This article reviews the state-of-the-art modeling and control techniques for aerial robots
such as quadrotor systems and presents several future research directions in this area. The …

This paper proposes an automatic controller tuning framework based on linear optimal
control combined with Bayesian optimization. With this framework, an initial set of controller …

In practice, the parameters of control policies are often tuned manually. This is time-
consuming and frustrating. Reinforcement learning is a promising alternative that aims to …

Model-based reinforcement learning (MBRL) has been shown to be a powerful framework
for data-efficiently learning control of continuous tasks. Recent work in MBRL has mostly …

Real-world robots are becoming increasingly complex and commonly act in poorly
understood environments where it is extremely challenging to model or learn their true …

The performance of robots in high-level tasks depends on the quality of their lower level
controller, which requires fine-tuning. However, the intrinsically nonlinear dynamics and …

Due to noisy actuation and external disturbances, tuning controllers for high-speed flight is
very challenging. In this letter, we ask the following questions: How sensitive are controllers …

A control problem of an inverted pendulum in the presence of parametric uncertainty has
been investigated in this paper. In particular, synthesis and implementation of an automatic …

Optimal control (OCl) can be traced back to the 1960s when it was utilised for solving an
optimisation problem (OP). In the OCl technique, a stable controller can be obtained by …

This paper presents a method for the automatic selection of weighting matrices for a linear-
quadratic regulator (LQR) in order to design an optimal active structural control system. The …