Showing papers in "International Journal of Robust and Nonlinear Control in 2002"
TL;DR: A new method of model reduction for nonlinear control systems is introduced, which requires only standard matrix computations and shows that when it is applied to linear systems it results in the usual balanced truncation.
Abstract: In this paper, we introduce a new method of model reduction for nonlinear control systems. Our approach is
to construct an approximately balanced realization. The method requires only standard matrix computations,
and we show that when it is applied to linear systems it results in the usual balanced truncation. For
nonlinear systems, the method makes use of data from either simulation or experiment to identify the
dynamics relevant to the input}output map of the system. An important feature of this approach is that the
resulting reduced-order model is nonlinear, and has inputs and outputs suitable for control. We perform an
example reduction for a nonlinear mechanical system.
570 citations
TL;DR: In this article, the identification of a class of discrete-time nonlinear systems known as linear parameter varying systems (LPSS) is considered, and the identification problem can be reduced to a linear regression, and compact formulae for the corresponding least mean square and recursive least square algorithms are provided.
Abstract: We consider identification of a certain class of discrete-time nonlinear systems known as linear parameter varying system. We assume that inputs, outputs and the scheduling parameters are directly measured, and a form of the functional dependence of the system coefficients on the parameters is known. We show how this identification problem can be reduced to a linear regression, and provide compact formulae for the corresponding least mean square and recursive least-squares algorithms. We derive conditions on persistency of excitation in terms of the inputs and scheduling parameter trajectories when the functional dependence is of polynomial type. These conditions have a natural polynomial interpolation interpretation, and do not require the scheduling parameter trajectories to vary slowly. This method is illustrated with a simulation example using two different parameter trajectories. Copyright © 2002 John Wiley & Sons, Ltd.
408 citations
TL;DR: The multilayer perceptron neural network is introduced and how it can be used for function approximation is described and several techniques for improving generalization are discussed.
Abstract: SUMMARY The purpose of this paper is to provide a quick overview of neural networks and to explain how they can be used in control systems. We introduce the multilayer perceptron neural network and describe how it can be used for function approximation. The backpropagation algorithm (including its variations) is the principal procedure for training multilayer perceptrons; it is briefly described here. Care must be taken, when training perceptron networks, to ensure that they do not overfit the training data and then fail to generalize well in new situations. Several techniques for improving generalization are discussed. The paper also presents three control architectures: model reference adaptive control, model predictive control, and feedback linearization control. These controllers demonstrate the variety of ways in which multilayer perceptron neural networks can be used as basic building blocks. We demonstrate the practical implementation of these controllers on three applications: a continuous stirred tank reactor, a robot arm, and a magnetic levitation system. Copyright # 2002 John Wiley & Sons, Ltd.
293 citations
TL;DR: In this paper, the authors present fuel/time-optimal control algorithms for a co-ordination and control architecture that was designed for a fleet of spacecraft, including low-level formation-keeping algorithms and a high-level fleet planner that creates trajectories to re-size or re-target the formation.
Abstract: SUMMARY Formation flying of multiple spacecraft is an enabling technology for many future space science missions. However, the co-ordination and control of these instruments poses many difficult design challenges. This paper presents fuel/time-optimal control algorithms for a co-ordination and control architecture that was designed for a fleet of spacecraft. This architecture includes low-level formation-keeping algorithms and a high-level fleet planner that creates trajectories to re-size or re-target the formation. The trajectory and formation-keeping optimization algorithms are based on the solutions of linear and integer programming problems. The result is a very flexible optimization framework that can be used off-line to analyse various aspects of the mission design and in real time as part of an onboard autonomous formation flying control system. The overall control approach is demonstrated using a nonlinear simulation environment that includes realistic measurement noises, disturbances, and actuator nonlinearities. Copyright # 2002 John Wiley & Sons, Ltd.
240 citations
TL;DR: In this article, three linear, parameter-varying (LPV) approaches to control design of a turbofan engine are discussed. But, the time variation of each of the parameters is not known in advance, but is assumed to be measurable in real-time.
Abstract: This paper describes application of parameter-dependent control design methods to a turbofan engine. Parameter-dependent systems are linear systems, whose state-space descriptions are known functions of time-varying parameters. The time variation of each of the parameters is not known in advance, but is assumed to be measurable in real-time. Three linear, parameter-varying (LPV) approaches to control design are discussed. The first method is based on linear fractional transformations which relies on the small gain theorem for bounds on performance and robustness. The other methods make use of either a single (SQLF) or parameter-dependent (PDQLF) quadratic Lyapunov function to bound the achievable level of performance. The latter two techniques are used to synthesize controllers for a high-performance turbofan engine. A LPV model of the turbofan engine is constructed from Jacobian linearizations at fixed power codes for control design. The control problem is formulated as a model matching problem in the ℋ∞ and LPV framework. The objective is decoupled command response of the closed-loop system to pressure and rotor speed requests. The performance of linear, ℋ∞ point designs are compared with the SQLF and PDQLF controllers. Nonlinear simulations indicate that the controller synthesized using the SQLF approach is slightly more conservative than the PDQLF controller. Nonlinear simulations with the SQLF and PDQLF controllers show very robust designs that achieve all desired performance objectives. Copyright © 2002 John Wiley & Sons, Ltd.
202 citations
TL;DR: In this article, a decentralized architecture is proposed for autonomous establishment and maintenance of satellite formations, where the spacecraft can cooperatively track planned maneuvers and trajectories in the face of disturbances and uncertainties, while processing only local measurement information.
Abstract: SUMMARY A decentralized architecture is proposed for autonomous establishment and maintenance of satellite formations. Such an architecture does not require a central supervisor, as the spacecraft can cooperatively track planned maneuvers and trajectories in the face of disturbances and uncertainties, while processing only local measurement information. If the planned maneuvers and trajectories are themselves optimal, the decentralized framework generates a neighbouring optimal control. Use of such a controller simplifies and improves the robustness of formation operations, since it distributes an autonomous capability for orbit determination and relative navigation, formation maintenance, and maneuver trim among all of the spacecraft. In an example formation flying scenario, the decentralized approach successfully maintains the formation in the face of uncertainties and nonlinear perturbations, and produces identical results to those of a corresponding centralized controller. Published in 2002 by John Wiley & Sons, Ltd.
166 citations
131 citations
TL;DR: In this paper, a linear parameter-varying (LPV) control technique was used for the missile pitch-axis autopilot design, and the controller gain-scheduling function was constructed as affine matrix-valued function in the polytopic co-ordinates of the scheduled parameter.
Abstract: In this paper, the missile pitch-axis autopilot design is revisited using a new and recently available linear parameter-varying (LPV) control technique. The missile plant model is characterized by a linear fractional transformation (LFT) representation. The synthesis task is conducted by exploiting new capabilities of the LPV method: firstly, a set of H2/H∞ criteria defined channel-wise is considered; secondly, different Lyapunov and scaling variables are used for each channel/specification which is known to reduce conserva tism; and finally, the controller gain-scheduling function is constructed as affine matrix-valued function in the polytopic co-ordinates of the scheduled parameter. All these features are examined and evaluated in turn for the missile control problem. The method is shown to provide additional flexibility to tradeoff conflicting and demanding performance and robustness specifications for the missile while preserving the practical advantage of previous single-objective LPV methods. Finally, the method is shown to perform very satisfactorily for the missile autopilot design over a wide range of operating conditions. Copyright © 2001 John Wiley & Sons, Ltd.
116 citations
TL;DR: In this article, a range of optimization based approaches to fault diagnosis are presented, including (1) fault diagnosis (fault estimation, (FE)) for systems with model uncertainties; (2) fault estimation (FE) for system with parametric faults; and (3) FE for a class of nonlinear systems.
Abstract: SUMMARY This paper presents a range of optimization based approaches to fault diagnosis. A variety of fault diagnosis problems are reformulated in the so-called standard problem set-up introduced in the literature on robust control. Once the standard problem formulations are given, the fault diagnosis problems can be solved by standard optimization techniques. The proposed methods include (1) fault diagnosis (fault estimation, (FE)) for systems with model uncertainties; FE for systems with parametric faults, and FE for a class of nonlinear systems. Copyright # 2002 John Wiley & Sons, Ltd.
99 citations
TL;DR: In this paper, an adaptive, output feedback control design methodology is presented for a spacecraft formation flying (SFF) system, assuming that the leader spacecraft in the formation follows a no-thrust, natural, elliptical orbit.
Abstract: In this paper, an adaptive, output feedback control design methodology is presented for a spacecraft formation flying (SFF) system. A Lagrangian derivation of the SFF model is considered to produce position dynamics for follower spacecraft #n relative to follower spacecraft #(n−1), where n is an arbitrary positive integer, assuming that the leader spacecraft in the formation follows a no-thrust, natural, elliptical orbit. Next, a control law is designed to provide a filtered velocity measurement and a desired adaptive compensation with semi-global, asymptotic, relative position tracking. To show the efficacy of the control algorithm, all desired trajectories are generated online by numerically solving the unperturbed nonlinear SFF dynamics with initial conditions satisfying a no-thrust, natural orbit constraint equation. The proposed control law is simulated for the case of two and three spacecraft and is shown to yield semi-global, asymptotic tracking of the relative position in addition to the convergence of disturbance parameter estimates. Copyright © 2002 John Wiley & Sons, Ltd.
92 citations
TL;DR: In this paper, a high gain observer based on a triangular structure of nonlinear systems is proposed, and an algorithm capable of calculating a gain of the observer is given, which is then extended to a class of multi-output nonlinear system which contains the model of binary distillation columns.
Abstract: A high gain observer based on a triangular structure of nonlinear systems is proposed. An algorithm capable of calculating a gain of the observer is given. This observer synthesis is then extended to a class of multi-output nonlinear systems which contains the model of binary distillation columns. Finally, we illustrate the performance of the estimator using numerical simulations of a methanol–ethanol distillation column. Copyright © 2002 John Wiley & Sons, Ltd.
TL;DR: In this article, a method for determining the initial conditions that result in quasi-periodic relative orbits over the short term, in the presence of J2 perturbation, is presented.
Abstract: This paper deals with the determination of initial conditions and the design of fuel-balancing orbit control laws for a formation of satellites. Hill's equations describe the linearized dynamics of relative motion between two satellites. They admit bounded relative orbit solutions as special cases. Predictably, these bounded solutions break down in the presence of nonlinearities and perturbations. A method for determining the initial conditions that result in quasi-periodic relative orbits over the short term, in the presence of J2 perturbation, is presented. The control acceleration or equivalently, the fuel required to cancel the perturbation on a satellite depends upon its orbital inclination with respect to that of the reference satellite. An intelligent control concept that exploits the physics of the relative motion dynamics is presented. Analysis shows that this concept minimizes the total fuel consumption of the formation and maintains equal, average fuel consumption for each satellite. The concept is implemented using a novel, disturbance accommodating control design process. Numerical simulations and analytical results are in excellent agreement with each other. Copyright © 2002 John Wiley & Sons, Ltd.
TL;DR: In this paper, the authors present recent theoretical and experimental results on reset control, which was directly motivated by Horowitz's pioneering work on reset controllers in the 1970s, and show that with qualitative design, they can exhibit better performance trade-offs than those in linear, time-invariant systems.
Abstract: History shows that Prof. Isaac Horowitz was often ahead of the curve in his feedback control research, especially in developing quantitatively driven design procedures. In some topics, his work was so out of line with the main stream that it has received virtually no recognition from the control community until a few decades later. In this paper, we present recent research that was directly motivated by Horowitz's pioneering work on reset controllers in the 1970s. Reset controllers are linear controllers that reset some of their states to zero when their inputs reach a threshold. Horowitz motivated their use by showing that with qualitative design, they can exhibit better performance trade-offs than those in linear, time-invariant systems. This paper supports and advances his thinking by presenting recent theoretical and experimental results on reset control. Copyright © 2002 John Wiley & Sons, Ltd.
TL;DR: In this paper, a new methodology is proposed for the design of trajectory tracking controllers for autonomous vehicles based on gain scheduling control theory, and an application is made to design of a trajectory tracking controller for a prototype underwater vehicle (AUV).
Abstract: SUMMARY A new methodology is proposed for the design of trajectory tracking controllers for autonomous vehicles. The design technique builds on gain scheduling control theory. An application is made to the design of a trajectory tracking controller for a prototype autonomous underwater vehicle (AUV). The effectiveness and advantages of the new control laws derived are illustrated in simulation using a full set of non-linear equations of motion of the vehicle. Copyright # 2002 John Wiley & Sons, Ltd.
TL;DR: In this paper, the authors developed an efficient hybrid optimization algorithm to address the problem of task assignment among agents for terminal targets in the optimization process of fuel optimal spacecraft formation reconfiguration.
Abstract: The Air Force Research Laboratory has identified multiple spacecraft formation flying as an enabling technology for several future space missions. A key benefit of formation flying is the ability to reconfigure the spacecraft formation to achieve different mission objectives. In this paper, generation of fuel optimal manoeuvres for spacecraft formation reconfiguration is modelled and analysed as a multi-agent optimal control problem. Multi-agent optimal control is quite different from the traditional optimal control for single agent. Specifically, in addition to fuel optimization for a single agent, multi-agent optimal control necessitates consideration of task assignment among agents for terminal targets in the optimization process. In this paper, we develop an efficient hybrid optimization algorithm to address such a problem. The proposed multi-agent optimal control methodology uses calculus of variation, task assignment, and parameter optimization at different stages of the optimization process. This optimization algorithm employs a distributed computational architecture. In addition, the task assignment algorithm, which guarantees the global optimal assignment of agents, is constructed using the celebrated principle of optimality from dynamic programming. A communication protocol is developed to facilitate decentralized decision making among agents. Simulation results are included to illustrate the efficacy of the proposed multi-agent optimal control algorithm for fuel optimal spacecraft formation reconfiguration. Copyright © 2002 John Wiley & Sons, Ltd.
TL;DR: In this article, a model-based predictive regulation for a linear discrete-time system in the presence of unknown but bounded disturbances, partial state information and state/control constraints is proposed.
Abstract: This paper addresses model-based predictive regulation for a linear discrete-time system in the presence of unknown but bounded disturbances, partial state information and state/control constraints. The proposed nonlinear dynamic compensator uses a set-valued estimator, which recursively updates the membership set of the plant state, along with a receding-horizon regulator which selects on-line the control variable depending upon the current state membership set. It is shown that the overall scheme preserves feasibility if this is assumed from the outset, and hence guarantees closed-loop stability and constraint fulfilment. These properties rely on exact set-membership estimation. A simple approximation scheme which avoids set-membership estimation but preserves stability is also proposed and the relative performance/complexity tradeoffs are discussed. Simulation results demonstrate the effectiveness of the proposed method. Copyright © 2002 John Wiley & Sons, Ltd.
TL;DR: In this article, the authors formulate five different fault identification and signal estimation problems and derive the architecture of integrating the residual generator that isolates the fault and estimators that estimate the fault signals.
Abstract: We consider here both fault identification and fault signal estimation. Regarding fault identification, we seek either exact or almost fault identification. On the other hand, regarding fault signal estimation, we seek either $H_2$ optimal, $H_2$ suboptimal or Hinfinity suboptimal estimation. By appropriate combination of these two aspects, we formulate five different fault identification and fault signal estimation problems. We analyse and synthesize appropriate residual generators for fault identification and estimators which generate fault signal estimates for all these problems. Solvability conditions for all these problems are given. Also, for each of these problems, the architecture of integrating the residual generator that isolates the fault and estimators that estimate the fault signals is developed.
TL;DR: In this paper, a robust multiple-fault detection and identification algorithm is derived from solving an optimization problem, where the output error is divided into several subspaces, and the transmission from one fault, denoted the associated target fault, is maximized while the transmissions from other faults, referred to as associated nuisance faults, are minimized.
Abstract: A new robust multiple-fault detection and identification algorithm is determined. Different from other algorithms which explicitly force the geometric structure by using eigenstructure assignment or geometric theory, this algorithm is derived from solving an optimization problem. The output error is divided into several subspaces. For each subspace, the transmission from one fault, denoted the associated target fault, is maximized while the transmission from other faults, denoted the associated nuisance fault, is minimized. Therefore, each projected residual of the robust multiple-fault detection filter is affected primarily by one fault and minimally by other faults. The transmission from process and sensor noises is also minimized so that the filter is robust with respect to these disturbances. It is shown that, in the limit where the weighting on each associated nuisance fault transmission goes to infinity, the filter recovers the geometric structure of the restricted diagonal detection filter of which the Beard–Jones detection filter and unknown input observer are special cases. Filter designs can be obtained for both time-invariant and time-varying systems. Copyright © 2002 John Wiley & Sons, Ltd.
TL;DR: In this paper, a static anti-windup compensator is proposed to guarantee robust stability subject to input saturation and suppresses the degradation of robust performance during the saturation period, which is essentially a non-convex problem.
Abstract: In this paper, we propose a design method of a static anti-windup compensator that guarantees robust stability subject to input saturation and suppresses the degradation of robust performance during the saturation period. In previous studies, this problem has been considered to be equivalent to a static output feedback design problem, which is essentially a non-convex problem. We show that this problem can be reduced to an equivalent convex problem by using an appropriate sector transformation. The numerical solution can be obtained efficiently by solving linear matrix inequalities (LMIs). Further, a constant scaling matrix is introduced to the condition in order to reduce the conservativeness. In this case, since the design problem is no more LMIs, an algorithm for solving the problem by LMI iterations is presented. Four numerical examples are given to illustrate the effectiveness of the proposed method. Copyright © 2002 John Wiley & Sons, Ltd.
TL;DR: In this paper, a sliding mode feedback controller based on integral reconstructors is developed for the regulation of the "boost" DC-to-DC power converter circuit conduction in continuous conduction mode.
Abstract: A sliding mode feedback controller, based on integral reconstructors is developed for the regulation of the ‘boost’ DC-to-DC power converter circuit conduction in continuous conduction mode. The feedback control scheme uses only output capacitor voltage measurements, as well as knowledge of the available input signal, represented by the switch positions. The robustness of the feedback scheme is tested with abusively large, unmodelled, sudden load resistance variations. Copyright © 2002 John Wiley & Sons, Ltd.
TL;DR: In this article, the authors investigated the problem of ℋ∞ filtering for a class of uncertain Markovian jump linear systems, where the uncertainty is assumed to be norm-bounded and appears in all the matrices of the system state-space model, including the coefficients of the noise signals.
Abstract: This paper investigates the problem of ℋ∞ filtering for a class of uncertain Markovian jump linear systems. The uncertainty is assumed to be norm-bounded and appears in all the matrices of the system state-space model, including the coefficient matrices of the noise signals. It is also assumed that the jumping parameter is available. We develop a methodology for designing a Markovian jump linear filter that ensures a prescribed bound on the ℒ2-induced gain from the noise signals to the estimation error, irrespective of the uncertainty. The proposed design is given in terms of linear matrix inequalities. Copyright © 2002 John Wiley & Sons, Ltd.
TL;DR: This paper focuses on the analysis of a scheme for sensor failure, detection, identification and accommodation using experimental flight data of a research aircraft model using on‐line learning nonlinear neural approximators.
Abstract: This paper focuses on the analysis of a scheme for sensor failure, detection, identification and accommodation (SFDIA) using experimental flight data of a research aircraft model. Recent technical literature has shown the advantages of time-varying estimators and/or approximators. Conventional approaches are based on different versions of observers and Kalman filters while more recent methods are based on different approximators based on neural networks (NNs). The approach proposed in the paper is based on the use of on-line learning nonlinear neural approximators.
The characteristics of three different neural architectures were compared through different sensor failures. The first architecture is based on a multi layer perceptron (MLP) NN trained with the extended back propagation algorithm (EBPA). The second and third architectures are based on a radial basis function (RBF) NN trained with the minimal resource allocating network (MRAN) and extended-MRAN (EMRAN). The MRAN and EMRAN algorithms have recently been developed for RBF networks and have shown remarkable learning capabilities at a fraction of the memory requirements and computational effort typically associated with conventional RBF NNs.
The experimental data for this study are flight data acquired from the flight-testing of a th semi-scale B777 research model designed, built, and flown at West Virginia University (WVU). Copyright © 2002 John Wiley & Sons, Ltd.
TL;DR: In this paper, a hybrid of receding horizon techniques and control Lyapunov function (CLF)-based ideas is used for the control of an experimental tethered flying wing developed at Caltech.
Abstract: SUMMARY This paper deals with the application of receding horizon methods to hover and forward flight models of an experimental tethered flying wing developed at Caltech. The dynamics of the system are representative of a vertical landing and take off aircraft, such as a Harrier around hover, or a thrust-vectored aircraft such as F18-HARV or X-31 in forward flight. The adopted control methodology is a hybrid of receding horizon techniques and control Lyapunov function (CLF)-based ideas. First, a CLF is generated using quasi-LPV methods and then, by using the CLF as the terminal cost in the receding horizon optimization, stability is guaranteed. The main advantage of this approach is that stability can be guaranteed without imposing constraints in the on-line optimization, allowing the problem to be solved in a more efficient manner. Models of the experimental set-up are obtained for the hover and forward flight modes. Numerical simulations for different time horizons are presented to illustrate the effectiveness of the discussed methods. Specifically, it is shown that a mere upper bound on the cost-to-go is not an appropriate choice for a terminal cost, when the horizon length is short. Simulation results are presented using experimentally verified model parameters. Copyright # 2002 John Wiley & Sons, Ltd.
TL;DR: In this paper, a robust sampled-data control for a class of uncertain nonlinear systems with constraints on the output is developed, where the problem is formulated from vehicle steering control with constraint on the side slip angle of body.
Abstract: In industrial process control, computer control, which makes the closed-loop system a sampled-data one containing both continuous- and discrete-time signals, is widely used. In contrast with traditional approximation methods, sampled-data synthesis, a direct digital controller design procedure without approximation, has received increasing attention during the past few years. However, many of the existing results cannot be applied to sampled-data control design for the uncertain systems. In this paper, a result of robust asymptotic stability of sampled-data systems with constraints on the state is presented based on a result on practical stability for these systems. Then the robust sampled-data control for a class of uncertain nonlinear systems with constraints on the output is developed. The problem is formulated from vehicle steering control with constraint on the side slip angle of body. The result is described by some matrix inequalities which could be solved by an iterative algorithm based on the linear matrix inequality technique. Finally, a numerical example is presented to demonstrate the result. Copyright © 2002 John Wiley & Sons, Ltd.
TL;DR: In this paper, the robust fault detection filter design problem is considered as a scaledH∞ filtering problem, where the objective is to provide the smallest scaled L2 gain of the unknown input of the system that is guaranteed to be less than a prespecified level.
Abstract: The paper deals with the sensitivity optimization of detection filters in linear time-varying (LTV) systems which are subject to multiple simultaneous faults and disturbances. The robust fault detection filter design problem as a scaledH∞ filtering problem is considered. The effect of two different input scaling approaches to the optimization process is investigated. The objective is to provide the smallest scaled L2 gain of the unknown input of the system that is guaranteed to be less than a prespecified level, i.e., to produce a filter with optimal disturbance suppression capability in such a way that sufficient sensitivity to failure modes should still be maintained. It is shown how to obtain bounds on the scaled L2 gain by transforming the standard H∞ filtering problem into a convex feasibility problem, specifically, a structured, linear matrix inequality (LMI). Numerical examples demonstrating the effect of the scaled optimization with respect to conventional H∞ filtering is presented. Copyright © 2002 John Wiley & Sons, Ltd.
TL;DR: In this article, the authors show the utility of nonlinear state-dependent time-scaling to simplify the system dynamics, and consequently the controller design, in the context of the fourth order baker's yeast fed-batch fermentation process model.
Abstract: Even though the basic mechanisms of operation of reaction systems are relatively simple the dynamical models obtained from first principles are complex and contain highly uncertain terms. To develop reliable model-based controllers it is therefore necessary to simplify the system dynamics preserving the features which are essential for control. Towards this end, co-ordinate transformations identifying the states which are dependent/independent of the reactions and flows have been reported in the literature. This has allowed, for instance, the design of observers which are insensitive to the (usually unknown) reaction functions. The main contribution of this paper is to show the utility of nonlinear state-dependent time-scaling to simplify the system dynamics, and consequently the controller design. In particular, we show that with time-scaling and an input transformation we can reveal the existence of attractive invariant manifolds, which allows us to reduce the dimension of the system. As an application we study the well-known fourth order baker's yeast fed-batch fermentation process model, whose essential dynamics is captured by a planar system perturbed by an exponentially decaying term. We then exploit this particular structure to design, with reduced control authority, a nonlinear asymptotically stabilizing control law which is robust with respect to the reaction function. Copyright © 2001 John Wiley & Sons, Ltd.
TL;DR: In this article, the problem of residual generation for fault detection and isolation in the presence of measurement noise for a class of nonlinear systems is considered, and a method is proposed for the design of a filter which attenuates the effect of the measurement noise on the residual.
Abstract: The problem of residual generation for fault detection and isolation in the presence of measurement noise for a class of nonlinear systems is considered. Exploiting the tools and the results of the geometric approach to the problem of residual generation, a method is proposed for the design of a filter which attenuates the effect of the measurement noise on the residual in the case in which the effect of the fault on the residual is minimal.
TL;DR: In this article, a non-propulsive means for countering the differential orbital plane precession that is the major formation perturbation produced by oblateness is proposed, where the solar radiation pressure acting on a relatively small surface, termed a solar wing, is fixed to the satellite.
Abstract: One fundamental way in which satellite formation flight differs from conventional orbital proximity operations is that extended mission durations are required. Consequently, long-term perturbation effects, and in particular, those due to the oblateness of the Earth, must be corrected for if the formation is to persist. This paper considers a novel non-propulsive means for countering the differential orbital plane precession that is the major formation perturbation produced by oblateness. The approach taken is to make use of the solar radiation pressure acting on a relatively small surface, termed a solar wing, that is fixed to the satellite. The resulting torque causes the orbit to precess; if the wing is sized correctly, this motion will cancel, on average, with that due to oblateness, so maintaining the formation without use of propellant. It will be shown that the long-term orbital effects of the solar wing control input (the wing orientation angle) are highly nonlinear, and exhibit strong coupling between the orbital inclination and the longitude of the ascending node. Finally, numerical results are given to illustrate the approach. Copyright © 2002 John Wiley & Sons, Ltd.