Dynamic consensus of linear multi-agent system using self-triggered distributed model predictive control.

This article discusses self-triggering algorithm using distributed model predictive control (DMPC) to achieve dynamic consensus in linear multi-agent systems (MASs). The iterative computations and communications required at each time step in traditional consensus algorithms cause escalation of the energy consumption and shorten the life span of the MAS. An attempt to solve this problem is made by proposing a sequential self-triggering consensus algorithm, where each agent computes its own triggering instants.

Output feedback based adaptive composite nonlinear flight control design for a small-scale un-crewed helicopter.

This correspondence deals with the trajectory tracking control of an un-crewed helicopter during hover/low-speed flights. A multi-loop architecture is used in which the inner-loop holds the fast changing dynamics and the outer-loop establishes the trajectory tracking. The inner-loop is closed with a constrained H based controller which is cautiously designed to address actuator saturation, atmospheric wind disturbance, and parametric uncertainty.

Design of a multivariable stimulus for Emotional-Learning based control of a 2-DOF laboratory helicopter.

Emotional-Learning based controllers are becoming increasingly popular due to their non-parametric and flexible design approach. However, most of the existing Emotional-Learning based control strategies are designed specifically for individual loops and are not suitable for superior performance in a strongly coupled MIMO system. In this technical note, a multi-variable Emotional-Learning based strategy for trajectory tracking in a strongly coupled MIMO system is proposed.