## 1 Introduction  
It is essential that the flight control system of an unmanned helicopter (UH) should be endowed with well-suited automatic capabilities to carry out flight missions. However, the flight performance of an UH is intimately dependent on the stability and control characteristics of the UH [1,2]. Unlike some mechanical systems with desirable structural properties, UH is normally an inherently unstable system without stability augmentation control strategy. Furthermore, some of the aerodynamic parameters vary with flight environments or system conditions, with the result that it is very difficult to design the stability augmentation system for an UH using the conventional control methods [3]. UH is also a complicated nonlinear dynamic system described by nonlinear differential equations. However, for simplicity in the design of controllers for an UH, the linearized models are normally derived from nonlinear differential equations if the UH operates around an operating point. Many linear control techniques for application to UH flight control systems have been proposed in literature, among which single-input, single-output (SISO) feedback control methods are by far the most common choices with few dependencies on dynamic models. In [4], a SISO PD control law is adopted and further optimized for both hovering and forward flight of the CMU-R50 UH. In [5], a SISO PID control law is implemented for automatic hovering of the Ursa Major 3 UH. The above SISO methods have the advantages of conceptual and computational simplicity. However they may decrease the stability and control qualities of UHs without considering parametric uncertainties and cross-couplings among axes. Therefore, in order to improve the flight performance, a lot of research effort has been devoted to the design of advanced stability augmentation systems. Previous research reported in the literature includes gain scheduling [6], linear-quadratic regulation (LQR) or linear-quadratic Gaussian (LQG) approach [7], decentralized decoupled model predictive approach [8] and intelligent control methods like neural network [9] and fuzzy logic approach [10], _etc._ Several flight control systems using H\({}_{\infty}\) control methods, which can provide the robust stability and performance for the systems subject to uncertainties and disturbances, have been designed for mini UHs. In [11], a H\({}_{\infty}\) loop shaping technique is utilized for the stability augmentation system of Bell-205 helicopter. Mixed-norm optimization and weighted H\({}_{\infty}\) mixed sensitivity optimization methods are respectively designed to improve the stability and maneuverability characteristics of UHs [12,13]. Although these methods have achieved acceptable flight performance, they still rely heavily on the plant model. More importantly, the above-mentioned methods fail to consider the adverse effects of parametric uncertainties and measurement noises on the flying qualities. Some existing adaptive techniques can accommodate the parametric uncertainties more effectively without considering measurement noises [14, 15, 16, 17, 18]. A novel modified model reference adaptive control (MRAC) strategy is developed with added noise [19]. However, this method only reduces the noise disturbance using a low-pass filter. Usually in practical use, those existing adaptive control methods can hardly minimize the adverse effect of measurement noises on the flying qualities. The NASA Marshall Space Flight Center has developed an adaptive augmenting control (AAC) algorithm for launch vehicles by adapting a well tuned classical control algorithm to unexpected environments or variations in vehicle dynamics. The AAC algorithm has been successfully tested in a relevant environment. However it needs to be further evaluated by the flight tests [20]. The essential parameter regulation schemes can reduce the complexity of a high performance control system design problem in the presence of parametric uncertainties and measurement noises.