The learning-based model predictive control (LB-MPC) is an effective and critical method to
solve the path tracking problem in mobile platforms under uncertain disturbances. It is well …

Researchers in the domain of unmanned aerial vehicles (UAVs) have recently shown a
great deal of interest in the quadcopter domain due to its wide variety of applications. In …

We rethink the traditional reinforcement learning approach, which is based on optimizing
over feedback policies, and propose a new framework that optimizes over feedforward …

The camera-equipped quadrotor is a preferable choice for wind turbine blade (WTB) crack
inspection applications with the benefits of high portability and low cost. Unfortunately, the …

In this paper, a novel multi-feature model predictive control (MPC) framework with real-time
and adaptive performances is proposed for intelligent structural control in which some …

In this paper, a novel two-step nonlinear ground acceleration feedback mechanism is
proposed for vibration mitigation of building structures considering soil-structure interaction …

Reinforcement learning has traditionally focused on learning state-dependent policies to
solve optimal control problems in a closed-loop fashion. In this work, we introduce the …

Remote magnetic manipulation offers wireless control over magnetic objects, which has
important medical applications, such as targeted drug delivery and minimally invasive …

We deal with the optimal inventory control problem for Multi-Stage Supply Chains (MSSC)
with uncertain dynamics. The two sources of uncertainty we consider are about the …

A hallmark of human intelligence is the ability to plan by predicting the future. Equipping
artificial agents with such capability is essential for many fields, especially when the agents …