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RISS 인기검색어
- 검색키워드
- 저자 : Salah Sukkarieh (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Model Predictive Unified Planning and Control of Rotary-wing Unmanned Aerial Vehicle
Kwangjin Yang,Salah Sukkarieh 제어로봇시스템학회 2012 제어로봇시스템학회 국제학술대회 논문집 Vol.2012 No.10
This paper presents an integrated planning and control algorithm for the navigation of an autonomous rotary wing unmanned aerial vehicle (RUAV) in a cluttered environment. The model predictive control (MPC) combined with a potential field-like function integrates the planning and control in a single step. This strategy enables the generation of dynamically feasible motion because the planning is done in the action space of the RUAV. Moreover, it is able to respond promptly to abrupt changes in the environment because the reactive obstacle avoidance method only considers local sensory information. Finally, the shortsightedness of control-based reactive methods can be relieved by the larger planning horizon of the model predictive control. Simulation results show that the proposed integrated planning and control method can complete the navigation mission successfully in cluttered environment.
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Adaptive Nonlinear Model Predictive Path-Following Control for a Fixed-wing Unmanned Aerial Vehicle
강연식,양광진,Salah Sukkarieh 제어·로봇·시스템학회 2013 International Journal of Control, Automation, and Vol.11 No.1
This paper presents an adaptive Nonlinear Model Predictive Control (NMPC) for the path-following control of a fixed-wing unmanned aerial vehicle (UAV). The objective is to minimize the mean and maximum errors between the reference path and the UAV. Navigating in a cluttered environment requires accurate tracking. However, linear controllers cannot provide good tracking performance due to nonlinearities that arise in the system dynamics and physical limitations such as actuator saturation and state constraints. NMPC provides an alternative since it can combine multiple objectives and constraints, which minimize the objective function. However, it is difficult to decide appropriate control horizon since the path-following performance depends on the profile of the path. Therefore, a fixed-horizon NMPC cannot guarantee accurate tracking performance. An adaptive NMPC that varies the control horizon according to the path curvature profile for tight tracking is proposed in this paper. Simulation results show that the proposed adaptive NMPC controller can follow the path more accu-rately than a conventional, fixed-horizon NMPC.
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