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The baseline Jupiter Icy Moons Orbiter (JIMO) spacecraft presented challenging controls–structure interactions caused by having to accurately and simultaneously point the existing multiple articulated payloads (high-gain antenna, scan platform) which are mounted on a large flexible base. Sensitivity analyses and simulation studies were carried out using complex finite element models describing the vehicle's dynamics. The results of these studies indicate that: (1) the attitude controller bandwidth (BW) should be less than 0.01 Hz for stable pointing control at the end of a slew with fine thrusters; (2) to achieve a 90 ∘ slew maneuver in 2 h the attitude controller BW should be greater than 0.0001 Hz with coarse thrusters; and (3) the first natural frequency of the flexible vehicle should be in the range of 1 Hz, to avoid potential interactions with other flexible modes. A dissipative controller ensures the stability robustness during large-angle maneuvers of the articulated payloads at high speeds when linearity assumptions are likely to be incorrect and nonlinearity in dynamics may be significant. An advantage of the proposed controller is that the closed-loop stability is not only robust to unmodeled dynamics but also to actuator nonlinearities as expected in the proposed gimbals for JIMO.

This paper presents the development of a multiinput multioutput generalized predictive control (GPC) law and its application to reconfigurable control design in the event of actuator saturation. The stability of the GPC control law without reconfiguration is first established using an end-point state weighting. Based on the constrained nonlinear optimization, an end-point state weighting matrix synthesis method is derived. A novel reconfiguration strategy is developed for systems that have actuator redundancy and are faced with actuator saturation type failure. An elegant reconfigurable control design is presented with stability proof. A numerical simulation using a short-period approximation model of a civil transport aircraft is presented to demonstrate the reconfigurable control architecture.

This article presents experimental results of a transonic wind-tunnel test that demonstrates the use of generalized predictive control for flutter suppression for a subsonic wind-tunnel wing model. The generalized predictive control algorithm is based on the minimization of a suitable cost function over finite costing and control horizons. The cost function minimizes not only the sum of the mean square output of the plant predictions, but also the weighted square rate of change of the control input with its input constraints. An additional term was added to the cost function to compensate for dynamics of the wing model that cause it to be invariant to low input frequencies. This characteristic results in a control surface that drifts within the specified input constraints. The augmentation to the cost function that penalizes this low frequency drift is derived and demonstrated. The initial validation of the controller uses a linear plant predictor model for the computation of the control inputs. Simulation results of the closed-loop system that were used to determine nominal ranges for the tuning parameters are presented. The generalized predictive controller based on the linear predictor model successfully suppressed the flutter for all testable Mach numbers and dynamic pressures in the transonic region in both simulation and wind-tunnel testing. The results confirm that the generalized predictive controller is robust to modeling errors.

This paper examines the issues involved in controlling an air-breathing hypersonic vehicle (characterized by their unstable, non-minimum phase dynamics) in the presence of significant modeling uncertainty and nonlinearities (control saturations). Modeling of the vehicle exhaust (plume) is complicated, often requiring computational fluid dynamic (CFD) simulations to capture all relevant effects. The focus of this paper is on obtaining a control law that maintains the vehicle trajectory within an acceptable tube while enforcing control constraints in the presence of modeling uncertainty. A robust domain of attraction based approach is used to generate/validate a feasible tube. The computational aspects of such an approach is examined, and the benefits of a decentralized control technique is considered. This approach is compared with other techniques such as linear matrix inequalities based controller design. These approaches are applied to a command following scenario in order to illustrate the performance of the proposed approach.

In this paper, an overview is provided of control efforts implemented using the scramjetpowered hypersonic vehicle model developed by Bolender, Doman, et.al. (2005-2009). The nonlinear model is characterized by unstable non-minimum phase dynamics with aeroelastic-propulsion coupling and nonlinear propulsion constraints. The viability of simple inner-outer loop single-input single-output (SISO) control structures is examined and quantified. Reference command and control amplitude and bandwidth tradeoffs are addressed. A simple (non-scheduled ) inner-outer loop control law is shown to yield excellent tracking of a constant (q = 2076 psf) dynamic pressure guidance command profile from Mach 5.7 to Mach 12. Robustness, gain scheduling as well as multivariable control issues are also addressed.

In this paper, we examine the control of a scramjet-powered hypersonic vehicle with significant aero-elastic-propulsion interactions. Such vehicles are characterized by open loop unstable non-minimum phase dynamics, low frequency aero-elastic modes, significant coupling, and hard constraints (e.g. control surface deflection limits, thrust margin). Within this paper, attention is placed on maintaining acceptable closed loop performance (i.e. tracking of speed and flight path angle commands) while satisfying hard control surface deflection constraints as well as stoichiometrically normalized fuel-equivalency-ratio (FER) margin constraints. Control surface constraints are a consequence of maximum permissible aerodynamic loading. FER margin constraints are a consequence of thermal choking (i.e. unity combustor exit Mach number) and the fact that thrust loss may not be captured for FER greater than unity. Such limits are particularly important since the vehicle is open loop unstable and “saturation” can result in instability. To address these issues, one can design conservative (i.e. less aggressive or lower bandwidth) controllers that maintain operation below saturation levels for anticipated reference commands (and disturbances). Doing so, however, unnecessarily sacrifices performance - particularly when small reference commands are issued. Within this paper, the above issues are addressed using generalized predictive control (GPC). A 3DOF longitudinal model for a generic hypersonic vehicle, which includes aero-elastic-propulsion interactions, is used to illustrate the ideas.

In this paper, we provide an overview of scramjet-powered hypersonic vehicle modeling and control challenges. Such vehicles are characterized by unstable non-minimum phase dynamics with significant coupling and low thrust or FER (normalized fuel equivalency ratio) margins. Recent trends in hypersonic vehicle research is summarized. To illustrate control system design issues and tradeoffs, a generic nonlinear 3DOF longitudinal dynamics model capturing aero-elastic-propulsive interactions for wedge-shaped vehicle is used. The model is analyzed over a broad range of hypersonic flight conditions (i.e. operating points). The paper highlights how vehicle level-flight static (trim) and dynamic properties change over the trimmable air-breathing corridor (∼ Mach 4.75-12.6, 70-115 kft). Particular attention is paid to thermal choking constraints imposed on control system design as a direct consequence of having a finite FER margin. The dependence of FER margin on altitude, Mach, and the bow flow turning angle is discussed. The latter depends on Mach, altitude, angle-of-attack (AOA), and forebody flexing. It is (briefly) discussed how FER margin can be estimated on the basis of Mach, altitude, and AOA if a flexing upper bound is assumed. The implication of this state-dependent nonlinear FER margin constraint as well as that of the right half plane (RHP) zero, associated with the elevator-flight path angle (FPA) map, on control system bandwidth (BW) and FPA tracking are discussed. It is argued that while the non-minimum phase zero limits the achievable closed loop FPA BW, FER coupling into FPA can be used to address this. This, however, is limited by FER margin limits and may impose constraints on the size of the FPA (and velocity) commands that can be followed. This is particularly important because the vehicle is inherently unstable which implies a closed loop system (with a finite downward gain margin) that can become destabilized if driven sufficiently deep into control saturation. A consequence of this is that designers must take note of the fact that FPA commands which are sufficiently large and/or rapid may be impossible to follow with the desired level of fidelity. This is quantified within the paper. Speed command following issues are also discussed.

This paper presents multi-input multi-output (MIMO) generalized predictive control (GPC) law and its application to reconfigurable control. A method to compute the desired end-point state from the desired output and end-point state weighting matrix is given. In particular, an application to flight control with actuator saturation failure is presented. A numerical simulation using a short-period approximation model of a civil transport aircraft is presented to demonstrate the reconfigurable control architecture.

The objective of this paper is to report the preliminary results from the research being conducted in reconfigurable flight control. This paper highlights the Neural Generalized Predictive Control algorithm, which is capable of real-time control law reconfiguration, model adaptation, and the ability to identify failures in control effectiveness. This paper presents results for a full mission six degree-of-freedom conceptual commercial transport aircraft simulation where the elevator is frozen during the flight and the algorithm reconfigures to use symmetric aileron deflections to control pitch rate, thereby stabilizing the aircraft. The neural network is activated to learn the changed dynamics of having frozen elevators and performance is improved.

The paper demonstrates the feasibility of a nonlinear generalized predictive control (GPC) algorithm by showing real-time adaptive control on a plant with relatively fast time-constants. GPC has classically been used in process control where linear control laws were formulated for plants with relatively slow time-constants. The plant of interest for this paper is a magnetic levitation device that is nonlinear and open-loop unstable. In this application, the reference model of the plant is a neural network that has an embedded nominal linear model in the network weights. The control based on the linear model provides initial stability at the beginning of network training. By using a neural network the control laws are nonlinear and online adaptation of the model is possible to capture unmodeled or time-varying dynamics. Newton-Raphson is the minimization algorithm which requires the calculation of the Hessian, but even with this computational expense the low iteration rate make this a viable algorithm for real-time control.