This paper presents the design of a control strategy based on exact feedback linearization and dynamic stabilization for the mathematical model of a benchmark Quanser platform emulating the elevation, pitch, and travel motions of a three-degree-of-freedom (3-DOF) tandem helicopter. The orientation dynamics of the aerial vehicle are described by a set of highly coupled, inherently unstable nonlinear differential equations. Unlike conventional feedback linearization approaches, the core contribution of this work lies in a strategic subsystem decoupling technique combined with a symmetric input transformation and a formal proof of exact linearizability. Once the exact linear canonical representation is derived, optimal Linear Quadratic Regulator (LQR) control gains are synthesized directly within the transformed coordinates. Subsequently, a trajectory tracking scheme is developed for each degree of freedom, simulating aggressive trajectory changes that emulate obstacle-avoidance maneuvers during a mission. Numerical simulations in MATLAB/Simulink demonstrate that the proposed control scheme achieves precise tracking and rapid stabilization while strictly adhering to the physical angular bounds of the experimental platform and operational actuator constraints.

In this paper we focus on the human arm motion capture, which is motivated by the requirements in physical rehabilitation and training of stroke patients in the same way as monitoring of elderly person activities. The proposed methodology uses a data fusion of low-cost and low-weight MEMS sensors jointly to an a priori knowledge of the arm anatomy. The main goal is to estimate the arm position, the anatomical movements of the shoulder and its accelerations. We propose a discrete optimization based-approach which aims to search the optimal attitude ambiguity directly without decorrelation of ambiguity, and to computing the baseline vector consequently. The originality of this paper is to apply the discrete optimization to track the desired trajectory of a nonlinear system such as the Human Movement in the presence of uncertainties. The global asymptotic convergence of the nonlinear observer is guaranteed. Extensive tests of the presented methodology with real world data illustrate the effectiveness of the proposed procedure.