**1. Introduction**

Currently, there are many possible uses for unmanned aerial vehicles (UAVs), such as inspection operation, battle field operation, forest fire detection, meteorological observation, or search and rescue operation, among others. All these applications require achieving precise control systems. This has motivated an increased interest in the last years from researchers in

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

developing effective control algorithms for UAVs [1–4]. In many cases, the development of new control strategies requires the use of software and platforms which are able to simulate the operation of the UAVs in order to perform experimental tests for evaluating the different designs. The use of this kind of tools increases the productivity and reduces the development time. For this purpose, different laboratory test rigs have been specifically designed for teaching and research in flight dynamics and control. One such platform is the laboratory helicopter used in this research, namely the Twin Rotor MIMO System (TRMS) [5]. The TRMS is a nonlinear, multivariable and underactuated system, characterised by a coupling effect between the dynamics of the propellers and the body structure, which is caused by the actionreaction principle originated in acceleration and deceleration of the motor-propeller groups. All these features make the control of the TRMS to be perceived as a challenging engineering problem (note that the TRMS, and other laboratory platforms with similar dynamics are more difficult to control than a real helicopter platform [6]). The achievement of an accurate system dynamics model is a challenging problem, whilst, at the same time, an important issue is to develop accurate and efficient control systems.

The development of the dynamic model for the TRMS has been studied by an important number of researches. Ahmad et al. presented mathematical models for the dynamic characterisation of the TRMS, using a black box system identification technique [7] and radial basis function (RBF) networks [8]. Shaheed modelled the dynamics of the TRMS by means of a nonlinear autoregressive process through external input (NARX) approach with a feedforward neural work and a resilient propagation (RPROP) algorithm [9]. Rahideh and Shaheed have also contributed to the study of the TRMS dynamics by using both Newton- and Lagrange-based methods [10], and two models based on neural networks using Levenberg-Marquardt (LM) and gradient descent (GD) algorithms [11]. Toha and Tokhi presented an adaptive neuro-fuzzy inference system (ANFIS) network design, which was deployed and used for the TRMS modelling [12]. Finally, Tastemirov et al. developed a complete dynamic TRMS model using the Euler-Lagrange method [13].

On the other hand, the design of the control system for the TRMS has been widely discussed through several investigations. Ahmad et al. developed the dynamic model and implemented a feed-forward/open-loop control [14] and a linear quadratic Gaussian control [15]. López-Martínez et al. studied the design of a longitudinal controller based on Lyapunov functions [16], and the application of a nonlinear *L*2 controller [17]. Rahideh et al. presented an experimental implementation of an adaptive dynamic nonlinear model inversion control law using artificial neural networks [18]. Other interesting works are those of Tao et al. who designed a parallel distributed fuzzy linear quadratic regulator (LQR) controller [19]. Studies of Reynoso-Meza et al. developed a holistic multi-objective optimisation design technique for controller tuning [20], or the use of a particle swarm optimisation (PSO) algorithm for the proportionalintegral-derivative (PID) controller optimisation developed by Coelho et al. [21].

The aim of the present research is to develop a nonlinear cascade-based control algorithm in order to locally guarantee an asymptotically and exponentially stable behaviour of the controlled generalised coordinates of the TRMS. Additionally, the effectiveness of the proposed nonlinear feedback controller in terms of stabilisation and position tracking performance is demonstrated by means of numerical simulations. Finally, the paper is organised as follows.

Section 2 introduces a description of the TRMS platform by illustrating the details of the dynamics model obtained into two phases: electrical and mechanical parts. Section 3 describes the nonlinear cascade-based controller scheme proposed. The results of the numerical simulations performed under the MATLAB®/Simulink® environment are depicted in Section 4, and, finally, Section 5 is devoted to the conclusions of the work.
