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Found 10 papers, 1 papers with code
Identifying Reaction-Aware Driving Styles of Stochastic Model Predictive Controlled Vehicles by Inverse Reinforcement Learning
Nevertheless, an important indicator of the driving style, i. e., how an AV reacts to its nearby AVs, is not fully incorporated in the feature design of previous ME-IRL methods.
A Persistent-Excitation-Free Method for System Disturbance Estimation Using Concurrent Learning
A simulation study on epidemic control shows that the proposed method produces higher estimation precision than the conventional disturbance observer when PE is not satisfied.
Simultaneous Recursive Identification of Parameters and Switching Manifolds Identification of Discrete-Time Switched Linear Systems
Thirdly, techniques of incremental support vector machine are applied to develop the recursive algorithm to estimate the system switching manifolds, with its stability proven by a Lynapunov-based method.
Adaptive Observer for a Class of Systems with Switched Unknown Parameters Using DREM
These responses depend on the unknown states at switching instants (SASI) and constitute an additive disturbance to the parameter estimation, which obstructs parameter convergence to zero.
Data Informed Residual Reinforcement Learning for High-Dimensional Robotic Tracking Control
The learning inefficiency of reinforcement learning (RL) from scratch hinders its practical application towards continuous robotic tracking control, especially for high-dimensional robots.
Data Generation Method for Learning a Low-dimensional Safe Region in Safe Reinforcement Learning
Safe reinforcement learning aims to learn a control policy while ensuring that neither the system nor the environment gets damaged during the learning process.
Instantaneous Local Control Barrier Function: An Online Learning Approach for Collision Avoidance
This paper presents an integrated perception and control approach to accomplish safe autonomous navigation in unknown environments.
Model-Free Incremental Adaptive Dynamic Programming Based Approximate Robust Optimal Regulation
This paper presents a new formulation for model-free robust optimal regulation of continuous-time nonlinear systems.
Model Reference Adaptive Control of Piecewise Affine Systems with State Tracking Performance Guarantees
We also prove that the Lyapunov function is non-increasing even at the switching instants and thus does not impose extra dwell time constraints.
Off-Policy Risk-Sensitive Reinforcement Learning Based Constrained Robust Optimal Control
By introducing pseudo controls and risk-sensitive input and state penalty terms, the constrained robust stabilization problem of the original system is converted into an equivalent optimal control problem of an auxiliary system.
