The authors regret to inform that an incorrect transfer function was included in Eq. (12). The correct transfer function is: [Formula Presented] The selected gains were [Formula Presented]. This leads to a real pole at 0.964 and two complex poles at 0.965 ± 0.0445i. The difference of the model compared to the measured step response has now reduced, the largest difference being 4.8% of the final step value at 0.14 s. The mistake does not influence any of the conclusions drawn in the paper. The authors would like to apologize for any inconvenience caused.

In this paper, an online flight envelope protection system is developed and implemented on impaired aircraft with structural damage. The whole protection system is designed to be a closed loop of several subsystems, including system identification, damage classification, flight-envelope prediction, and fault-tolerant control. Based on the information given by damage classification, the flight envelopes are explicitly retrieved, processed online from the database, and fed into the fault-tolerant controller, which makes the protection system adaptive to a wide range of abnormal conditions. Simulation results show that with envelope protection, loss-of-control accidents are more likely to be prevented, since excessive commands to the controller are restricted based on the updated information of the changed flight envelopes. In this way, the fault tolerance of the impaired aircraft can be effectively enhanced.

Optical flow-based control strategies have always inspired robotic scientists, especially those in the field of Micro Air Vehicles (MAVs), thanks to their computational efficiency and relative simplicity. A major problem is that the success of optical flow control is governed by the availability of distance estimates, while optical flow provides only the ratio of velocity to distance. Therefore, with only monocular visual information, the inherent nonlinearity of optical flow observables has imposed several challenges in the controller design. In this paper, we propose a newly formulated controller, Extended Incremental Nonlinear Dynamic Inversion (EINDI), to deal with nonlinearities in the system output, such as optical flow control problems. The proposed method unlocks the potential of its predecessor (INDI) in output feedback control by removing the common assumption of time-scale separation, allowing internal dynamics to exist, and requiring only the input and output measurements. Furthermore, the EINDI method has been implemented on an MAV and tested successfully for optical flow landing in a simulation and a real-world outdoor environment. Both simulation and flight test results show 1) good tracking performance of the EINDI control compared to the conventional feedback control, 2) smooth landing trajectories without any oscillation, and 3) fast adaptation of the EINDI control even for landings at different heights and desired setpoints.

Managing air data sensor fault detection and diagnosis (FDD) in the presence of atmospheric turbulence is challenging since the effects of faults and turbulence are coupled. Existing FDD approaches cannot decouple the faults from the turbulence. To address this challenge, this brief first proposes a novel kinematic model that incorporates the effects of the turbulence. This model is valid inside the entire flight envelope, and there is no need to design a linear parameter varying system. Then, the double-model adaptive estimation algorithm is extended to achieve unbiased state estimation even in the presence of unknown disturbances. The proposed approach is validated using generated turbulence data with various scale lengths and intensities. More importantly, the proposed approach is successfully validated using the real flight test data of a business jet when it is experiencing atmospheric turbulence.

Heuristic dynamic programming is a class of reinforcement learning, which has been introduced to aerospace engineering to solve nonlinear, optimal adaptive control problems. However, it requires an off-line learning stage to train a global system model to represent the system dynamics. This paper uses an incremental model in heuristic dynamic programming to improve the online learning ability, which is incremental model based heuristic dynamic programming. The trait of the online identification of the incremental model makes this method an option for fault-tolerant control and partially observable control problems. This study, therefore, also extends this method to deal with partial observability. The presented method has been validated on two different online tracking problems: missile fault-tolerant control with full-state measurements and also spacecraft attitude control disturbed with liquid sloshing under partially observable conditions. The results reveal that the proposed method outperforms the conventional heuristic dynamic programming method in fault-tolerant control tasks, deals with partial observability, and is robust to internal uncertainties and external disturbances.

This paper presents an agile and robust spacecraft attitude tracking controller using the recently reformulated incremental nonlinear dynamic inversion (INDI). INDI is a combined model- and sensor-based control approach that only requires a control effectiveness model and measurements of the state and some of its derivatives, making a reduced dependency on exact system dynamics knowledge. The reformulated INDI allows a non-cascaded dynamic inversion control in terms of Modified Rodrigues Parameters (MRPs) where scheduling of the time-varying control effectiveness is done analytically. This way, the controller is only sensitive to parametric uncertainty of the augmented spacecraft inertia and its wheelset alignment. Moreover, we draw some parallels to time-delay control (TDC) -more familiar in the robotics community- which have been shown to be equivalent to the incremental formulation of proportional-integral-derivative (PID) control for second order nonlinear systems in controller canonical form. Simulation experiments for this particular problem demonstrate that INDI has similar nominal performance as TDC/PID control, but superior robust performance and stability.

A variable stability in-flight simulator has the capabilities to change the response of an aircraft in-flight, often without changing the physical properties of the aircraft. The ability to adjust the aircraft response characteristics and handling qualities has various purposes, such as pilot training, control system development, and handling quality research. A variable stability control system is designed for a medium-range business jet using incremental nonlinear dynamic inversion. The performance of the in-flight simulator is verified by two experiments, one conducted in a fixed-base flight simulator and one in a Cessna Citation II laboratory aircraft. The fly-by-wire actuation system in the Cessna Citation II is based on its existing autopilot, inheriting the limited performance and safety protections. The simulator experiment shows differences between the experienced handling qualities for a reference model and the designed controller combined with aircraft dynamics. These differences mainly arise due to actuator saturation for specific handling quality settings. The in-flight experiment supports the simulator findings but also reveals how the available control authority around the initial condition is limited due to constraints of the fly-by-wire system.

High-speed flight in GPS-denied environments is currently an important frontier in the research on autonomous flight of micro air vehicles. Autonomous drone races stimulate the advances in this area by representing a very challenging case with tight turns, texture-less floors, and dynamic spectators around the track. These properties hamper the use of standard visual odometry approaches and imply that the micro air vehicles will have to bridge considerable time intervals without position feedback. To this end, we propose an approach to trajectory estimation for drone racing that is computationally efficient and yet able to accurately estimate a micro air vehicle’s state (including biases) and parameters based on sparse, noisy observations of racing gates. The key concept of the approach is to optimize unknown and difficult-to-observe state variables so that the observations of the racing gates best fit with the known control inputs, estimated attitudes, and the quadrotor dynamics and aerodynamics during a time window. It is shown that a gradient-descent implementation of the proposed approach converges ∼4 times quicker to (approximately) correct bias values than a state-of-the-art 15-state extended Kalman filter. Moreover, it reaches a higher accuracy, as the predicted end-point of an open-loop turn is on average only ∼20 cm away from the actual end-point, while the extended Kalman filter and the gradient descent method with kinematic model only reach an accuracy of ∼50 cm. Although the approach is applied here to drone racing, it generalizes to other settings in which a micro air vehicle may only have sparse access to velocity and/or position measurements.

This paper proposes an Incremental Sliding Mode Control driven by Sliding Mode Disturbance Observers (INDI-SMC/SMDO), with application to a quadrotor fault tolerant control problem. By designing the SMC/SMDO based on the control structure of the sensor-based Incremental Nonlinear Dynamic Inversion (INDI), instead of the model-based Nonlinear Dynamic Inversion (NDI) in the literature, the model dependency of the controller and the uncertainties in the closed-loop system are simultaneously reduced. This allows INDI-SMC/SMDO to passively resist a wider variety of faults and external disturbances using continuous control inputs with lower control and observer gains. When applied to a quadrotor, both numerical simulations and real-world flight tests demonstrate that INDI based SMC/SMDO has better performance and robustness over NDI based SMC/SMDO, in the presence of model uncertainties, wind disturbances, and sudden actuator faults. Moreover, the implementation process is simplified because of the reduced model dependency and smaller uncertainty variations of INDI-SMC/SMDO. Therefore, the proposed control method can be easily implemented to improve the performance and survivability of quadrotors in real life.

This paper proposes incremental nonsingular terminal sliding mode control for a class of multi-input and multi-output nonlinear systems considering model uncertainties, external disturbances, and sudden actuator faults. This method is free from singularity because it does not involve any negative fractional power. The convergence time in both reaching and sliding phases are proved to be finite. Moreover, by fully exploiting sensor measurements, the proposed incremental control method simultaneously reduces model dependency and the uncertainty remaining in the closed-loop system. The reduction of model dependency simplifies the implementation process and reduces the computational load, while the reduction of uncertainty decreases the minimum possible sliding mode control gains, which is beneficial to chattering reduction. These merits are verified by a quadrotor trajectory tracking problem. Simulation results demonstrate that the proposed method has better robustness against model uncertainties, gusts, and actuator faults than the model-based nonsingular terminal sliding mode control in the literature.

Autonomous guidance and navigation problems often have high-dimensional spaces, multiple objectives, and consequently a large number of states and actions, which is known as the ‘curse of dimensionality’. Furthermore, systems often have partial observability instead of a perfect perception of their environment. Recent research has sought to deal with these problems by using Hierarchical Reinforcement Learning, which often uses same or similar reinforcement learning methods within one application so that multiple objectives can be combined. However, there is not a single learning method that can benefit all targets. To acquire optimal decision-making most efficiently, this paper proposes a hybrid Hierarchical Reinforcement Learning method consisting of several levels, where each level uses various methods to optimize the learning with different types of information and objectives. An algorithm is provided using the proposed method and applied to an online guidance and navigation task. The navigation environments are complex, partially observable, and a priori unknown. Simulation results indicate that the proposed hybrid Hierarchical Reinforcement Learning method, compared to flat or non-hybrid methods, can help to accelerate learning, to alleviate the ‘curse of dimensionality’ in complex decision-making tasks. In addition, the mixture of relative micro states and absolute macro states can help to reduce the uncertainty or ambiguity at high levels, to transfer the learned results within and across tasks efficiently, and to apply to non-stationary environments. This proposed method can yield a hierarchical optimal policy for autonomous guidance and navigation without a priori knowledge of the system or the environment.

This paper proposes a novel control framework that combines the recently reformulated incremental nonlinear dynamic inversion with (higher-order) sliding-mode controllers/observers, for generic multi-input/multi-output nonlinear systems, named incremental sliding-mode control. As compared to the widely used approach that designs (higher-order) sliding-mode controllers/observers based on nonlinear dynamic inversion, the proposed incremental framework can further reduce the uncertainties while requiring less model knowledge. Because the uncertainties are reduced in the incremental framework, theoretical analyses demonstrate that the incremental sliding-mode control can passively resist a wider range of perturbations with reduced minimum possible control/observer gains. These merits are validated via numerical simulations for aircraft command tracking problems, in the presence of sudden actuator faults and structural damage.

Recently, several insect- and hummingbird-inspired tailless flapping wing robots have been introduced. However, their flight dynamics, which are likely to be similar to that of their biological counterparts, remain yet to be fully understood. We propose a minimal dynamic model that is not only validated with experimental data, but also able to predict the consequences of various important design changes. Specifically, the model captures the flapping-cycle-averaged longitudinal dynamics, considering the main aerodynamic effects. We validated the model with flight data captured with a tailless flapping wing robot, the DelFly Nimble, for air speeds from near-hover flight up to 3.5 m s-1. Moreover, the model succeeds in predicting the effects of changes to the center of mass location, and to the control system gains. Hence, the model is suitable even for the initial control design phase. To demonstrate this, we have used the simulation model to tune the robot's control system for higher speeds. Using the new control parameters on the real robot improved its maximal stable speed from 4 m s-1 to 7 m s-1.

This paper discusses the design, implementation and flight testing of an incremental Backstepping (IBS) based manual flight control law with angular accelerometer (AA) feedback. The main advantages of incremental control laws are that they only require a partial model of the system and are of low complexity. Incremental control laws for aircraft rotational motion, however, need angular acceleration measurements to compute the control increments. Previously, estimates based on angular rate measurements were used for this. The newly implemented AA feedback is expected to improve the performance of the controller by decreasing the sensor delay. The manual control laws command roll rate/angle, vertical load factor, and side slip angle and have been implemented on a Cessna Citation II aircraft, equipped with an experimental fly-by-wire system. The IBS based control law has an integrated integral control term and uses Pseudo Control Hedging to handle actuator saturations. The IBS based control law is shown to have highly satisfactory performance in flight. Test manoeuvres included standard roll and load factor commands and asymmetric thrust handling. Robustness to model mismatch has been compared in a nonlinear simulation for the controllers with and without AA feedback. In general, the AA feedback improved the tolerance to mismatch substantially.