This Open Platform for Limit Protection guides the open design of maneuver limit protection systems in general, and manned, rotorcraft, aerospace applications in particular. The platform uses three stages of limit protection modules: limit cue creation, limit cue arbitration, and control system interface. A common set of limit cue modules provides commands that can include constraints, alerts, transfer functions, and friction. An arbitration module selects the best limit protection cues and distributes them to the most appropriate control path interface. This platform adopts a holistic approach to limit protection whereby it considers all potential interface points, including the pilots visual, aural, and tactile displays; and automatic command restraint shaping for autonomous limit protection. For each functional module, this thesis guides the control system designer through the design choices and information interfaces among the modules. Limit cue module design choices include type of prediction, prediction mechanism, method of critical control calculation, and type of limit cue. Special consideration is given to the nature of the limit, particularly the level of knowledge about it, and the ramifications for limit protection design, especially with respect to intelligent control methods such as fuzzy inference systems and neural networks. The Open Platform for Limit Protection reduces the effort required for initial limit protection design by defining a practical structure that still allows considerable design freedom. The platform reduces lifecycle effort through its open engineering systems approach of decoupled, modular design and standardized information interfaces. Using the Open Platform for Limit Protection, a carefree maneuver system is designed that addresses: main rotor blade stall as a steady-state limit; hub moment as a transient structural limit; and pilot induced oscillation as a controllability limit. The limit cue modules in this system make use of static neural networks, adaptive neural networks, and fuzzy inference systems to predict these limits. Visual (heads up display) and tactile (force-feedback) limit cues are employed. The carefree maneuver system is demonstrated in manned simulation using a General Helicopter (GENHEL) math model of the UH-60 Black Hawk, a projected, 53 degree field of view for the pilot, and a two-axis, active sidestick for cyclic control.

In this work we investigate the use of state-of-the-art tools for the regulation of complex, non-linear systems to improve the methodologies currently applied to trim comprehensive virtual prototypes of rotors and rotorcrafts. Among the several methods that have been proposed in the literature, the auto-pilot approach has the potential to solve trim problems efficiently even for the large and complex vehicle models of modern comprehensive finite element-based analysis codes. In this approach, the trim condition is obtained by adjusting the controls so as to virtually ``fly' the system to the final steady (periodic) flight condition. Published proportional auto-pilots show to work well in many practical instances. However, they cannot guarantee good performance and stability in all flight conditions of interest. Limit-cycle oscillations in control time histories are often observed in practice because of the non-linear nature of the problem and the difficulties in enforcing the constant-in-time condition for the controls. To address all the above areas of concern, in this research we propose a new auto-pilot, based on non-linear model-predictive control (NMPC). The formulation uses a non-linear reference model of the system augmented with an adaptive neural element, which identifies and corrects the mismatch between reduced model and controlled system. The methodology is tested on the wind-tunnel trim of a rotor multibody model and compared to an existing implementation of a classic auto-pilot. The proposed controller shows good performance without the need of a potentially very expensive tuning phase, which is required in classical auto-pilots. Moreover, model-predictive control provides a framework for guaranteeing stability of the non-linear closed-loop system, so it seems to be a viable approach for trimming complete rotorcraft comprehensive models in free-flight.

Thesis (MScEng)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: The landing phase of any helicopter is the most critical part of the whole flight envelope, particularly on a moving flight deck. The flight deck is usually located at the stern of the ship, translating to large heave motions. This thesis focuses on the three fundamental components required for a successful landing: accurate, relative state-estimation between the helicopter and the flight deck; a prediction horizon to forecast suitable landing opportunities; and excellent control to safely unite the helicopter with the flight deck. A monocular-vision sensor node was developed to provide accurate, relative position and attitude information of the flight deck. The flight deck is identified by a distinct, geometric pattern. The relative states are combined with the onboard, kinematic state-estimates of the helicopter to provide an inertial estimate of the flight deck states. Onboard motion prediction is executed to forecast a possible safe landing time which is conveyed to the landing controller. Camera pose-estimation tests and hardware-in-the-loop simulations proved the system developed in this thesis viable for flight tests. The practical flight tests confirmed the success of the monocular-vision sensor node. / AFRIKAANSE OPSOMMING: Die mees kritiese deel van die hele vlug-duurte van ’n helikopter is die landings-fase, veral op ’n bewegende vlugdek. Die vlugdek is gewoonlik geleë aan die agterstewe-kant van die skip wat groot afgee bewegings mee bring. Hierdie tesis ondersoek die drie fundamentele komponente van ’n suksesvolle landing: akkurate, relatiewe toestand-beraming tussen die helikopter en die vlugdek; ’n vooruitskatting horison om geskikte landings geleenthede te voorspel; en uitstekended beheer om die helikopter en vlugdek veilig te verenig. ’n Monokulêre-visie sensor-nodus was ontwikkel om akkurate, relatiewe-posisie en oriëntasie informasie van die vlugdek te verwerf. Die vlugdek is geidentifiseer deur ’n kenmerkende, geometriese patroon. Die relatiewe toestande word met die aan-boord kinematiese toestandafskatter van die helikopter gekombineer, om ’n beraming van die inertiale vlugdek-toestande te verskaf. Aan-boord beweging-vooruitskatting is uitgevoer om moontlike, veilige landingstyd te voorspel en word teruggevoer na die landingsbeheerder. Kamera-orientasie afskat-toetse en hardeware-in-die-lus simulasies het die ontwikkelde sisteem van hierdie tesis lewensvatbaar vir vlug-toetse bewys. Praktiese vlug-toetse het die sukses van die monokulêre-visie sensor-nodus bevestig.

Military helicopter operations require precise maneuverability characteristics for performance to be determined for the entire helicopter flight envelope. Historically, these maneuverability analyses are combinatorial in nature and involve human-interaction, which hinders their integration into conceptual design. A model formulation that includes the necessary quantitative measures and captures the impact of changing requirements real-time is presented. The formulation is shown to offer a more conservative estimate of maneuverability than traditional energy-based formulations through quantitative analysis of a typical pop-up maneuver. Although the control system design is not directly integrated, two control constraint measures are deemed essential in this work: control deflection rate and trajectory divergence rate. Both of these measures are general enough to be applied to any control architecture, while at the same time enable quantitative trades that relate overall vehicle maneuverability to control system requirements. The dimensionality issues stemming from the immense maneuver space are mitigated through systematic development of a maneuver taxonomy that enables the operational envelope to be decomposed into a minimal set of fundamental maneuvers. The taxonomy approach is applied to a helicopter canonical example that requires maneuverability and design to be assessed simultaneously. The end result is a methodology that enables the impact of design choices on maneuverability to be assessed for the entire helicopter operational envelope, while enabling constraints from control system design to be assessed real-time.

The present work focuses on the two areas of investigation: system identification of helicopter and design of controller for the helicopter. Helicopter system identification, the first subject of investigation in this thesis, can be described as the extraction of system characteristics/dynamics from measured flight test data. Wind tunnel experimental data suffers from scale effects and model deficiencies. The increasing need for accurate models for the design of high bandwidth control system for helicopters has initiated a renewed interest in and a more active use of system identification. Besides, system identification is likely to become mandatory in the future for model validation of ground based helicopter simulators. Such simulators require accurate models in order to be accepted by pilots and regulatory authorities like Federal Aviation Regulation for realistic complementary helicopter mission training. Two approaches are widely used for system identification, namely, black box and gray box approach. In the black-box approach, the relationship between input-output data is approximated using nonparametric methods such as neural networks and in such a case, internal details of the system and model structure may not be known. In the gray box approach, parameters are estimated after defining the model structure. In this thesis, both black box and gray box approaches are investigated. In the black box approach, in this thesis, a comparative study and analysis of different Recurrent Neural Networks(RNN) for the identification of helicopter dynamics using flight data is investigated. Three different RNN architectures namely, Nonlinear Auto Regressive eXogenous input(NARX) model, neural network with internal memory known as Memory Neuron Networks(MNN)and Recurrent MultiLayer perceptron (RMLP) networks are used to identify dynamics of the helicopter at various flight conditions. Based on the results, the practical utility, advantages and limitations of the three models are critically appraised and it is found that the NARX model is most suitable for the identification of helicopter dynamics. In the gray box approach, helicopter model parameters are estimated after defining the model structure. The identification process becomes more difficult as the number of degrees-of-freedom and model parameters increase. To avoid the drawbacks of conventional methods, neural network based techniques, called the delta method is investigated in this thesis. This method does not require initial estimates of the parameters and the parameters can be directly extracted from the flight data. The Radial Basis Function Network(RBFN)is used for the purpose of estimation of parameters. It is shown that RBFN is able to satisfactorily estimate stability and control derivatives using the delta method. The second area of investigation addressed in this thesis is the control of helicopter in flight. Helicopter requires use of a control system to achieve satisfactory flight. Designing a classical controller involves developing a nonlinear model of the helicopter and extracting linearized state space matrices from the nonlinear model at various flight conditions. After examining the stability characteristics of the helicopter, the desired response is obtained using a feedback control system. The scheduling of controller gains over the entire envelope is used to obtain the desired response. In the present work, a helicopter having a soft inplane four bladed hingeless main rotor and a four-bladed tail rotor with conventional mechanical controls is considered. For this helicopter, a mathematical model and also a model based on neural network (using flight data) has been developed. As a precursor, a feed back controller, the Stability Augmentation System(SAS), is designed using linear quadratic regulator control with full state feedback and LQR with out put feedback approaches. SAS is designed to meet the handling qualities specification known as Aeronautical Design Standard ADS-33E-PRF. The control gains have been tuned with respect to forward speed and gain scheduling has been arrived at. The SAS in the longitudinal axis meets the requirement of the Level1 handling quality specifications in hover and low speed as well as for forward speed flight conditions. The SAS in the lateral axis meets the requirement of the Level2 handling quality specifications in both hover and low speed as well as for forward speed flight conditions. Such conventional design of control has served useful purposes, however, it requires considerable flight testing which is time consuming, to demonstrate and tune these control law gains. In modern helicopters, the stringent requirements and non-linear maneuvers make the controller design further complicated. Hence, new design tools have to be explored to control such helicopters. Among the many approaches in adaptive control, neural networks present a potential alternative for modeling and control of nonlinear dynamical systems due to their approximating capabilities and inherent adaptive features. Furthermore, from a practical perspective, the massive parallelism and fast adaptability of neural network implementations provide more incentive for further investigation in problems involving dynamical systems with unknown non-linearity. Therefore, adaptive control approach based on neural networks is proposed in this thesis. A neural network based Feedback Error Neural adaptive Controller(FENC) is designed for a helicopter. The proposed controller scheme is based on feedback error learning strategy in which the outer loop neural controller enhances the inner loop conventional controller by compensating for unknown non-linearity and parameter un-certainties. Nonlinear Auto Regressive eXogenous input(NARX)neural network architecture is used to approximate the control law and the controller network parameters are adapted using updated rules Lyapunov synthesis. An offline (finite time interval)and on-line adaptation strategy is used to approximate system uncertainties. The results are validated using simulation studies on helicopter undergoing an agile maneuver. The study shows that the neuro-controller meets the requirements of ADS-33 handling quality specifications. Even though the tracking error is less in FENC scheme, the control effort required to follow the command is very high. To overcome these problems, a Direct Adaptive Neural Control(DANC)scheme to track the rate command signal is presented. The neural controller is designed to track rate command signal generated using the reference model. For the simulation study, a linearized helicopter model at different straight and level flight conditions is considered. A neural network with a linear filter architecture trained using back propagation through time is used to approximate the control law. The controller network parameters are adapted using updated rules Lyapunov synthesis. The off-line trained (for finite time interval)network provides the necessary stability and tracking performance. The on-line learning is used to adapt the network under varying flight conditions. The on-line learning ability is demonstrated through parameter uncertainties. The performance of the proposed direct adaptive neural controller is compared with feedback error learning neural controller. The performance of the controller has been validated at various flight conditions. The theoretical results are validated using simulation studies based on a nonlinear six degree-of-freedom helicopter undergoing an agile maneuver. Realistic gust and sensor noise are added to the system to study the disturbance rejection properties of the neural controllers. To investigate the on-line learning ability of the proposed neural controller, different fault scenarios representing large model error and control surface loss are considered. The performances of the proposed DANC scheme is compared with the FENC scheme. The study shows that the neuro-controller meets the requirements of ADS-33 handling quality specifications.

Carefree handling refers to the ability of a pilot to operate an aircraft without the need to continuously monitor aircraft operating limits. At the heart of all carefree handling or maneuvering systems, also referred to as envelope protection systems, are algorithms and methods for predicting future limit violations. Recently, envelope protection methods that have gained more acceptance, translate limit proximity information to its equivalent in the control channel. Envelope protection algorithms either use very small prediction horizon or are static methods with no capability to adapt to changes in system configurations. Adaptive approaches maximizing prediction horizon such as dynamic trim, are only applicable to steady-state-response critical limit parameters. In this thesis, a new adaptive envelope protection method is developed that is applicable to steady-state and transient response critical limit parameters. The approach is based upon devising the most aggressive optimal control profile to the limit boundary and using it to compute control limits. Pilot-in-the-loop evaluations of the proposed approach are conducted at the Georgia Tech Carefree Maneuver lab for transient longitudinal hub moment limit protection. Carefree maneuvering is the dual of carefree handling in the realm of autonomous Uninhabited Aerial Vehicles (UAVs). Designing a flight control system to fully and effectively utilize the operational flight envelope is very difficult. With the increasing role and demands for extreme maneuverability there is a need for developing envelope protection methods for autonomous UAVs. In this thesis, a full-authority automatic envelope protection method is proposed for limit protection in UAVs. The approach uses adaptive estimate of limit parameter dynamics and finite-time horizon predictions to detect impending limit boundary violations. Limit violations are prevented by treating the limit boundary as an obstacle and by correcting nominal control/command inputs to track a limit parameter safe-response profile near the limit boundary. The method is evaluated using software-in-the-loop and flight evaluations on the Georgia Tech unmanned rotorcraft platform- GTMax. The thesis also develops and evaluates an extension for calculating control margins based on restricting limit parameter response aggressiveness near the limit boundary.