[go: up one dir, main page]

Baldauf et al., 2023 - Google Patents

Iterative learning-based model predictive control for mobile robots in space applications

Baldauf et al., 2023

Document ID
7825473850709307144
Author
Baldauf N
Turnwald A
Publication year
Publication venue
2023 27th International Conference on Methods and Models in Automation and Robotics (MMAR)

External Links

Snippet

This paper presents an iterative learning-based model predictive controller (MPC) for trajectory tracking control of an autonomous planetary rover on unknown terrain. In order to achieve accurate trajectory tracking under model uncertainties, a nonlinear controller and an …
Continue reading at ieeexplore.ieee.org (other versions)

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
    • G05B13/0265—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
    • 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
    • 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
    • G06N3/08—Learning methods
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks

Similar Documents

Publication Publication Date Title
Saviolo et al. Active learning of discrete-time dynamics for uncertainty-aware model predictive control
Wang et al. Policy learning for nonlinear model predictive control with application to USVs
Kashyap et al. Optimization of stability of humanoid robot NAO using ant colony optimization tuned MPC controller for uneven path
Ramezani et al. UAV path planning employing MPC-reinforcement learning method considering collision avoidance
Gheisarnejad et al. Supervised control strategy in trajectory tracking for a wheeled mobile robot
Faust et al. Continuous action reinforcement learning for control-affine systems with unknown dynamics
Lee et al. Gp-ilqg: Data-driven robust optimal control for uncertain nonlinear dynamical systems
Morales et al. LAMDA control approaches applied to trajectory tracking for mobile robots
CN117223011A (en) Multi-objective reinforcement learning using weighted strategy projection
Wen et al. A deep residual reinforcement learning algorithm based on Soft Actor-Critic for autonomous navigation
Sun et al. Iterative learning control based robust distributed algorithm for non-holonomic mobile robots formation
Khadiv et al. Learning locomotion skills from mpc in sensor space
Li et al. Research on the agricultural machinery path tracking method based on deep reinforcement learning
Zhou et al. Simultaneous system identification and model predictive control with no dynamic regret
Hazem et al. Reinforcement learning-based intelligent trajectory tracking for a 5-DOF Mitsubishi robotic arm: Comparative evaluation of DDPG, LC-DDPG, and TD3-ADX
Baldauf et al. Iterative learning-based model predictive control for mobile robots in space applications
Ngo et al. A novel self-organizing fuzzy cerebellar model articulation controller based overlapping Gaussian membership function for controlling robotic system
Long et al. Certifying stability of reinforcement learning policies using generalized lyapunov functions
Wang et al. Formation control of multiple nonholonomic mobile robots with limited information of a desired trajectory
Zhao et al. Efficient deep learning of robust, adaptive policies using tube mpc-guided data augmentation
Pozzi et al. Imitation learning-driven approximation of stochastic control models
Srikanthakumar et al. Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles
Qiao et al. Q-learning based on dynamical structure neural network for robot navigation in unknown environment
Yongqiang et al. Path‐Integral‐Based Reinforcement Learning Algorithm for Goal‐Directed Locomotion of Snake‐Shaped Robot
Sun et al. Unmanned aerial vehicles control study using deep deterministic policy gradient