotational Inverted-Pendulum System | |
| Xue et al. | Robust manipulation primitive learning via domain contraction | |
| Mitrovic et al. | Adaptive optimal control for redundantly actuated arms | |
| Zhao et al. | Efficient deep learning of robust, adaptive policies using tube mpc-guided data augmentation | |
| Junker et al. | Learning Data-Driven PCHD Models for Control Engineering Applications | |
| Junker et al. | Adaptive Data‐Driven Models in Port‐Hamiltonian Form for Control Design | |
| Hwangbo et al. | Direct state-to-action mapping for high DOF robots using ELM | |
| Pignat et al. | Generative adversarial training of product of policies for robust and adaptive movement primitives |