Helicopters are often used to transport supplies and equipment. When a heavy load is carried via suspension cables below a helicopter, the load oscillates in response to helicopter motion and disturbance forces, such as wind. This oscillation is dangerous and adversely affects control of the helicopter, especially when carrying large or heavy loads. By adding input shaping to the helicopter's flight controller, the suspended load oscillation caused by helicopter motion is greatly reduced. A significant benefit of this approach is that it does not require measurement of the load position. This thesis contains derivations and analysis of simple planar helicopter-load dynamic models, and these models are verified using experimental data from model-scale, radio-controlled helicopters. The effectiveness of input shaping at eliminating suspended load oscillation is then demonstrated on this experimental hardware. In addition, the design of an attitude command, near-hover flight controller that combines input shaping and a common flight control architecture is illustrated using dynamic models of a Sikorsky S-61 helicopter, and simulation results are shown for example lateral and longitudinal repositioning movements. Results show that applying input shaping to simulated pilot commands greatly improves performance when carrying a suspended load.

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.

The field of guided projectile research has continually grown over the past several decades. Guided projectiles, typically encompassing bullets, mortars, and artillery shells, incorporate some sort of guidance and control mechanism to generate trajectory alterations. This serves to increase accuracy and decrease collateral damage. Control mechanisms for smart weapons must be able to withstand extreme acceleration loads at launch while remain simple for cost and reliability reasons. One type of control mechanism utilizes controllable internal translating masses (ITM's) that oscillate within the projectile to generate control forces. Several techniques for using internal translating masses for smart weapon flight control purposes are explored here. Specifically, the use of ITM's as direct control mechanisms, as a means to increase control authority, and as a means to protect the smart weapons sensor suite are examined. It is first shown that oscillating a mass orthogonal to the projectile axis of symmetry generates reasonable control force in statically-stable rounds. Trade studies examine the impact of mass size, mass offset from the center of gravity, and reductions in static stability on control authority. Then, the topic of static margin control through mass center modification is explored. This is accomplished by translating a mass in flight along the projectile axis of symmetry. Results show that this system allows for greater control authority and reduced throw-off error at launch. Another study, aimed at examining shock reduction potential at launch rather than static margin alteration, also considers ITM movement along the projectile centerline. In these studies, the ITM is comprised of sensitive electronic sensors, and is configured as a first-order damper during launch. Trade study results show that although the mechanism cannot substantially reduce the magnitude of launch loads, it is successful at dampening harmful structural vibrations typically experienced after muzzle exit. Finally, an active control system is developed for the ITM control mechanism using sliding mode methodology. Example cases and Monte Carlo simulations incorporating model uncertainties and sensor errors show that ITM control of projectiles can substantially reduce dispersion error. Furthermore, the novel sliding mode control law is shown to be highly robust to feedback disturbances. In a final study, combined ITM-canard control of projectiles is explored, concluding that ITM mechanisms can serve as a useful supplement in increasing the efficiency of currently-deployed control mechanisms.

This dissertation seeks to merge the ideas from robust control theory such as H-Infinity control design and the Small Gain Theorem, L stability theory and Lyapunov stability from nonlinear control, and recent theoretical achievements in adaptive control. The fusion of frequency domain and linear time domain ideas allows the derivation of an H-Infinity Norm Minimization Approach (H-Infinity-NMA) for adaptive control architecture that permits a control designer to simplify the adaptive tuning process and tune the uncertainty compensation characteristics via linear control design techniques, band limit the adaptive control signal, efficiently handle redundant actuators, and handle unmatched uncertainty and matched uncertainty in a single design framework. The two stage design framework is similar to that used in robust control, but without sacrificing performance. The first stage of the design considers an ideal system with the system uncertainty completely known. For this system, a control law is designed using linear H-Infinity theory. Then in the second stage, an adaptive process is implemented that emulates the behavior of the ideal system. If the linear H-Infinity design is applied to control the emulated system, it then guarantees closed loop system stability of the actual system. All of this is accomplished while providing notions of transient performance bounds between the ideal system and the true system. Extensions to the theory include architectures for a class of output feedback systems, limiting the authority of an adaptive control system, and a method for improving the performance of an adaptive system with slow dynamics without any modification terms. Applications focus on using aerodynamic flow control for aircraft flight control and the Crew Launch Vehicle.

Model Reference Adaptive Control (MRAC) is a widely studied adaptive control methodology that aims to ensure that a nonlinear plant with significant modeling uncertainty behaves like a chosen reference model. MRAC methods attempt to achieve this by representing the modeling uncertainty as a weighted combination of known nonlinear functions, and using a weight update law that ensures weights take on values such that the effect of the uncertainty is mitigated. If the adaptive weights do arrive at an ideal value that best represent the uncertainty, significant performance and robustness gains can be realized. However, most MRAC adaptive laws use only instantaneous data for adaptation and can only guarantee that the weights arrive at these ideal values if and only if the plant states are Persistently Exciting (PE). The condition on PE reference input is restrictive and often infeasible to implement or monitor online. Consequently, parameter convergence cannot be guaranteed in practice for many adaptive control applications. Hence it is often observed that traditional adaptive controllers do not exhibit long-term-learning and global uncertainty parametrization. That is, they exhibit little performance gain even when the system tracks a repeated command. This thesis presents a novel approach to adaptive control that relies on using current and recorded data concurrently for adaptation. The thesis shows that for a concurrent learning adaptive controller, a verifiable condition on the linear independence of the recorded data is sufficient to guarantee that weights arrive at their ideal values even when the system states are not PE. The thesis also shows that the same condition can guarantee exponential tracking error and weight error convergence to zero, thereby allowing the adaptive controller to recover the desired transient response and robustness properties of the chosen reference models and to exhibit long-term-learning. This condition is found to be less restrictive and easier to verify online than the condition on persistently exciting exogenous input required by traditional adaptive laws that use only instantaneous data for adaptation. The concept is explored for several adaptive control architectures, including neuro-adaptive flight control, where a neural network is used as the adaptive element. The performance gains are justified theoretically using Lyapunov based arguments, and demonstrated experimentally through flight-testing on Unmanned Aerial Systems.

Engineering design may be viewed as a decision making process that supports design tradeoffs. The designer makes decisions based on information available and engineering judgment. The designer determines the direction in which the design must proceed, the procedures that need to be adopted, and develops a strategy to perform successive decisions. The design is only as good as the decisions made, which is in turn dependent on the information available. Information is time and process dependent. This thesis work focuses on developing a coherent bottom-up framework and methodology to improve information transfer and decision making while designing complex systems. The rotorcraft drive system is used as a test system for this methodology. The traditional serial design approach required the information from one discipline and/or process in order to proceed with the subsequent design phase. The Systems Engineering (SE) implementation of Concurrent Engineering (CE) and Integrated Product and Process Development (IPPD) processes tries to alleviate this problem by allowing design processes to be performed in parallel and collaboratively. The biggest challenge in implementing Concurrent Engineering is the availability of information when dealing with complex systems such as aerospace systems. The information is often incomplete, with large amounts of uncertainties around the requirements, constraints and system objectives. As complexity increases, the design process starts trending back towards a serial design approach. The gap in information can be overcome by either “softening” the requirements to be adaptable to variation in information or to delay the decision. Delayed decisions lead to expensive modifications and longer product design lifecycle. Digitization of IPPD tools for complex system enables the system to be more adaptable to changing requirements. Design can proceed with “soft” information and decisions adapted as information becomes available even at early stages. The advent of modern day computing has made digitization and automation possible and feasible in engineering. Automation has demonstrated superior capability in design cycle efficiency [1]. When a digitized framework is enhanced through automation, design can be made adaptable without the requirement for human interaction. This can increase productivity, and reduce design time and associated cost. An important aspect in making digitization feasible is having the availability of parameterized Computer Aided Design (CAD) geometry [2]. The CAD geometry gives the design a physical form that can interact with other disciplines and geometries. Central common CAD database allows other disciplines to access information and extract requirements; this feature is of immense importance while performing systems syntheses. Through database management using a Product Lifecycle Management (PLM) system, Integrated Product Teams (IPTs) can exchange information between disciplines and develop new designs more efficiently by collaborating more and from far [3]. This thesis focuses on the challenges associated with automation and digitization of design. Making more information available earlier goes jointly with making the design adaptable to new information. Using digitized sizing, synthesis, cost analysis and integration, the drive system design is brought in to early design. With modularity as the objective, information transfer is made streamlined through the use of a software integration suite. Using parametric CAD tools, a novel ‘Fully-Relational Design’ framework is developed where geometry and design are adaptable to related geometry and requirement changes. During conceptual and preliminary design stages, the airframe goes through many stages of modifications and refinement; these changes affect the sub-system requirements and its design optimum. A fully-relational design framework takes this into account to create interfaces between disciplines. A novel aspect of the fully-relational design methodology is to include geometry, spacing and volume requirements in the system design process. Enabling fully-relational design has certain challenges, requiring suitable optimization and analysis automation. Also it is important to ensure that the process does not get overly complicated. So the method is required to possess the capability to intelligently propagate change. There is a need for suitable optimization techniques to approach gear train type design problems, where the design variables are discrete in nature and the values a variables can assume is a result of cascading effects of other variables. A heuristic optimization method is developed to analyze this multimodal problem. Experiments are setup to study constraint dependencies, constraint-handling penalty methods, algorithm tuning factors and innovative techniques to improve the performance of the algorithm. Inclusion of higher fidelity analysis in early design is an important element of this research. Higher fidelity analyses such as nonlinear contact Finite Element Analysis (FEA) are useful in defining true implied stresses and developing rating modification factors. The use of Topology Optimization (TO) using Finite Element Methods (FEM) is proposed here to study excess material removal in the gear web region.

Commercial Aircraft Corporation of China, Ltd (COMAC)and Chinese Scholarship Council. / The aim of this thesis is to develop a real time PIO detection and mitigation system that consists of a detector based on short time Fourier transform(STFT) and autoregressive model(ARX) with exogenous inputs, together with an adaptive controller based mitigation system. The system not only detects the traditional PIO characteristics but also focuses on the trend of pilot behaviour by calculating the rate of change in the open loop crossover frequency. In the detection system, a sliding windowed STFT method was applied to identify the frequency and phase characteristics of the system via processing the signal of pilot input and aircraft state. An ARX model was also applied to get the rate of change of the crossover frequency. After detection, a PIO cue was shown on the primary flight display. A scheduled gain controller was coupled to provide PIO mitigation by varying stick input gain. Compensatory and tracking tests for the evaluation of this system were performed using a quasi-linear Boeing-747 aircraft model including nonlinear command gearing and actuator rate-limiting. Bandwidth and Gibson criteria were used to design PIO prone control laws for system evaluation experiments. Results from PIO tests conducted on desktop PCs were presented. These were analyzed and compared with those obtained from implementing the Real-time Oscillation Verifier module available in literature.

The work in this dissertation comprises aeroservoelastic simulation development, two modal filter design case studies and theoretical improvement of the modal filter. The modal filter is made robust to sensor bias. Studies have shown that the states estimated by the modal filter can be integrated into active structural control. The integration of modal filters into aircraft structural control systems is explored. Modal filters require distributed sensing to achieve accurate modal coordinate estimates. Distributed sensing technology has progressed to the point, where it is being tested on aircraft such as Ikhana and the upcoming X-56A. Previously, the modal filter was criticized for requiring too many sensors. It was never assessed for its potential benefits in aircraft control. Therefore it is of practical interest to reinvestigate the modal filter. The first case study shows that under conditions of sensor normality, the modal filter is a Gaussian efficient estimator in an aeroservoelastic environment. This is a fundamental experiment considering the fact that the modal filter has never been tested in the airflow. To perform this case study a linear aeroservoelastic code capable of modeling distributed sensing is developed and experimentally validated. From this code, a computational wing model is fitted with distributed sensing. A modal filtering design methodology is developed and applied. With distributed sensing and modal filtering feedback control is achieved. This is also compared and contrasted with a controller using state-of-the-art accelerometers. In addition, new methods of active shape control are introduced for warping an aeroelastic structure utilizing the modal filter and control surfaces. The next case study takes place in a realistic setting for an aircraft. Flexible aircraft bring challenges to the active control community. Increased gust loads, possibility of flutter, and off-design drag may detrimentally affect performance and safety. Aeroservoelastic tailoring, gust load alleviation (GLA) and active flutter suppression (AFS) may be required on future flexible air vehicles. It is found that modal filters can theoretically support these systems. The aircraft case study identifies additional steps required in the modal filtering design methodology. Distributed sensing, the modal filter and modal reference shape control are demonstrated on the X-56A flutter-unstable simulation model. It is shown that control of deformations at potentially millions of points on an aircraft vehicle can be achieved through control of a few modal coordinates. Finally modal filter robustness is theoretically improved and computationally verified. State-of-the-art modal filters have high bias sensitivity. In fact, this is so critical that state-of-the-art modal filters may never be certified for aircraft implementation. This is especially true within a flight critical control system. The solution to this problem is found through derivation of the robust modal filter. The filter combines good properties of concentration algorithms with robust re-descending M-estimation. A new trim criterion specific to the strain based modal sensing system is derived making the filter robust to asymmetric or leverage point outliers. Robust starts are introduced to improve convergence of the modal estimation system to the globally optimal solution in the presence of 100s of biased fiber optic sensors.

This research work provides the development of an Adaptive Flight Control System (AFCS) for autonomous hover of a Rotary-wing Unmanned Aerial Vehicle (RUAV). Due to the complex, nonlinear and time-varying dynamics of the RUAV, indirect adaptive control using the Model Predictive Control (MPC) is utilised. The performance of the MPC mainly depends on the model of the RUAV used for predicting the future behaviour. Due to the complexities associated with the RUAV dynamics, a neural network based black box identification technique is used for modelling the behaviour of the RUAV. Auto-regressive neural network architecture is developed for offline and online modelling purposes. A hybrid modelling technique that exploits the advantages of both the offline and the online models is proposed. In the hybrid modelling technique, the predictions from the offline trained model are corrected by using the error predictions from the online model at every sample time. To reduce the computational time for training the neural networks, a principal component analysis based algorithm that reduces the dimension of the input training data is also proposed. This approach is shown to reduce the computational time significantly. These identification techniques are validated in numerical simulations before flight testing in the Eagle and RMAX helicopter platforms. Using the successfully validated models of the RUAVs, Neural Network based Model Predictive Controller (NN-MPC) is developed taking into account the non-linearity of the RUAVs and constraints into consideration. The parameters of the MPC are chosen to satisfy the performance requirements imposed on the flight controller. The optimisation problem is solved numerically using nonlinear optimisation techniques. The performance of the controller is extensively validated using numerical simulation models before flight testing. The effects of actuator and sensor delays and noises along with the wind gusts are taken into account during these numerical simulations. In addition, the robustness of the controller is validated numerically for possible parameter variations. The numerical simulation results are compared with a base-line PID controller. Finally, the NN-MPCs are flight tested for height control and autonomous hover. For these, SISO as well as multiple SISO controllers are used. The flight tests are conducted in varying weather conditions to validate the utility of the control technique. The NN-MPC in conjunction with the proposed hybrid modelling technique is shown to handle additional disturbances successfully. Extensive flight test results provide justification for the use of the NN-MPC technique as a reliable technique for control of non-linear complex dynamic systems such as RUAVs.