Nonhoff et al., 2019 - Google Patents
Economic model predictive control for snake robot locomotionNonhoff et al., 2019
View PDF- Document ID
- 13279293135048948380
- Author
- Nonhoff M
- Köhler P
- Kohl A
- Pettersen K
- Allgöwer F
- Publication year
- Publication venue
- 2019 IEEE 58th Conference on Decision and Control (CDC)
External Links
Snippet
In this work, the control of snake robot locomotion via economic model predictive control (MPC) is studied. Only very few examples of applications of MPC to snake robots exist and rigorous proofs for recursive feasibility and convergence are missing. We propose an …
- 241000270295 Serpentes 0 title abstract description 50
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
-
- 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
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
- G05D1/0291—Fleet control
- G05D1/0295—Fleet control by at least one leading vehicle of the fleet
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Nonhoff et al. | Economic model predictive control for snake robot locomotion | |
| Carron et al. | Data-driven model predictive control for trajectory tracking with a robotic arm | |
| Zhang et al. | Adaptive near-optimal control of uncertain systems with application to underactuated surface vessels | |
| CN109343350A (en) | A Path Tracking Control Method for Underwater Robots Based on Model Predictive Control | |
| Du et al. | Model predictive formation tracking-containment control for multi-UAVs with obstacle avoidance | |
| Liu et al. | Robust adaptive self-organizing neuro-fuzzy tracking control of UUV with system uncertainties and unknown dead-zone nonlinearity | |
| Chen et al. | A Practical Iterative Learning Path‐Following Control of An Omni‐Directional Vehicle | |
| Peng et al. | Model-free antidisturbance autopilot design for autonomous surface vehicles with hardware-in-the-loop experiments | |
| Bang et al. | Rl-augmented mpc framework for agile and robust bipedal footstep locomotion planning and control | |
| Jin et al. | Collision avoidance for multiple quadrotors using elastic safety clearance based model predictive control | |
| Kumar et al. | Barrier lyapunov function based trajectory tracking controller for autonomous vehicles with guaranteed safety bounds | |
| Wang et al. | Adaptive neural network control of a wheeled mobile robot violating the pure nonholonomic constraint | |
| Bahadorian et al. | Robust time-varying model predictive control with application to mobile robot unmanned path tracking | |
| Liu et al. | Swarm-Based Dynamic Coverage of Multi-ASV Systems in the Presence of Measurement Noises | |
| Mousavifard et al. | Formation control of multi-quadrotors based on deep Q-learning | |
| Marais et al. | Go with the flow: energy minimising periodic trajectories for uvms | |
| Rosolia et al. | Learning model predictive control for iterative tasks | |
| Zamani et al. | Continuous-time nonlinear robust MPC for offset-free tracking of piece-wise constant setpoints with unknown disturbance | |
| Rezapour et al. | Body shape and orientation control for locomotion of biologically-inspired snake robots | |
| Blažič | Two approaches for nonlinear control of wheeled mobile robots | |
| Zhang et al. | AQ‐Learning‐Based Parameters Adaptive Algorithm for Formation Tracking Control of Multi‐Mobile Robot Systems | |
| Pei et al. | Nonlinear model predictive tracking control of nonholonomic wheeled mobile robot using modified C/GMRES algorithm | |
| Leu et al. | Safe and coordinated hierarchical receding horizon control for mobile manipulators | |
| Liang et al. | Simultaneous gait generation and path following control of snake robot using MPC | |
| Arévalo et al. | Sliding mode formation control of mobile robots with input delays |