Variance-constrained control of maneuvering helicopters with sensor failure

Abstract

This article presents the novel results obtained using variance-constrained controllers and maneuvering helicopters also when some helicopter sensors fail. For this purpose, complex, control oriented, and physics-based helicopter models are used. A nonlinear model of the helicopter, which includes blade flexibility, is first linearized around specific maneuvering flight conditions (i.e. level banked turn and helical turn). The resulting linearized models are used for the design of variance-constrained controllers (i.e. output and input variance-constrained controllers). Then, the robustness of the closed-loop systems with respect to modeling uncertainties (i.e. flight conditions and helicopter inertial parameters variations) is studied. Next, variance-constrained controllers are designed for these maneuvering helicopter models when some helicopter sensors fail. Several sensor failure cases are examined and robustness properties of the closedloop systems with respect to modeling uncertainties are also examined. Limitations of the control design process due to the number and type of failed sensors are investigated as well. Finally, the possibility to adaptively switch between controllers in order to mitigate sensor failure is studied.

Integrated Maneuvering Helicopter Model and Controller Design

“…The feasibility of using these controllers for our complex, control oriented helicopter models was illustrated recently in Ref. 17, where OVC controllers were designed for given helicopters.…”

Simultaneous Helicopter and Control-System Design

“…The feasibility of using these controllers for our complex, control oriented helicopter models was illustrated recently in Ref. 19. For OVC design the problem described next must be solved (see Refs.…”

“…State estimators are crucial for complex systems such as helicopters, since some states cannot be easily measured. Second, these controllers use second order information, namely the state covariance matrix [11][12][13][14][15][16][17][18][19] . This is very important for multivariable control design because it enables parameterization of all stabilizing controllers in terms of the state covariance matrix, which is physically meaningful.…”

Output variance constrained bending control of rotating Euler–Bernoulli beam

“…Variance constrained controllers have been used for many physical systems (e.g. helicopters, [17][18][19][20][21][22][23][24] tiltrotor aircraft, 25 Hubble space telescope, 26 and tensegrity structures 27 ) before. Variance constrained controllers have many advantages with respect to the other existing control strategies in the literature.…”