A High-Order, Linear Time-Invariant Model for Application to Higher Harmonic Control and Flight Control System Interaction

This research describes a new methodology for the extraction of a high-order, linear time invariant model, which allows the periodicity of the helicopter response to be accurately captured. This model provides the needed level of dynamic fidelity to permit an analysis and optimization of the AFCS and HHC algorithms. The key results of this study indicate that the closed-loop HHC system has little influence on the AFCS or on the vehicle handling qualities, which indicates that the AFCS does not need modification to work with the HHC system. However, the results show that the vibration response to maneuvers must be considered during the HHC design process, and this leads to much higher required HHC loop crossover frequencies. This research also demonstrates that the transient vibration responses during maneuvers can be reduced by optimizing the closed-loop higher harmonic control algorithm using conventional control system analyses

Investigations of Model-Free Sliding Mode Control Algorithms including Application to Autonomous Quadrotor Flight

Sliding mode control is a robust nonlinear control algorithm that has been used to implement tracking controllers for unmanned aircraft systems that are robust to modeling uncertainty and exogenous disturbances, thereby providing excellent performance for autonomous operation. A significant advance in the application of sliding mode control for unmanned aircraft systems would be adaptation of a model-free sliding mode control algorithm, since the most complex and time-consuming aspect of implementation of sliding mode control is the derivation of the control law with incorporation of the system model, a process required to be performed for each individual application of sliding mode control. The performance of four different model-free sliding mode control algorithms was compared in simulation using a variety of aerial system models and real-world disturbances (e.g. the effects of discretization and state estimation). The two best performing algorithms were shown to exhibit very similar behavior. These two algorithms were implemented on a quadrotor (both in simulation and using real-world hardware) and the performance was compared to a traditional PID-based controller using the same state estimation algorithm and control setup. Simulation results show the model-free sliding mode control algorithms exhibit similar performance to PID controllers without the tedious tuning process. Comparison between the two model-free sliding mode control algorithms showed very similar performance as measured by the quadratic means of tracking errors. Flight testing showed that while a model-free sliding mode control algorithm is capable of controlling realworld hardware, further characterization and significant improvements are required before it is a viable alternative to conventional control algorithms. Large tracking errors were observed for both the model-free sliding mode control and PID based flight controllers and the performance was characterized as unacceptable for most applications. The poor performance of both controllers suggests tracking errors could be attributed to errors in state estimation, which effectively introduce unknown dynamics into the feedback loop. Further testing with improved state estimation would allow for more conclusions to be drawn about the performance characteristics of the model-free sliding mode control algorithms

Recent Advances in Robust Control

Robust control has been a topic of active research in the last three decades culminating in H_2/H_\infty and \mu design methods followed by research on parametric robustness, initially motivated by Kharitonov's theorem, the extension to non-linear time delay systems, and other more recent methods. The two volumes of Recent Advances in Robust Control give a selective overview of recent theoretical developments and present selected application examples. The volumes comprise 39 contributions covering various theoretical aspects as well as different application areas. The first volume covers selected problems in the theory of robust control and its application to robotic and electromechanical systems. The second volume is dedicated to special topics in robust control and problem specific solutions. Recent Advances in Robust Control will be a valuable reference for those interested in the recent theoretical advances and for researchers working in the broad field of robotics and mechatronics

Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms

This book is a reprint of the Special Issue “Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms”,which was published in Applied Sciences

UAV Model-based Flight Control with Artificial Neural Networks: A Survey

Model-Based Control (MBC) techniques have dominated flight controller designs for Unmanned Aerial Vehicles (UAVs). Despite their success, MBC-based designs rely heavily on the accuracy of the mathematical model of the real plant and they suffer from the explosion of complexity problem. These two challenges may be mitigated by Artificial Neural Networks (ANNs) that have been widely studied due to their unique features and advantages in system identification and controller design. Viewed from this perspective, this survey provides a comprehensive literature review on combined MBC-ANN techniques that are suitable for UAV flight control, i.e., low-level control. The objective is to pave the way and establish a foundation for efficient controller designs with performance guarantees. A reference template is used throughout the survey as a common basis for comparative studies to fairly determine capabilities and limitations of existing research. The end-result offers supported information for advantages, disadvantages and applicability of a family of relevant controllers to UAV prototypes

Nonlinear Regression Methods for Estimation

Regression techniques are developed for batch estimation and applied to three specific areas, namely, ballistic trajectory launch point estimation, adaptive flight control, and radio-frequency target triangulation. Specifically, linear regression with an intercept is considered in detail. An augmentation formulation is developed. Extensions of theory are applied to nonlinear regression as well. The intercept parameter estimate within the linear regression is used to identify the effects of trim change that are associated with the occurrence of a control surface failure. These estimates are used to adjust the inner loop control gains via a feed-forward command, hence providing an automatic reconfigurable retrim of an aircraft. The regression algorithms are used to consider reduced information applications, such as initial position target determination from bearings-only measurement data. In total, this dissertation develops algorithms for batch processes that broaden the envelope of successful estimation within the three aforementioned application areas. Additionally, the developed batch algorithms do not adversely impact the estimation ability in cases that are already estimated successfully by conventional approaches

Nonlinear Stabilization And Control Of Medium Range Surface To Air Interceptor Missiles

Nonlinear stabilization and control autopilots are capable of sustaining nominal performance throughout the entire fight envelope an interceptor missile may encounter during hostile engagements and require no gain scheduling to maintain autopilot stability. Due to non minimum phase conditions characteristic of tail controlled missile airframes, a separation of time scales within the dynamic equations of motion between rotational and translational differential equations was enforced to overcome unstable effects of non minimum phase. Dynamic inversion techniques are then applied to derive linearizing equations which, when injected forward into the plant result in a fully controllable linear system. Objectives of the two time scale control architecture are to stabilize vehicle rotational rates while at the same time controlling acceleration within the lateral plane of the vehicle under rapidly increasing dynamic pressure. Full 6 degree of freedom dynamic terms including all coriolis accelerations due to translational and rotational dynamic coupling have been taken into account in the inversion process. The result is a very stable, nonlinear autopilot with fixed control gains fully capable of stable nonlinear missile control. Several actuator systems were also designed to explore the destabilizing effects second order nonlinear actuator characteristics can have on nonlinear autopilot designs