Hierarchical control system synthesis for rotorcraft-based unmanned aerial vehicles

Shim, David Hyunchul; Kim, H.J.; Sastry, S. Shankar

doi:10.2514/6.2000-4057

Cited by 131 publications

(99 citation statements)

References 3 publications

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“…Typically, f f (·) and f m (·) are nonlinear functions and the differences between SUH models are mainly in here. Details of the two functions can be found in [1,14].…”

Section: Unmanned Helicopter Modelingmentioning

confidence: 99%

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Self-Healing Control Design under Actuator Fault Occurrence on Single-rotor Unmanned Helicopters

Theilliol

et al. 2016

J Intell Robot Syst

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Actuator faults are inevitable but affect reliability and safety of unmanned helicopters (UHs), especially when there are actuator constraints. In this paper, self-healing control, which is an extended active fault-tolerant control (FTC) method with reference redesign on-line, is proposed to analyze and to guarantee the safety of single-rotor UHs (SUHs) under both actuator faults and constraints. The safety includes body safety and mission safety. More specifically, body safety represents the stability of SUH itself and mission safety represents mission accomplishment with acceptable performance, furthermore, set-point tracking mission is considered. The main contribution of this paper is to analyze and to guarantee the safety of SUHs by solving a set of Linear Matrix Inequalities (LMIs) at one time. The set of LMIs includes saturation compensator design and stability guaranty with a given controller in the absence of actuator constraints, actuator fault compensation analysis, reference reachability analysis and reference redesign. On the other hand, by adding swashplate configuration, SUH model with real actuator outputs as control inputs is constructed which can describe actuator faults more clearly compared to SUH models with nominal control inputs. Finally, the proposed self-healing control method is illustrated by simulation with a nonlinear SUH model.

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“…Typically, f f (·) and f m (·) are nonlinear functions and the differences between SUH models are mainly in here. Details of the two functions can be found in [1,14].…”

Section: Unmanned Helicopter Modelingmentioning

confidence: 99%

“…During the last decades, many models of SUHs have been proposed [1,6,14,17]. All of these models are compose of kinematics and dynamics.…”

Section: Unmanned Helicopter Modelingmentioning

confidence: 99%

Self-Healing Control Design under Actuator Fault Occurrence on Single-rotor Unmanned Helicopters

Theilliol

et al. 2016

J Intell Robot Syst

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“…The flight control software, implemented on QXXTAr real-time operation system, manages sensors, vehicle control, and communication. Nore detailed theoretical and practical issues in building an RUAV are described in [4].…”

Section: Vehicle Platform and Dynamicsmentioning

confidence: 99%

Flying robots: modeling, control and decision making

Kim¹,

Shim²,

Sastry³

2002

Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292)

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This paper presents a flight rnanageiiient system (FhIS) iinpleniented as on-board intelligence for rotorcraft-based unmanned aerial vehicles (RUAVs), in order to gradually refine gilen abstract mission coniinands into real-time control signals for each vehicle. A strategy planner uses the probabilistic decision making algorithms to determine suboptinial action at each time step. A graphical interface on ground station enables human intervention. We derive nonlinear dynamics model upon which we design a tracking control layer using nonlinear model predictive control and integrate with a trajectory generator for logistical action planning. The proposed structure has been implemented on Berkeley RUAVs and validated in probabilistic pursuit-evasion games to show the possibility of intelligent flying robots.

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“…This implies that the guidance and control subsystems are designed separately. The inner-loop SAS (Stability Augmentation System) and autopilots are designed to follow the commands that are generated by the outer-loop guidance algorithm, because the guidance loop has a much larger time constant compared to the inner-loop controller [1,2]. In the design of the guidance loop, as the characteristics of the controller are not considered directly, the designed guidance loop may generate large control inputs that are hard on the control subsystems.…”

Section: Introductionmentioning

confidence: 99%

Integrated Design of Rotary UAV Guidance and Control Systems Utilizing Sliding Mode Control Technique

Hong¹,

Kim²

2012

International Journal of Aeronautical and Space Sciences

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In this paper, the Integrated Guidance and Control (IGC) law is proposed for the Rotary Unmanned Aerial Vehicle (RUAV). The objective of the IGC law is to consider the nonlinear dynamic characteristics of the RUAV and to design a guidance law which takes into consideration the nonlinear relationship between kinematics and dynamics. In order to control the RUAV system, sliding mode control scheme is adopted. As the RUAV is an under-actuated system, a slack variable approach is used to generate the available control inputs. Through the Lyapunov stability theorem, the stability of the proposed IGC law is proved. In order to verify the performance of the IGC law, numerical simulations are performed for waypoint tracking missions.

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Hierarchical control system synthesis for rotorcraft-based unmanned aerial vehicles

Cited by 131 publications

References 3 publications

Self-Healing Control Design under Actuator Fault Occurrence on Single-rotor Unmanned Helicopters

Self-Healing Control Design under Actuator Fault Occurrence on Single-rotor Unmanned Helicopters

Flying robots: modeling, control and decision making

Integrated Design of Rotary UAV Guidance and Control Systems Utilizing Sliding Mode Control Technique

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