Automated Carrier Landing of an Unmanned Combat Aerial Vehicle Using Dynamic Inversion

Dynamic Inversion (DI) is a powerful nonlinear control technique which has been applied to several modern flight control systems. This research utilized concepts of DI in order to develop a controller to land an Unmanned Combat Aerial Vehicle (UCAV) on an aircraft carrier. The Joint Unmanned Combat Air System (J-UCAS) Equivalent Model was used as the test aircraft. An inner-loop DI controller was developed to control the pitch, roll, and yaw rate dynamics of the aircraft, while an outer-loop DI controller was developed to provide flight path commands to the inner-loop. The controller design and simulation were conducted in the MATLAB/Simulink environment. Simulations were conducted for various starting positions near the carrier and for varying wind, wind turbulence, and sea state conditions. In the absence of wind and sea state turbulence, the controller performed well. After adding wind and sea state turbulence, the controller performance was degraded. Future work in this area should include a more robust disturbance rejection technique to compensate for wind turbulence effects and a method of carrier motion prediction to compensate for sea state effects

Observer-based engine air charge characterisation: rapid, observer-assisted engine air charge characterisation using a dynamic dual-ramp testing method

Characterisation of modern complex powertrains is a time consuming and expensive process. Little effort has been made to improve the efficiency of testing methodologies used to obtain data for this purpose. Steady-state engine testing is still regarded as the golden standard, where approximately 90% of testing time is wasted waiting for the engine to stabilize. Rapid dynamic engine testing, as a replacement for the conventional steady-state method, has the potential to significantly reduce the time required for characterisation. However, even by using state of the art measurement equipment, dynamic engine testing introduces the problem that certain variables are not directly measurable due to the excitation of the system dynamics. Consequently, it is necessary to develop methods that allow the observation of not directly measurable quantities during transient engine testing. Engine testing for the characterisation of the engine air-path is specifically affected by this problem since the air mass flow entering the cylinder is not directly measurable by any sensor during transient operation. This dissertation presents a comprehensive methodology for engine air charge characterisation using dynamic test data. An observer is developed, which allows observation of the actual air mass flow into the engine during transient operation. The observer is integrated into a dual-ramp testing procedure, which allows the elimination of unaccounted dynamic effects by averaging over the resulting hysteresis. A simulation study on a 1-D gas dynamic engine model investigates the accuracy of the developed methodology. The simulation results show a trade-off between time saving and accuracy. Experimental test result confirm a time saving of 95% compared to conventional steady-state testing and at least 65% compared to quasi steady-state testing while maintaining the accuracy and repeatability of conventional steady-state testing

Teaching data-driven control: from linear design to adaptive control with throttle valves

Electric throttle valves represent a challenge for control design, as their dynamics involve strong nonlinearities, characterized by an asymmetric hysteresis. Carrying experiments on multiple valves, a large variability in the characteristics of each valve and erratic steady-state behaviors can also be noticed, impairing classical model-based control strategies. Nevertheless, local data-driven linear models can be obtained and simple proportional-integral (PI) controllers, tuned individually for each valve with the appropriate data set, provide good tracking performance. As these controllers cannot be transposed from one valve to another, a robust strategy and an adaptive controller (using identification in closed-loop and controller re-design) may be necessary to propose a general method. This work aims at promoting control education on a simple yet challenging process, going from frequency analysis and linear design to an adaptive control method implemented with an online recursive algorithm.Comment: 12 page

Hybrid Electric Distributed Propulsion for Vertical Takeoff and Landing Aircraft

This research effort explores the interactions between aerodynamics and hybridelectric power system (HEPS) design and control for vertical takeoff and landing (VTOL) aircraft applications. Specifically, this research focuses on embedded distributed electric propulsion systems, for which the aerodynamic forces and moments are inextricably linked to power input. This effort begins by characterizing the performance of two similar embedded propulsion systems using computational fluid dynamics (CFD). From this initial analysis, a wind tunnel model is constructed and the systems are tested across the operating conditions required to characterize the performance of a VTOL aircraft, where 0 deg ≤ α ≤ 90 deg. One of these configurations is selected for evaluating the interaction with the hybrid-power system. An experimental HEPS is constructed based on a small two-stroke internal combustion engine as well, with a rated continuous power output of 2kW. This experiment is used to develop a validated dynamical HEPS model in MATLAB and Simulink, where the control systems are refined and the performance of the system is extended to accommodate the VTOL power demand during transitional flight. A robust control design is developed using a second order sliding mode controller (2-SMC), implemented using the super-twisting algorithm and integrated with classical linear control schemes in an interleaved-cascade architecture. The resulting system has a variable voltage output and a robust response to rapid changes in power demand. Additionally, the HEPS is also demonstrated to fully utilize the mechanical power output capability of the two-stroke engine. Ultimately, the HEPS is demonstrated, via the dynamical model, to be capable of supplying power for an embedded propulsion VTOL aircraft. This performance is further extended with the addition of an actively controlled slack bus, utilizing battery energy storage and a buck-converter integrated with the HEPS control system. In this configuration, the peak power demands of the system can exceed the maximum sustained power threshold (MSPT) of the HEPS

Modeling and active disturbance rejection control for sequential airdrop operations

With the assumption t at the motion acceleration of the cargo is unknown, the dynamic model that accords with the engineering practice of sequential cargo airdrop operations is derived by using the separation body method, which can describe the impact of the sequential moving cargos on the flight safety and airdrop-mission capacity. On this basis, a novel flight control method is designed based on the active disturbance rejection control (ADRC) theory. the system is decoupled and linearized through the nonlinear state error feedback; the total unknown disturbances, including unmolded dynamics and uncertainty, are estimated and compensated real-timely by the extended state observer. Moreover, with the consideration of the time-delay system, the ADRC is improved to enhance the accuracy and rapidity of the control system. Simulations are carried out under the condition that one transport aircraft performs sequential airdrop operations. The results verify that the desirable performance and robustness have been achieved and the proposed control method is quite competent for the sequential airdrop operations

Adaptive Model Predictive Control for Engine-Driven Ducted Fan Lift Systems using an Associated Linear Parameter Varying Model

Ducted fan lift systems (DFLSs) powered by two-stroke aviation piston engines present a challenging control problem due to their complex multivariable dynamics. Current controllers for these systems typically rely on proportional-integral algorithms combined with data tables, which rely on accurate models and are not adaptive to handle time-varying dynamics or system uncertainties. This paper proposes a novel adaptive model predictive control (AMPC) strategy with an associated linear parameter varying (LPV) model for controlling the engine-driven DFLS. This LPV model is derived from a global network model, which is trained off-line with data obtained from a general mean value engine model for two-stroke aviation engines. Different network models, including multi-layer perceptron, Elman, and radial basis function (RBF), are evaluated and compared in this study. The results demonstrate that the RBF model exhibits higher prediction accuracy and robustness in the DFLS application. Based on the trained RBF model, the proposed AMPC approach constructs an associated network that directly outputs the LPV model parameters as an adaptive, robust, and efficient prediction model. The efficiency of the proposed approach is demonstrated through numerical simulations of a vertical take-off thrust preparation process for the DFLS. The simulation results indicate that the proposed AMPC method can effectively control the DFLS thrust with a relative error below 3.5%

Performance Analysis Of Non-Linear Adaptive Control Laws Using Hardware in the Loop of an Unmanned Aerial System

In practical applications, an Unmanned Aerial System\u27s (UAS) baseline performance is dictated by how well it can follow a given trajectory with limited stress on the actuators. However, these can be insufficient performance metrics when the UAS is allowed to adapt to an unpredicted external influence such as turbulence or actuation failure, while maintaining a satisfactory baseline performance. In this thesis, different control laws based on the formation flight geometry problem, nonlinear dynamic inversion and an artificial immune system adaptive mechanism , are implemented in hardware-in-the-loop as a precursor for in-flight testing. These controllers are compared based on three performance metrics: trajectory following, control activity and computer task execution time. The controllers chosen for comparison are: Basic Proportional-Integral-Derivative (PID), Outer loop Non-Linear Dynamic Inversion (NLDI), Extended NLDI, and the previous three controllers augmented with an AIS for a total of six controllers. The Extended NLDI augmented with the AIS outperformed all of the other algorithms under falure conditions on a global scale

Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds

Autonomous formation flight is a key approach for reducing energy cost and managing traffic in future high density airspace. The use of Unmanned Aerial Vehicles (UAVs) has allowed low-budget and low-risk validation of autonomous formation flight concepts. This paper discusses the implementation and flight testing of nonlinear dynamic inversion (NLDI) controllers for close formation flight (CFF) using two distinct UAV platforms: a set of fixed wing aircraft named “Phastball” and a set of quadrotors named “NEO.” Experimental results show that autonomous CFF with approximately 5-wingspan separation is achievable with a pair of low-cost unmanned Phastball research aircraft. Simulations of the quadrotor flight also validate the design of the NLDI controller for the NEO quadrotors