Six-DOF spacecraft optimal trajectory planning and real-time attitude control: a deep neural network-based approach

This brief presents an integrated trajectory planning and attitude control framework for six-degree-of-freedom (6-DOF) hypersonic vehicle (HV) reentry flight. The proposed framework utilizes a bilevel structure incorporating desensitized trajectory optimization and deep neural network (DNN)-based control. In the upper level, a trajectory data set containing optimal system control and state trajectories is generated, while in the lower level control system, DNNs are constructed and trained using the pregenerated trajectory ensemble in order to represent the functional relationship between the optimized system states and controls. These well-trained networks are then used to produce optimal feedback actions online. A detailed simulation analysis was performed to validate the real-time applicability and the optimality of the designed bilevel framework. Moreover, a comparative analysis was also carried out between the proposed DNN-driven controller and other optimization-based techniques existing in related works. Our results verify the reliability of using the proposed bilevel design for the control of HV reentry flight in real time

Adaptive Flight Control Research at NASA

A broad overview of current adaptive flight control research efforts at NASA is presented, as well as some more detailed discussion of selected specific approaches. The stated objective of the Integrated Resilient Aircraft Control Project, one of NASA s Aviation Safety programs, is to advance the state-of-the-art of adaptive controls as a design option to provide enhanced stability and maneuverability margins for safe landing in the presence of adverse conditions such as actuator or sensor failures. Under this project, a number of adaptive control approaches are being pursued, including neural networks and multiple models. Validation of all the adaptive control approaches will use not only traditional methods such as simulation, wind tunnel testing and manned flight tests, but will be augmented with recently developed capabilities in unmanned flight testing

Observer-Based Nonlinear Dynamic Inversion Adaptive Control with State Constraints

Hypersonic vehicle research and development has grown recently in the aerospace industry due to the powerful potential of operating a vehicle that flies at substantially higher speeds than typical aircraft. From a guidance, navigation and control perspective, hypersonic vehicles are particularly interesting due both to inherent vehicle complexities as well as practical concerns that only arise at high Mach numbers. Challenges inherent to the vehicle include nonlinearities, a wide range of operating conditions, high elasticity, high temperatures and parametric uncertainty. Although these challenges have by no means fully been explored in the literature, in the realm of control theory, they are somewhat common. Hypersonic vehicle control is difficult however, because in addition to these more traditional complexities a control designer must also deal with problems very specific to flying at high speeds such as: inlet unstart, overcoming sensing deficiencies at high speeds and creating an implementable digital control framework for a plant with extremely fast dynamics. This dissertation develops three novel theoretical approaches for addressing these challenges through advances in the nonlinear dynamic inversion adaptive control technique. Although hypersonic vehicle control is the motivation and often the application that the control algorithms in this dissertation are tested on, several of the theoretical developments apply to a general class of nonlinear continuous time systems. First, in order to address the problem of inlet unstart, two state constraint mechanisms which integrate into the nonlinear dynamic inversion adaptive control framework are presented. These state constraining control laws require full state feedback and are capable of restricting the outputs of nonlinear systems containing parameter uncertainty to specific regions of the state-space. The first state constraint mechanism achieves this objective using sliding mode control and the second uses bounding functions to smoothly adjust the control and adaptive laws and drive the states toward the origin when constraints are approached. Stability is proven using Lyapunov analysis and these techniques are demonstrated in a nonlinear simulation of a hypersonic vehicle. Second, an observer-based feedback controller is developed that allows for a nonlinear system to track a reference trajectory with bounded errors and without measuring multiple states. Again, the technique used is nonlinear dynamic inversion adaptive control, but because of uncertainty in the system state, it is not assumed that the nonlinear control effectiveness matrix can be canceled perfectly. A nonlinear observer is implemented to estimate the values of the unknown states. This observer allows for the closed-loop stability of the system to be proven through Lyapunov analysis. It is shown that parametric uncertainty can successfully be accounted for using an adaptive mechanism and that all tracking and estimation errors are uniformly ultimately bounded. Finally, a sampled-data nonlinear dynamic inversion adaptive control architecture is introduced. Despite the prevalence of digital controllers in practice, a nonlinear dynamic inversion adaptive control scheme in a sampled-data setting has not previously been developed. The method presented in this dissertation has the capability of extending the benefits of nonlinear dynamic inversion adaptive control - robust control of nonlinear systems with respect to model uncertainty - to more practical platforms

Deterministic learning enhanced neutral network control of unmanned helicopter

In this article, a neural network-based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid without any concern on either initial conditions or range of state variables. In addition, deterministic learning is applied to the neutral network learning control, such that the adaptive neutral networks are able to store the learned knowledge that could be reused to construct neutral network controller with improved control performance. Simulation studies are carried out on a helicopter model to illustrate the effectiveness of the proposed control design

Adaptive Neural Back-Stepping Control with Constrains for a Flexible Air-Breathing Hypersonic Vehicle

The design of an adaptive neural back-stepping control for a flexible air-breathing hypersonic vehicle (AHV) in the presence of input constraint and aerodynamic uncertainty is discussed. Based on functional decomposition, the dynamics can be decomposed into the velocity subsystem and the altitude subsystem. To guarantee the exploited controller’s robustness with respect to parametric uncertainties, neural network (NN) is applied to approximate the lumped uncertainty of each subsystem of AHV model. The exceptional contribution is that novel auxiliary systems are introduced to compensate both the tracking errors and desired control laws, based on which the explored controller can still provide effective tracking of velocity and altitude commands when the actuators are saturated. Finally, simulation studies are made to illustrate the effectiveness of the proposed control approach in spite of the flexible effects, system uncertainties, and varying disturbances

Design Of An Adaptive Autopilot For An Expendable Launch Vehicle

This study investigates the use of a Model Reference Adaptive Control (MRAC) direct approach to solve the attitude control problem of an Expendable Launch Vehicle (ELV) during its boost phase of flight. The adaptive autopilot design is based on Lyapunov Stability Theory and provides a useful means for controlling the ELV in the presence of environmental and dynamical uncertainties. Several different basis functions are employed to approximate the nonlinear parametric uncertainties in the system dynamics. The control system is designed so that the desire dresponse to a reference model would be tracked by the closed-loop system. The reference model is obtained via the feedback linearization technique applied to the nonlinear ELV dynamics. The adaptive control method is then applied to a representative ELV longitudinal motion, specifically the 6th flight of Atlas-Centaur launch vehicle (AC-6) in 1965. The simulation results presented are compared to that of the actual AC-6 post-flight trajectory reconstruction. Recommendations are made for modification and future applications of the method for several other ELV dynamics issues, such as control saturation, engine inertia, flexible body dynamics, and sloshing of liquid fuels

Fault Diagnosis and Fault-Tolerant Control of Unmanned Aerial Vehicles

With the increasing demand for unmanned aerial vehicles (UAVs) in both military and civilian applications, critical safety issues need to be specially considered in order to make better and wider use of them. UAVs are usually employed to work in hazardous and complex environments, which may seriously threaten the safety and reliability of UAVs. Therefore, the safety and reliability of UAVs are becoming imperative for development of advanced intelligent control systems. The key challenge now is the lack of fully autonomous and reliable control techniques in face of different operation conditions and sophisticated environments. Further development of unmanned aerial vehicle (UAV) control systems is required to be reliable in the presence of system component faults and to be insensitive to model uncertainties and external environmental disturbances. This thesis research aims to design and develop novel control schemes for UAVs with consideration of all the factors that may threaten their safety and reliability. A novel adaptive sliding mode control (SMC) strategy is proposed to accommodate model uncertainties and actuator faults for an unmanned quadrotor helicopter. Compared with the existing adaptive SMC strategies in the literature, the proposed adaptive scheme can tolerate larger actuator faults without stimulating control chattering due to the use of adaptation parameters in both continuous and discontinuous control parts. Furthermore, a fuzzy logic-based boundary layer and a nonlinear disturbance observer are synthesized to further improve the capability of the designed control scheme for tolerating model uncertainties, actuator faults, and unknown external disturbances while preventing overestimation of the adaptive control parameters and suppressing the control chattering effect. Then, a cost-effective fault estimation scheme with a parallel bank of recurrent neural networks (RNNs) is proposed to accurately estimate actuator fault magnitude and an active fault-tolerant control (FTC) framework is established for a closed-loop quadrotor helicopter system. Finally, a reconfigurable control allocation approach is combined with adaptive SMC to achieve the capability of tolerating complete actuator failures with application to a modified octorotor helicopter. The significance of this proposed control scheme is that the stability of the closed-loop system is theoretically guaranteed in the presence of both single and simultaneous actuator faults

Adaptive control design with guaranteed margins for nonlinear plants

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.Includes bibliographical references (p. 139-142).Adaptive control is one of the technologies that improve both performance and safety as controller parameters can be redesigned autonomously in the presence of uncertainties. Considerable research has been accomplished in adaptive control theory for several decades and a solid foundation has been laid out for stability and robustness of adaptive systems. However, a large gap between theory and practice has been an obstacle to transition theoretical results into applications and it still remains. In order to reduce the gap, this thesis presents a unified framework for design and analysis of adaptive control for general nonlinear plants.An augmented adaptive control architecture is proposed where a nominal controller is designed in the inner-loop with an adaptive controller in the outer-loop. The architecture is completed by addressing three separate problems. The first problem is the design of adaptive control in the presence of input constraints. With a rigorous stability analysis, an algorithm is developed to remove the adverse effects of multi-input magnitude saturation. The second problem is the augmentation of adaptive control with a nominal gain-scheduling controller. Though adaptive controllers have been employed with gain-scheduling to various applications, no formal stability analysis has been developed. In the proposed architecture, adaptive control is combined with gain-scheduling in a specific manner while stability is guaranteed. The third problem is the development of analytic stability margins of the closed-loop plant with the proposed adaptive controller. A time-delay margin is derived using standard Lyapunov stability analysis as an analytic stability margin.The overall adaptive control architecture as well as the analytically derived margins are validated by a 6-DoF nonlinear flight dynamics based on the NASA X-15 hypersonic aircraft. Simulation results show that the augmented adaptive control is able to stabilize the plant and tracks desired trajectories with uncertainties in the plant while instability cannot be overcome only with the nominal controller. The time-delay margins are validated based on a generic transport model and they are compared with margins obtained from simulations studies. We utilize numerical methods to find less conservative time-delay margins.by Jinho Jang.Ph.D

Neural MRAC based on modified state observer

A new model reference adaptive control design method with guaranteed transient performance using neural networks is proposed in this thesis. With this method, stable tracking of a desired trajectory is realized for nonlinear system with uncertainty, and modified state observer structure is designed to enable desired transient performance with large adaptive gain and at the same time avoid high frequency oscillation. The neural network adaption rule is derived using Lyapunov theory, which guarantees stability of error dynamics and boundedness of neural network weights, and a soft switching sliding mode modification is added in order to adjust tracking error. The proposed method is tested by different theoretical application problems simulations, and also Caterpillar Electro-Hydraulic Test Bench experiments. Satisfying results show the potential of this approach --Abstract, page iv