This paper presents a hierarchical inner-outer loop-based scheme for flight control of a small unmanned helicopter in the presence of input time-delay. The controller is designed based on a two-time-scale separation architecture which includes a fast inner loop and a slow outer loop. The inner-loop (attitude controller) employs an inverse optimal control strategy, which circumvents the tedious task of numerically solving an online Hamilton-Jacobi-Bellman (HJB) equation to obtain the optimal controller. The designed controller is optimal with respect to a meaningful objective function which considers penalties for control input, angular position and angular velocity errors. The outer loop (position) controller uses the backstepping technique to control the position and keep the helicopter on track. Finally, computer simulations are conducted to validate the theoretical results and illustrate the tracking performance of the proposed control method.

We consider a single Dubins-like mobile robot traveling with a constant longitudinal speed in a planar region supporting an unknown field distribution. A single sensor provides the distribution value at the current vehicle location. We present a new sliding mode control method for tracking environmental level sets: the controller drives the vehicle to the set where the distribution assumes a pre-specified value and ensures that the vehicle circulates along this set afterwards. The proposed control algorithm does not employ gradient estimation and is non-demanding with respect to both computation and motion. Its mathematically rigorous analysis and justification are provided. The effectiveness of the proposed guidance law is confirmed by illustrative examples and computer simulations.

In this paper, we consider a group of wheeled mobile robots, where each robot has very limited information on other robots in the group. We propose a simple bio-inspired decentralized navigation law, which guarantees that all robots will eventually move in the same direction and with the same speed.