Improvement of plant performance using Closed loop Reference Model Simple Adaptive Control for Micro Air Vehicle

In this paper, we present a novel idea to improve the transient performance of the existing Simple Adaptive Control architecture, without requiring high adaptation gains. Improvement in performance is achieved by incorporating the closed loop reference model based on the output feedback to the Simple Adaptive Control architecture. In this proposed scheme, the reference model dynamics is driven by the desired command as well as the error signal between the plant output and the reference model output. It is shown that the modified control architecture improves the system performance without any additional control efforts, which is then validated through simulations of the lateral model dynamics of Micro Air Vehicle.

Integrated guidance and control framework for the waypoint navigation of a miniature aircraft with highly coupled longitudinal and lateral dynamics

A solution to the waypoint navigation problem for fixed wing micro air vehicles (MAV) is addressed in this paper, in the framework of integrated guidance and control (IGC). IGC yields a single step solution to the waypoint navigation problem, unlike conventional multiple loop design. The pure proportional navigation (PPN) guidance law is integrated with the MAV dynamics. A multivariable static output feedback (SOF) controller is designed for the linear state space model formulated in the IGC framework. The waypoint navigation algorithm handles the minimum turn radius constraint of the MAV. The algorithm also evaluates the feasibility of reaching a waypoint. Extensive non-linear simulations are performed on high fidelity 150 mm wingspan MAV model to demonstrate the potential advantages of the proposed waypoint navigation algorithm.

Analysis of Obstacle based Probabilistic RoadMap Method using Geometric Probability

Sampling based planners have been successful in robot motion planning, with many degrees of freedom, but still remain ineffective in the presence of narrow passages within the configuration space. There exist several heuristics, which generate samples in the critical regions and improve the efficiency of probabilistic roadmap planners. In this paper, we present an evaluation of success probability of one such heuristic method, called obstacle based probabilistic roadmap planners or OBPRM, using geometric probability theory. The result indicates that the probability of success of generating free sample points around the surface of the $n$ dimensional configuration space obstacle is directly proportional to the surface area of the obstacles.