Fuzzy Multi-model Switching H-Infinity Control for Helicopters in a Full Envelope
- PDF / 438,871 Bytes
- 13 Pages / 439.37 x 666.142 pts Page_size
- 60 Downloads / 138 Views
Fuzzy Multi-model Switching H-Infinity Control for Helicopters in a Full Envelope Jiyang Dai · Chang Tan · Jin Ying · Guohui Wu
Received: 5 May 2012 / Revised: 22 January 2013 © Springer Science+Business Media New York 2013
Abstract This paper presents a fuzzy multi-model switching H-infinity control law for a helicopter in a full envelope. The space of the nonlinear model for a helicopter is partitioned to some sub-spaces of the multiple linearized models using the fuzzy set theory. We use the genetic algorithm to design a local H-infinity controller for each linearized model of the helicopter at the different equilibrium point. The fuzzy logic strategy can handle smoothly the controller switching of the linearized models of the helicopter. The flight controller for the helicopter is synthesized using the proposed control law design technique. The simulation and performance evaluation show that the designed helicopter flight control can guarantee the smooth switching of control laws of the helicopter. Keywords Helicopter flight control · H-infinity control · Multi-model control · Fuzzy switching 1 Introduction In the field of H-infinity and fuzzy control, Sun et al. studied finite frequency Hinfinity control problems for vehicle active suspension systems [42] and active susJ. Dai () · J. Ying · G. Wu Key Laboratory of Nondestructive Testing (Ministry of Education), Nanchang Hangkong University, Nanchang 330063, PR China e-mail: [email protected] J. Ying e-mail: [email protected] G. Wu e-mail: [email protected] C. Tan College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China e-mail: [email protected]
Circuits Syst Signal Process
pension control problems with frequency band constraints and actuator input delay [43]; Wu et al. discussed model approximations for discrete-time state-delay systems in the T-S fuzzy framework [48] and fuzzy filter design methods for nonlinear Ito stochastic systems with application to sensor fault detection [47]; Lin et al. presented a robust adaptive fuzzy sliding mode control algorithm for a class of uncertain discrete-time nonlinear systems [27]; Ho et al. designed the stable and quadraticoptimal static output feedback controllers for T-S fuzzy model-based control systems [24]; Su et al. proposed a novel approach to filter design for T-S fuzzy discretetime systems with time-varying delay [40]; Wu et al. developed a new approach to stabilize discrete-time T-S fuzzy time-varying delay systems [49]. The system modeling, filtering, and parameter estimation are basic for control problems [8, 9, 39, 50, 53]. Parameter estimation is the basic methods of system modeling [11, 12]. Recently, some new identification methods have been reported [30, 31, 33], e.g., the multi-innovation stochastic gradient algorithms [4, 10, 28, 32, 44], the multi-innovation least squares algorithms [19], the auxiliary model-based identification algorithms [15, 20, 29], the stochastic gradient algorithms [14, 17, 51], the hierarchical identification algorithms [16, 23,
Data Loading...
