Enhancing Autonomy in VTOL Aircraft Based on Symbolic Computation Algorithms
Research into the autonomy of small Unmanned Aerial Vehicles (UAVs), and especially on Vertical Take Off and Landing (VTOL) systems has intensified significantly in recent years. This paper develops a generic model of a VTOL UAV in symbolic form. The nove
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stract. Research into the autonomy of small Unmanned Aerial Vehicles (UAVs), and especially on Vertical Take Off and Landing (VTOL) systems has intensified significantly in recent years. This paper develops a generic model of a VTOL UAV in symbolic form. The novelty of this work stems from the designed Model Predictive Control (MPC) algorithm based on this symbolic model. The MPC algorithm is compared with a state-of-the-art Linear Quadratic Regulator algorithm in attitude rate acquisition and its more accurate performance and robustness to noise is demonstrated. Results for the controllers designed for each of the aircraft’s angular rates are presented in response to input disturbances.
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Introduction
Vertical Take-off and Landing (VTOL) aircraft are unique in having a propulsion system that allows the generation of lift independently of the aircraft’s velocity. This affords the vehicle to conduct controlled manoeuvres in scenarios where other vehicles may be unable to operate. More recently, the growth of micro-Unmanned Aerial Vehicle (UAV) technology has seen a rise in interest in developing compact VTOL systems as a platform for a range of applications, such as search and rescue, ordinance surveying and aerial cinematography [1]. The current applications of these systems are however, limited by the level of autonomy that has been achieved to allow the system to handle events that could otherwise compromise the vehicle. Robust control regimes able to handle events such as gusts, or rotor loss are highly desirable in enhancing the future of autonomous vehicles. One of the most powerful methods for control is the Model Predictive Control (MPC) [14]. Recent advances for MPC algorithms are presented in the recent survey [10]. Although widely applied to industrial systems, partially to fixed-wing aircraft and UAV formations [3], the MPC application to VTOL is still limited. One of the main advantages of the MPC is that it can provide both the desired level of performance and safety. This is especially important for small VTOL aircraft, who’s applications may be limited by their resilience to disturbances. Efficient numerical methods for non-linear MPC and moving horizon estimation are presented in [4]. Other efficient algorithms for linear small scale control are c Springer International Publishing Switzerland 2016 L. Alboul et al. (Eds.): TAROS 2016, LNAI 9716, pp. 99–110, 2016. DOI: 10.1007/978-3-319-40379-3 10
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presented in [8]. Although a significant efforts have been devoted to both linear and non-linear MPC, including in [12] its application to VTOL UAVs is still limited. Hence, in this paper we explore the advantages on the MPC approach in the light of VTOL craft. The MPC performance is compared with a Linear Quadratic Gaussian (LQG) regulator and its accuracy is demonstrated. The main contributions of this paper stem from: (i) the developed symbolic model of the VTOL UAV. The model is general and comprises all possible motions and changes in 3D manoeuvres. (ii) the designed MPC algorithm
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