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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.

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.