Online artificial neural network model‐based nonlinear model predictive controller for the meridian UAS

Garcia, Gonzalo; Keshmiri, Shawn

doi:10.1002/rnc.3037

Cited by 22 publications

(14 citation statements)

References 32 publications

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“…1, will be used to model each air craft in simulation (see Refs. [28] and [29]). The Meridian UAS is a 1100 pound (499 kg), long range, high endurance autonomous aircraft designed, built, and flight tested by the University of Kansas Aerospace Engineering Department for the National Sci ence Foundation (NSF) Center for Remote Sensing of Ice Sheets (CReSIS) to provide an aerial platform for ice-penetrating radar developed for research in polar regions [27].…”

Section: Nonlinear Modeling Guidance and Controlmentioning

confidence: 95%

“…[30] and [31] and implemented in Ref. [28] has shown promise for excellent aircraft control characteristics, real-time fea sibility, and exhibits predictive qualities desirable for the present application, i.e., necessity for premonition in planning of avoid ance maneuvers. The NMPC formulation also provides a means to incorporate a full nonlinear description of the aircraft system and its actuator constraints, allowing an additional step in realism, over linear control techniques (operating in close range of some trim point), for an ultimate goal of flight worthy guidance and control in an expanded range of operation.…”

Section: Nonlinear Modeling Guidance and Controlmentioning

confidence: 99%

“…[28], [30][31][32][33][34] for detailed development) allows the UAS to follow moving points, i.e., to track a moving waypoint Figure 2 shows the waypoint generation logic, where / is the local (fiat Earth) inertial coordinate frame (see Ref. [35]) with components North N, East E, and Down D (Height H being the negative of Down).…”

Section: Nonlinear Modeling Guidance and Controlmentioning

confidence: 99%

See 2 more Smart Citations

Collision and Obstacle Avoidance in Unmanned Aerial Systems Using Morphing Potential Field Navigation and Nonlinear Model Predictive Control

Stastny

Garcia

Keshmiri

2014

Journal of Dynamic Systems, Measurement, and Control

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This paper presents a novel approach to collision and obstacle avoidance in fixed-wing unmanned aerial systems (UASs), vehicles with high speed and high inertia, operating in proximal or congested settings. A unique reformulation o f classical artifi cial potential field (APF) navigational approaches, adaptively morphing the functions' shape considering six-degrees-of-freedom (6DOF) dynamic characteristics and constraints of fixed-wing aircraft, is fitted to an online predictive and prioritized waypoint planning algorithm for generation o f evasive paths during abrupt encounters. The time-varying waypoint horizons output from the navigation unit are integrated into a combined guidance and non linear model predictive control scheme. Real-time avoidance capabilities are demonstrated in full nonlinear 6DOF simulation of a large unmanned aircraft showcasing evasion efficiency with respect to classical methods and collision free operation in a congested urban scenario.

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Section: Nonlinear Modeling Guidance and Controlmentioning

confidence: 95%

Section: Nonlinear Modeling Guidance and Controlmentioning

confidence: 99%

Section: Nonlinear Modeling Guidance and Controlmentioning

confidence: 99%

See 1 more Smart Citation

Collision and Obstacle Avoidance in Unmanned Aerial Systems Using Morphing Potential Field Navigation and Nonlinear Model Predictive Control

Stastny

Garcia

Keshmiri

2014

Journal of Dynamic Systems, Measurement, and Control

Self Cite

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“…The implemented guidance (see Ref. [20][21][22] for detailed development) allows the UASs to follow moving points, i.e. to track a moving waypoint characterized by time-varying position and velocity.…”

Section: Moving Point Guidance and Nonlinear Model Predictive Contmentioning

confidence: 99%

Guidance of multi-agent fixed-wing aircraft using a moving mesh method

Kim

Keshmiri

Huang

et al. 2015

2015 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper presents implementation of a novel guidance logic for multi-agent fixed-wing unmanned aerial systems using a moving mesh method. The moving mesh method is originally designed for use in the adaptive numerical solution of partial differential equations where a high proportion of mesh points are placed in the regions of large solution variation and few points in the rest of the domain. In this work, the positions of the aircraft are considered as mesh nodes and connected to form a triangular mesh in two spatial dimensions. The outer aircraft, or the boundary nodes of the mesh, are moved with the velocities which are specified using the the relative distance from and the heading angle of a reference point while keeping the formation of the aircraft in a desired shape. The inner agents or the interior mesh nodes, on the other hand, are moved with a moving mesh technique to keep the whole mesh as uniform as possible. The moving mesh technique has builtin mechanisms to keep the mesh as uniform as possible and prevent nodes from crossing over or tangling. This property can be seen as an automatic collision avoidance mechanism. It also has explicit formulas for nodal velocities, which makes the technique easy to implement on computer. The centralized moving mesh guidance was complimented by a decentralized nonlinear predictive controller to control each aircraft. The mesh nodes are replaced by unmanned aerial systems with nonlinear six degrees of freedom dynamics. To increase flexibility of aircraft in the formation, the moving point concept is used to follow arbitrary linear or curvature shaped flight segments.

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“…The special issue consists of six papers that cover several different flight control systems. Garcia and Keshmiri consider the safe control of the Meridian unmanned aerial system with abnormal conditions. A novel adaptive nonlinear model predictive controller is proposed and conceptually proven to ensure safe control of the Meridian unmanned aerial system in off‐nominal conditions.…”

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confidence: 99%

New advances in flight control systems

2013

Intl J Robust & Nonlinear

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EDITORIALNew advances in flight control systems Flight control systems are obviously very important for vehicles, such as aircrafts, helicopters, satellites, launch vehicles, missiles, hypersonic vehicles, airships, etc. Flight control systems play a crucial role in the stability augmentation of such flight vehicles. In the past, because most flight vehicles were assumed to be a rigid body, the flight control systems were generally transformed into linear systems by linearization. Consequently, traditional control methods proposed for flight control systems were mainly concerned with PID or gain scheduling-based techniques.With the higher requirements placed on aeronautical and space systems, in both transient and steady state phases, the required control performances expected from the flight control system has become more demanding. In this case, the PID-based algorithms are not satisfactory. In fact, with the expanded flight envelope of modern vehicles, the dynamics of such vehicles are inherently nonlinear. These nonlinearities include couplings, uncertainties, unmodeled dynamics, etc. The performance of the flight control systems to some extent is dominated by the aforementioned nonlinearities. If the nonlinearities are not very prominent, PID-based algorithms may be still sufficient. However, when the nonlinearities are prominent, for example, when flight vehicles perform a large aggressive maneuver, the nonlinearities will play a dominant role in the dynamics and, in turn, affects the performance of the flight control system. Under these circumstances, robust nonlinear control algorithms are necessary. The objective of this special issue is to bring attention to the latest advancements in flight control systems. With this goal in mind, we have invited several well-known researchers to present their recent research results in robust and nonlinear controls. The special issue consists of six papers that cover several different flight control systems. Garcia and Keshmiri[1] consider the safe control of the Meridian unmanned aerial system with abnormal conditions. A novel adaptive nonlinear model predictive controller is proposed and conceptually proven to ensure safe control of the Meridian unmanned aerial system in offnominal conditions. Controller performance is improved by updating the physics-based model by using real-time nonlinear estimation of aerodynamic forces. The authors also show that the proposed predictive controller coupled with real-time parameter identification, exhibits robust characteristics and successfully mitigates the impact of nonlinear and unsteady aerodynamics while preventing loss of control. The guidance control problem for a small unmanned aerial vehicle (UAV) is proposed in Liu, McAree, and Chen [2] so that the UAV will perform path-following under wind disturbances. A disturbance observer-based control approach is adopted. The wind information is first estimated by a nonlinear disturbance observer; then, it is incorporated into the nominal path-following controller to formulate ...

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Online artificial neural network model‐based nonlinear model predictive controller for the meridian UAS

Cited by 22 publications

References 32 publications

Collision and Obstacle Avoidance in Unmanned Aerial Systems Using Morphing Potential Field Navigation and Nonlinear Model Predictive Control

Collision and Obstacle Avoidance in Unmanned Aerial Systems Using Morphing Potential Field Navigation and Nonlinear Model Predictive Control

Guidance of multi-agent fixed-wing aircraft using a moving mesh method

New advances in flight control systems

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