Extended Nonlinear Dynamic Inversion Control Laws for Unmanned Air Vehicles

This paper presents Hardware-in-the-Loop (HIL) simulation results of a novel configuration for guidance and tracking control laws for unmanned air vehicles (UAV). The proposed adaptive system is based on an extended non-linear dynamic inversion (NLDI) approach augmented with an artificial immune system mechanism that relies on a direct compensation inspired primarily by the biological immune system response. The implementation of the control laws is illustrated through HIL simulation using a mathematical model of an UAV research platform developed at Embry-Riddle Aeronautical University (ERAU) to support the design, testing and validation of bio-inspired fault tolerant adaptive control algorithms. The main objective of the control laws is to minimize forward, lateral, and vertical distances with respect to a desired trajectory, and maintain stability and adequate performance in the presence of sub-system failures. The performance of the control laws is evaluated during autonomous flight in terms of trajectory tracking errors, real-time execution on board the flight computer, and control activity at nominal and abnormal conditions. The results obtained with the ERAU-UAV HIL environment show that for all cases investigated the extended NLDI approach augmented with the immunity-based mechanism has desirable fault tolerant capabilities and is reliable for in-flight testing operation as a next step towards the validation and verification of this adaptive configuration.

This paper presents an advanced single network adaptive critic (SNAC) aided nonlinear dynamic inversion (NDI) approach for simultaneous attitude control and trajectory tracking of a micro-quadrotor. Control of micro-quadrotors is a challenging problem due to its small size, strong coupling in pitch-yaw-roll and aerodynamic effects that often need to be ignored in the control design process to avoid mathematical complexities. In the proposed SNAC aided NDI approach, the gains of the dynamic inversion design are selected in such a way that the resulting controller behaves closely to a pre-synthesized SNAC controller for the output regulation problem. However, since SNAC is based on optimal control theory, it makes the dynamic inversion controller to operate near optimal and enhances its robustness property as well. More important, it retains two major benefits of dynamic inversion: (i) closed form expression of the controller and (ii) easy scalability to command tracking application even without any apriori knowledge of the reference command. Effectiveness of the proposed controller is demonstrated from six degree-of-freedom simulation studies of a micro-quadrotor. It has also been observed that the proposed SNAC aided NDI approach is more robust to modeling inaccuracies, as compared to the NDI controller designed independently from time domain specifications.

Combining Homogeneous High Order Sliding Mode and Nonlinear Dynamic Inversion for fixed wing aircraft attitude and speed control

High performance aircraft of the future will be designed to be lighter, more maneuverable, and operate over an ever expanding flight envelope. This set of conditions will necessarily mean highly flexible vehicles operating in nonlinear regimes. A methodology proposed to better optimize their responses to both pilot input and external disturbances, as well as to decrease the cost of vehicle design is the novel dynamic inversion. The attractiveness of this methodology lies in the fact that the inherent nonlinearities of the problem and the coupled nature of flexible dynamics are explicitly considered. The contribution of this work to the state of the art is predicated on the development and application of the novel dynamic inversion methodology to handle highly flexible aircraft in an integrated flight/structural mode control manner. The unprecedented small separation between rigid body and flexible dynamics as well as the reciprocal interaction between them due to flight control action are the key elements of the aircraft model. The novel approach to the nonlinear dynamic inversion allows the methodology to more intelligently handle flexible dynamics in the context of the dual objectives of integrated flight/SMC control by altering flexible mode damping without cancellation; thus, improving disturbance response and avoiding the potentially destabilizing effect of pole cancellation close to the j-omega-axis in case of modeling uncertainty. The necessary level of model complexity for design has been established with particular attention given to understanding physics. The effect of uncertainty in the structural mode dynamics has been addressed. Further contribution of this work addresses the issue of stability of the dynamic systems driven by nonlinear controllers. One result shows how assessing stability of an n-dimensional system can be reduced to checking stability of a two-dimensional one using algebraic expressions that are based on the vehicle characteristics such as aerodynamic coefficients. This reduces a complicated dynamical problem to something purely algebraic and manageably complex. Another approach is based on algorithmically finding a local Lyapunov function using sum of squares. The presented results are the first to address the question of stability for the nonlinear dynamic inversion in the presence of flexible dynamics.

This paper describes a dual-loop control scheme for fault tolerant flight control system design. The dual-loop controller consists of an outer loop controller–so-called adaptive neural sliding mode control (ANSC) and an inner loop controller designed by using nonlinear dynamic inversion (NDI) technique. The merits of adaptive neural network and sliding mode control scheme are that 1) the ability of adaptive neural network control to deal with unstructured uncertainty and 2) the ability of sliding mode control to guarantee transient response. Using timescale separation principal, the aircraft dynamics can be decomposed into fast and slow dynamics and the decomposed dynamics are inversed for NDI controllers. For real-time pilot simulation, one-stage inverse dynamics is used and the pilot inputs are translated to roll, pitch and yaw rate commands. For cascade NDI, two-stage dynamic inversion is used. The stability analysis of the proposed controller is performed using Lyapunov theory. To verify the effectiveness of the proposed control scheme, numerical simulation is performed for six degree-of-freedom nonlinear aircraft model while a failure occurs in longitudinal control surface. Simulation results demonstrate that closedloop system has good performance while encountering lock-in-place, partial destruction and floating actuator failures. Nomenclature stick long δ , stick lat δ , stick dir δ = pilot inputs for longitudinal, lateral and directional command dir lon lat K K K , , = sticks and pedal gains ref p , ref q , ref r = reference model rate commands cmd z n , cmd y n = normal and lateral acceleration command

This paper focuses on the design of a disturbance rejection controller for a tailless aircraft based on the technique of nonlinear dynamic inversion (NDI). The tailless aircraft model mounted on a three degree-of-freedom (3-DOF) dynamic rig in the wind tunnel is modeled as a nonlinear affine system subject to mismatched disturbances. First of all, a baseline NDI attitude controller is designed for sufficient stability and good reference tracking performance of the nominal system. Then, a nonlinear disturbance observer (NDO) is supplemented to the baseline NDI controller to estimate the lumped disturbances for compensation, including unmodeled dynamics, parameter uncertainties, and external disturbances. Mathematical analysis demonstrates the convergence of the employed NDO and the resulting closed-loop system. Furthermore, an anti-windup modification is applied to the NDO for control performance preserving in the presence of actuator saturation. Subsequently, the designed control schemes are preliminarily validated and compared via simulations. The baseline NDI controller demonstrates satisfactory attitude tracking performance in the case of nominal simulation; the NDO augmented NDI controller presents significantly improved ability of disturbance rejection when compared with the baseline NDI controller in the case of robust simulation; the anti-windup modified scheme, rather than the baseline NDI controller nor the NDO augmented NDI controller, can preserve the closed-loop performance in the case of actuator saturation. Finally, the baseline NDI scheme and the NDO augmented NDI scheme are implemented and further validated in the wind tunnel flight tests, which demonstrate that the experimental results are in good agreement with that of the simulations.

To avoid the singular problems in the roll axis of Nonlinear Dynamic Inversion (NDI) flight control law with common roll axis response-type, the design of a NDI flight control law with the roll axis response-type of wind-axis roll rate is discussed. Furthermore, a new slow dynamics is used inside the outer loop controller. Reference model and PI compensator are used to generate the command for outer loop controller. The control law and its performance are evaluated on a nonlinear dynamic model of an F-16 aircraft. And the new response-type is shown to yield better tacking performance by comparing with the two conventional response-types in a combined maneuver of Split-S and Cobra.

The so-called "New Space era" has seen a disruptive change in the business models and manufacturing technologies of launch vehicle companies. However, limited consideration has been given to the benefits that innovation in control theory can bring; not only in terms of increasing the limits of performance but also reducing mission preparation or "missionisation" efforts. Moreover, there is a gap between the current state-of-practice that still relies on linear controls and other modern control techniques that could bring relevant improvements in launcher attitude control; this is the case for nonlinear control algorithms, especially those based on Nonlinear Dynamic Inversion (NDI). NDI is a technique that basically 'cancels' the nonlinearities of a class of nonlinear systems, allowing for a single linear control law to be applied without the need for gain-scheduling across different operational points. Incremental NDI (INDI) is a variation of NDI that generates incremental commands and employs acceleration feedback to reduce model dependency, making it easier to design, and results in being more robust in closed-loop. While INDI has been applied successfully to several aerospace applications, its applicability to launch vehicles has not yet been adequately investigated. The objective of this paper is therefore to introduce and raise awareness of the INDI method among the launcher guidance, navigation, and control (GNC) community, showcasing its implementation on a representative launch ascent application scenario which highlights INDI's strengths and challenges. We present a new, practical approach for stability analysis of INDI for attitude control, and compare INDI with scheduled PD controllers with-and without angular acceleration estimates. Results show that, while INDI controllers are generally more sensitive to sensor noise and actuator delay than linear controllers, their potential benefits outweigh these limitations in terms of robustness and performance.

This paper presents a Single Network Adaptive Critic (SNAC) aided Nonlinear Dynamic Inversion (NDI) optimization controller with a Proportional-Derivative (PD) based Optimal Control Modification (OCM) design for fast pitch rate adaptation in an MRAC flight control system. The advantages of adding SNAC to the NDI MRAC controller with OCM adaptation are presented. It is seen from simulation results that the SNAC aided NDI controller with the OCM adaptation has fewer high frequency oscillations and increased robustness to the different network architectures of the OCM. Three variations of OCM uncertainty estimation basis functions, namely the OCM – Linear (OCM-L), OCM – Bias (OCM-B) and OCM Linear and Bias (OCM-LB) are included in this paper. The responses of the SNAC aided NDI and traditional NDI controllers, using the same OCM adaptation technique are compared. Simulation results run on a nonlinear model of a small single piston engine are included for demonstration.

PurposeAn artificial immune system (AIS) for the detection and identification of abnormal operational conditions affecting an unmanned air vehicle (UAV) is developed using the partition of the universe approach. The performance of the proposed methodology is assessed through simulation within the West Virginia University (WVU) unmanned aerial system (UAS) simulation environment.Design/methodology/approachAn AIS is designed and generated for a fixed wing UAV using data from the WVU UAS simulator. A novel partition of the universe approach augmented with the hierarchical multiself strategy is used to define the self, within the AIS paradigm. Several 2-dimensional and 3-dimensional commanded trajectories are simulated under normal and abnormal conditions affecting actuators and sensors. Data recorded are used to build the AIS and develop an abnormal condition detection and identification scheme for the two categories of subsystems. The performance of the methodology is evaluated in terms of detection and identification rates, false alarms and decision times.FindingsThe proposed methodology for UAV abnormal condition detection and identification has the potential to support a comprehensive and integrated solution to the problem of aircraft subsystem health management. The novel partition of the universe approach has been proven to be a promising alternative to the previously investigated clustering methods by providing similar or better performance for the cases investigated.Research limitations/implicationsThe promising results obtained within this research effort motivate further investigation and extension of the proposed methodology toward a complete system health management process, including abnormal condition evaluation and accommodation.Practical implicationsThe use of the partition of the universe approach for AIS generation may potentially represent a valuable alternative to current clustering methods within the AIS paradigm. It can facilitate a simpler and faster implementation of abnormal condition detection and identification schemes.Originality/valueIn this paper, a novel method for AIS generation, the partition of the universe approach, is formulated and applied for the first time for the development of abnormal condition detection and identification schemes for UAVs. This approach is computationally less expensive and mitigates some of the issues related to the typical clustering approaches. The implementation of the proposed approach can potentially enhance the robustness of UAS for safety purposes.

Nonlinear Dynamic Inversion(NDI) control has excellent rapidity and decoupling ability, unfortunately it lacks the essential robustness to disturbance. From the perspective of enhancing the robustness, an adaptive NDI method based on L1 adaptive structure is proposed. The L1 adaptive structure is introduced into the NDI control to enhance its robustness, which also guarantees the stability and expected dynamic performance of the system suffering from the disturbance influence. Secondly, the flight control law of the advanced aircraft is designed based on the present method to improve the robustness and fault tolerance of the flight control system. Finally, the effectiveness of the flight control law based on the present approach is verified under the fault disturbance. The results showed that the flight control law based on L1 adaptive NDI has excellent dynamic performance and strong robustness to parameter uncertainties and disturbances.

The traditional aircraft controller design is usually based on the off-line aerodynamic model. Due to the deviation of the off-line aerodynamic model, the flight quality is difficult to meet the requirements when the aircraft is flying in the real atmosphere. To solve this problem, this paper proposes a frequency domain identification-based improved adaptive nonlinear dynamic inversion (NDI) control method (FDI-ANDI). In this paper, an online recursive aerodynamic parameter identification method in the frequency domain is first designed, and then an adaptive dynamic inversion control method based on the online aerodynamic parameter identification results is established. Finally, aiming at the problem of the slow response speed of the NDI controller, an improved adaptive dynamic inversion control law is designed by using the method of series lead correction. Compared with the traditional control method, the adaptive dynamic inversion method based on online aerodynamic identification has stronger robustness and a faster response speed in the face of model uncertainty. The final simulation analysis shows that the method has a better control effect than the traditional control method.

The problem of cooperative obstacle avoidance by a group of unmanned ground vehicles (UGVs) and unmanned air vehicles (UAVs) during a typical search mission is addressed in this paper. The group of UAVs and UGVs are performing a search operation in the designated area. All the UAVs and UGVs are equipped with a vision sensor/LIDAR to identify the possible obstacles in the search space. Due to their operation on the ground, UGVs are more likely to encounter obstacles. The obstacle avoidance for UGV under the event of sensor failure is done with environment information from the nearest UAV. The UAV plans its trajectory according to the UGV’s expected future trajectory, leading towards the base station. The UGV replans its trajectory to avoid obstacles after obtaining the information from its nearest UAV. A simulation study is performed with 10 UAVs and five UGVs performing a search mission in 1[Formula: see text]km[Formula: see text][Formula: see text][Formula: see text]1[Formula: see text]km area. The proposed obstacle avoidance method is experimentally validated in the outdoor environment with an autonomous UAV equipped with a camera and an autonomous UGV navigating based on GPS localization and environment information from the UAV.

At the Institute of Flight System Dynamics of the Technische Universitat Munchen (TUM), a digital auto flight system for a fixed-wing Unmanned Air Vehicle (UAV) featuring a novel diamond-shaped configuration was designed, implemented and tested up to its very successful and impressive first flight campaign at the Denel Overberg Testrange, South Africa in July 2017. The capabilities of the UAV comprise a fully automated flight mission, with automatic take-off and landing including ground control for centerline tracking, runway alignment and automatic braking. This paper presents the in-flight verification of the model-based designed ground controller during the first flight of the UAV Demonstrator. Prior to the first flight, the performance during the lift-off and touch-down phases could be simulated only and never be tested before in real-live. However, the recorded flight test data exhibit and underline the capabilities of the ground controller during the whole automatic take-off and landing maneuvers. The focus of this paper is set on the analysis and verification of the ground controller performance as demonstrated in the course of the UAV's first flight in comparison to dedicated model-in-the-loop simulations.

Pseudo control compensation based neural network dynamic inversion control is applied in super-maneuverable flight control. The base control law is designed in nonlinear dynamic inversion; neural networks are used to adaptively cancel inversion error. Pseudo control compensator is designed to cancel the interaction between actuator and adaptive neural networks. Simulations show that limitation that needs accurate mathematical model in dynamic inversion method can be released. The actuator that affects the adaptive factor is removed in this control structure and the robustness of the whole system is greatly improved.