Combining passivity-based control and linear quadratic regulator to control a rotary inverted pendulum
In this manuscript, new combination methodology is proposed, which named combining
Passivity-Based Control and Linear Quadratic Regulator (for short, CPBC-LQR), to support …
Passivity-Based Control and Linear Quadratic Regulator (for short, CPBC-LQR), to support …
Optimal Lateral Path-Tracking Control of Vehicles With Partial Unknown Dynamics Via DPG-Based Reinforcement Learning Methods
This article focuses on the optimal lateral path-tracking control problem of vehicles with
unknown drift dynamics in a model-free manner through two novel deterministic policy …
unknown drift dynamics in a model-free manner through two novel deterministic policy …
Safe Control Framework of Multi-Agent Systems From a Performance Enhancement Perspective
In the control problems of multi-agent systems, collision avoidance is a fundamental safety
requirement. One effective approach to ensure safety involves combining control barrier …
requirement. One effective approach to ensure safety involves combining control barrier …
Learning-Based High-Precision Tracking Control: Development, Synthesis, and Verification on Spiral Scanning With a Flexure-Based Nanopositioner
The traditional methodology utilized in dynamic tracking control synthesis is usually model-
based, and therefore, the performance is highly dependent on a precise mathematical …
based, and therefore, the performance is highly dependent on a precise mathematical …
An Approach to Data-Based Linear Quadratic Optimal Control
This paper presents a data-based approach to linear quadratic optimal control design. The
system manipulated variable is assumed to have a zero mean uncertainty with a certain …
system manipulated variable is assumed to have a zero mean uncertainty with a certain …
Trajectory tracking control for Ackerman vehicle based on improved reward function
H Xie, X Ma, Q Qin, X Sun - 2024 43rd Chinese Control …, 2024 - ieeexplore.ieee.org
This article focuses on an online reinforcement learning algorithm (ORRL) for trajectory
tracking control of a class of nonlinear systems. Based on the Q-learning framework, the …
tracking control of a class of nonlinear systems. Based on the Q-learning framework, the …
Data-Driven Predictive Control Towards Multi-Agent Motion Planning With Non-Parametric Closed-Loop Behavior Learning
In many specific scenarios, accurate and effective system identification is a commonly
encountered challenge in the model predictive control (MPC) formulation. As a …
encountered challenge in the model predictive control (MPC) formulation. As a …