Model Predictive Control of a Thrust-Vectored Flight Control Experiment

Dunbar, William B.; Milam, Mark B.; Franz, Ryan; Murray, Richard M.

doi:10.3182/20020721-6-es-1901.00965

Cited by 30 publications

(26 citation statements)

References 16 publications

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“…In the last decade there have been an increasing interest in investigating MPC for aeronautical applications [Keviczky and Balas, 2006, Gros et al, 2012, Dunbar et al, 2002. The biggest interest have been to design reconfigurable flight control laws that can adapt to actuator failures and battle damages [Almeida and Leissling, 2009, Maciejowski and Jones, 2003, Kale and Chipperfield, 2005.…”

Section: Research Motivationmentioning

confidence: 99%

Model Predictive Control in Flight Control Design : Stability and Reference Tracking

Simon

2014

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Aircraft are dynamic systems that naturally contain a variety of constraints and nonlinearities such as, e.g., maximum permissible load factor, angle of attack and control surface deflections. Taking these limitations into account in the design of control systems are becoming increasingly important as the performance and complexity of the controlled systems is constantly increasing. It is especially important in the design of control systems for fighter aircraft. These require maximum control performance in order to have the upper hand in a dogfight or when they have to outmaneuver an enemy missile. Therefore pilots often maneuver the aircraft very close to the limit of what it is capable of, and an automatic system (called flight envelope protection system) against violating the restrictions is a necessity.In other application areas, nonlinear optimal control methods have been successfully used to solve this but in the aeronautical industry, these methods have not yet been established. One of the more popular methods that are well suited to handle constraints is Model Predictive Control (MPC) and it is used extensively in areas such as the process industry and the refinery industry. Model predictive control means in practice that the control system iteratively solves an advanced optimization problem based on a prediction of the aircraft's future movements in order to calculate the optimal control signal. The aircraft's operating limitations will then be constraints in the optimization problem.In this thesis, we explore model predictive control and derive two fast, low complexity algorithms, one for guaranteed stability and feasibility of nonlinear systems and one for reference tracking for linear systems. In reference tracking model predictive control for linear systems we build on the dual mode formulation of MPC and our goal is to make minimal changes to this framework, in order to develop a reference tracking algorithm with guaranteed stability and low complexity suitable for implementation in real time safety critical systems.To reduce the computational burden of nonlinear model predictive control several methods to approximate the nonlinear constraints have been proposed in the literature, many working in an ad hoc fashion, resulting in conservatism, or worse, inability to guarantee recursive feasibility. Also several methods work in an iterative manner which can be quit time consuming making them inappropriate for fast real time applications. In this thesis we propose a method to handle the nonlinear constraints, using a set of dynamically generated local inner polytopic approximations. The main benefits of the proposed method is that while computationally cheap it still can guarantee recursive feasibility and convergence.v Populärvetenskaplig sammanfattningFlygplan är dynamiska system som naturligt innehåller en mängd begränsningar och olinjäriteter så som t.ex. max tillåten lastfaktor, anfallsvinkel och roderlägen. Att ta hänsyn till dessa begränsningar i designen av styrsystem blir allt viktiga...

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Section: Research Motivationmentioning

confidence: 99%

Model Predictive Control in Flight Control Design : Stability and Reference Tracking

Simon

2014

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“…Dunbar et al (2002) applied receding horizon control to the Caltech Ducted Fan based on a B-spline parameterization of the trajectories. In recent years, pseudospectral methods, and in particular the Legendre pseudospectral (PS) method (Elnagar, 1995;Ross & Fahroo, 2003), have been used for real-time generation of optimal trajectories for many systems.…”

Section: Optimal Trajectory Generationmentioning

confidence: 99%

Predictive Control of Tethered Satellite Systems

Williams¹

2010

Model Predictive Control

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“…The directed graph G = (V, E) that corresponds to this example has vertex and edge sets V = {1, 2, 3, 4, 5, 6} and E = {(1, 2), (1, 3), (2, 3), (2,5), (3,2), (3,5), (4, 2), (4, 5), (5, 2), (5, 3), (6,5), (6, 3)}. Figure 7 shows the desired formation depicted as a directed graph, where each vehicle has 2 neighbors with a desired distance of 1 from each neighbor.…”

Section: Six-vehicle Formation Stabilization Simulationmentioning

confidence: 99%

“…Instead, we use a variation of backstepping procedure called "cascade backstepping" given in Olfati Saber [7,8] that does not make use of any recursive procedure for construction of Lyapunov functions. An alternative approach that accounts for the control constraints is to use Model Predictive Control (MPC) as in Dunbar et al [2], where similar restrictive control constraints need to be satisfied for MPC of the Caltech ducted fan experiment. Formation stabilization of three MVWT vehicles using MPC is considered in Dunbar and Murray [3].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear and cooperative control of multiple hovercraft with input constraints

Dunbar

Saber

Murray

2003

2003 European Control Conference (ECC)

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In this paper, we introduce an approach for distributed nonlinear control of multiple hovercraft-type underactuated vehicles with bounded and unidirectional inputs. First, a bounded nonlinear controller is given for stabilization and tracking of a single vehicle, using a cascade backstepping method. Then, this controller is combined with a distributed gradient-based control for multi-vehicle formation stabilization using formation potential functions previously constructed. The vehicles are used in the Caltech Multi-Vehicle Wireless Testbed (MVWT). We provide simulation and experimental results for stabilization and tracking of a single vehicle, and a simulation of stabilization of a six-vehicle formation, demonstrating that in all cases the control bounds and the control objective are satisfied.

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Model Predictive Control of a Thrust-Vectored Flight Control Experiment

Cited by 30 publications

References 16 publications

Model Predictive Control in Flight Control Design : Stability and Reference Tracking

Model Predictive Control in Flight Control Design : Stability and Reference Tracking

Predictive Control of Tethered Satellite Systems

Nonlinear and cooperative control of multiple hovercraft with input constraints

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