System identification and robust stabilization using structured controller for a novel ducted fan flying robot

Abstract

This paper proposes complete procedures for system identification and control of a novel ducted fan flying robot on near hover flight condition. A control-oriented model structure is derived from a comprehensive mechanism model and identified by ground tests and flight experiments. In order to achieve state decoupling and reference tracking, a two-loop control architecture is employed. Non-smooth optimization algorithm is applied to efficiently tune controller parameters against multiple control requirements. Experimental results show that designed structured controller can provide better performance than classical proportional-integral-derivative (PID) controller.

Aerodynamic performance and analysis of a hovering micro-scale shrouded rotor in confined environment

“…The experiments are conducted at the Vehicle Research Center of BIT (Beijing Institute of Technology). There are a variety of SR vehicles 10,11,[38][39][40][41] developed successfully in the laboratory. In our past work, 10 the test model is mounted vertically and reversely on a test bench (0.3 m 3 0.3 m 3 1.2 m) so that the air flows from bottom to top.…”

Flight and Interaction Control of an Innovative Ducted Fan Aerial Manipulator

“…Since the aerial platform works in hovering or near-hovering condition in the aerial manipulation scenarios, to facilitate the controller design process in Section 3.2, a linear state space model is identified at the hovering condition using the frequency domain identification method. Following the identification procedure in [31], and let…”

“…Then the structural controller parameters can be tuned by solving the H-infinity norm optimization problem satisfying the stability margin constraint to ensure the stability and robust performance of the closed-loop system. The detailed theory and design process can be referred to in [33] and our previous work [31]. The stability margin of the closed loop system with the designed inner loop controller is shown in Figure 5.…”

“…Combined with Equations ( 24), (31), and (33) and considering the boundary of state estimation error in Equation ( 29) the adaptive state error between the ideal adaptive system and the real adaptive system can be proved uniformly bounded by L 1 norm stability theorem [35] as…”