One of the main challenges in autonomous robotic exploration and navigation in unknown
and unstructured environments is determining where the robot can or cannot safely move. A …

In this article, we present a data-driven learning model predictive control (MPC) scheme for
chance-constrained Markov jump systems with unknown switching probabilities. Using …

This paper considers a risk-constrained infinite-horizon optimal control problem and
proposes to solve it in an iterative manner. Each iteration of the algorithm generates a …

Distributionally robust control (DRC) aims to effectively manage distributional ambiguity in
stochastic systems. While most existing works address inaccurate distributional information …

Wasserstein distributionally robust control (WDRC) is an effective method for addressing
inaccurate distribution information about disturbances in stochastic systems. It provides …

Safe navigation is a fundamental challenge in multi-robot systems due to the uncertainty
surrounding the future trajectory of the robots that act as obstacles for each other. In this …

The successful operation of mobile robots requires them to adapt rapidly to environmental
changes. To develop an adaptive decision-making tool for mobile robots, we propose a …

Safety is one of the main challenges when applying learning-based motion controllers to
practical robotic systems, especially when the dynamics of the robots and their surrounding …

For nonlinear systems with stochastic uncertainties and input delay, the existing control
approaches based on the robust mechanism are generally conservative in most practical …

In this work, a predictor-based fixed-time sliding mode control is designed to tackle the
problem of achieving precise trajectory tracking control of multiple unmanned aerial vehicle …