Qiping Chu

Modeling Human Multimodal Perception and Control Using Genetic Maximum Likelihood Estimation

This paper presents a new method for estimating the parameters of multi-channel pilot models that is based on maximum likelihood estimation. To cope with the inherent nonlinearity of this optimization problem, the gradient-based Gauss-Newton algorithm commonly used to optimize the likelihood function in terms of output error is complemented with a genetic algorithm. This significantly increases the probability of finding the global optimum of the optimization problem. The genetic maximum likelihood method is successfully applied to data from a recent human-in-the-loop experiment. Accurate estimates of the pilot model parameters and the remnant characteristics were obtained. Multiple simulations with increasing levels of pilot remnant were performed, using the set of parameters found from the experimental data, to investigate how the accuracy of the parameter estimate is affected by increasing remnant. It is shown that only for very high levels of pilot remnant the bias in the parameter estimates is substantial. Some adjustments to the maximum likelihood method are proposed to reduce this bias.

Differential constraints for bounded recursive identification with multivariate splines

The ability to perform online model identification for nonlinear systems with unknown dynamics is essential to any adaptive model-based control system. In this paper, a new differential equality constrained recursive least squares estimator for multivariate simplex splines is presented that is able to perform online model identification and bounded model extrapolation in the framework of a model-based control system. A new type of linear constraints, the differential constraints, are used as differential boundary conditions within the recursive estimator which limit polynomial divergence when extrapolating data. The differential constraints are derived with a new, one-step matrix form of the de Casteljau algorithm, which reduces their formulation into a single matrix multiplication. The recursive estimator is demonstrated on a bivariate dataset, where it is shown to provide a speedup of two orders of magnitude over an ordinary least squares batch method. Additionally, it is demonstrated that inclusion of differential constraints in the least squares optimization scheme can prevent polynomial divergence close to edges of the model domain where local data coverage may be insufficient, a situation often encountered with global recursive data approximation.

Nonlinear Multivariate Spline-Based Control Allocation for High-Performance Aircraft

High performance flight control systems based on the nonlinear dynamic inversion (NDI) principle require highly accurate models of aircraft aerodynamics. In general, the accuracy of the internal model determines to what degree the system nonlinearities can be canceled; the more accurate the model, the better the cancellation, and with that, the higher the performance of the controller. In this paper a new control system is presented that combines NDI with multivariate simplex spline based control allocation. We present three control allocation strategies which use novel expressions for the analytical Jacobian and Hessian of the multivariate spline models. Multivariate simplex splines have a higher approximation power than ordinary polynomial models, and are capable of accurately modeling nonlinear aerodynamics over the entire flight envelope of an aircraft. This new method, indicated as SNDI, is applied to control a high performance aircraft (F-16) with a large flight envelope. The simulation results indicate that the SNDI controller can achieve feedback linearization throughout the entire flight envelope, leading to a significant increase in tracking performance compared to ordinary polynomial based NDI.

Multivariate Spline-Based Adaptive Control of High-Performance Aircraft with Aerodynamic Uncertainties

In this paper, a new modular adaptive control system is presented to compensate for aerodynamic uncertainties in high-performance flight control systems. This approach combines nonlinear dynamic inversion with multivariate spline-based adaptive control allocation. A new real-time identification routine for multivariate splines is presented to compensate for aerodynamic uncertainties in the control allocation system. This method, indicated as spline-based adaptive nonlinear dynamic inversion, is applied to control an F-16 aircraft subject to significant aerodynamics uncertainties. Simulation results indicate that the new controller can tune itself each time a model error is detected and has superior adaptability compared to an ordinary polynomial-based adaptive controller. Multivariate splines have sufficient flexibility and approximation power to accurately model nonlinear aerodynamics over the entire flight envelope. As a result, the global model remains intact. Although a part of the model is being reco...

Joint Sensor Based Backstepping for Fault-Tolerant Flight Control

The sensor based backstepping control law, based on the singular perturbation theory and Tikhonov’s theorem, is a novel nonlinear incremental control approach. This Lyapunov function based method is not susceptible to model uncertainties since it uses measured state derivatives instead of an onboard model. Considering these merits, the sensor based backstepping method is extended to handle sudden structural changes in the fault-tolerant flight control of an overactuated Boeing 747-200 aircraft with the control allocation being considered. Because of the application of the backstepping technique, this double-loop joint sensor based backstepping attitude controller allows more interaction between its outer and inner loops compared to a standard nonlinear dynamic inversion angular control approach. The benchmark with engine separation and rudder runaway failure scenarios is employed to evaluate the new controller. The simulation results show that the new joint sensor based backstepping attitude controller ca...

Stereo Vision Based Obstacle Avoidance on Flapping Wing MAVs

One of the major challenges in robotics is to develop a fly-like robot that can autonomously fly around in unknown environments. State-of-the-art research on autonomous flight of light-weight flapping wing MAVs uses information such as optic flow and appearance variation extracted from a single camera, and has met with limited success. This paper presents the first study of stereo vision for onboard obstacle detection. Stereo vision provides instantaneous distance estimates making the method less dependent than single camera methods on the camera motions resulting from the flapping. After hardware modifications specifically tuned to use on a flapping wing MAV, the computationally efficient Semi-Global Matching (SGM) algorithm in combination with off-board processing allows for accurate real-time distance estimation. Closed-loop indoor experiments with the flapping wing MAV DelFly II demonstrate the advantage of this technique over the use of optic flow measurements.

Online Aerodynamic Model Identification Using a Recursive Sequential Method for Multivariate Splines

Avoiding high computational loads is essential to online aerodynamic model identi- fication algorithms, which are at the heart of any model-based adaptive flight control system. Multivariate simplex B-spline (MVSB) methods are excellent function approximation tools for modeling the nonlinear aerodynamics of high performance aircraft. However, the computational efficiency of the MVSB method must be improved in order to enable real-time onboard applications, for example in adaptive nonlinear flight control systems. In this paper, a new recursive sequential identification strategy is proposed for the MVSB method aimed at increasing its computational efficiency, thereby allowing its use in onboard system identification applications. The main contribution of this new method is a significant reduction of computational load for large scale online identification problems as compared to the existing MVSB methods. The proposed method consists of two sequential steps for each time interval, and makes use of a decomposition of the global problem domain into a number of subdomains, called modules. In the first step the B-coefficients for each module are estimated using a least squares estimator. In the second step the local B-coefficients for each module are then smoothened into a single global B-coefficient vector using a linear minimum mean square errors (LMMSE) estimation. The new method is compared to existing batch and recursive MVSB methods in a numerical experiment in which an aerodynamic model is recursively identified based on data from an NASA F-16 wind-tunnel model.

Adaptive three-step Kalman filter for air data sensor fault detection and diagnosis

Air data sensor fault detection and diagnosis is important for the safety of aircraft. In this paper, first an extension of the robust three-step Kalman filter to nonlinear systems is made by proposing a robust three-step unscented Kalman filter. The robust three-step unscented Kalman filter is found to be sensitive to the initial condition error when dealing with air data sensor fault estimation. A theoretical analysis of this sensitivity is presented and a novel adaptive three-step unscented Kalman filter is proposed which is able to cope with not only the estimation of the air data sensor faults, but also the detection and isolation of faults. The adaptive three-step unscented Kalman filter contains three steps: time update, fault estimation and measurement update. This approach can reduce the sensitivity to the initial condition error. Finally, the air data sensor fault detection and diagnosis performance of the adaptive three-step unscented Kalman filter is validated using simulated aircraft data. Ad...

Air Data Sensor Fault Detection Using Kinematic Relations

This paper presents a Fault Detection and Isolation (FDI) method for Air Data Sensors (ADS) of aircraft. In the most general case, fault detection of these sensors on modern aircraft is performed by a logic that selects one of, or combines three redundant measurements. Such a method is compliant with current airworthiness regulations. However, in the framework of the global aircraft optimization for future and upcoming aircraft, it could be required, e.g. to extend the availability of sensor measurements. So, an improvement of the state of practice could be useful. Introducing a form of analytical redundancy of these measurements can increase the fault detection performance and result in a weight saving of the aircraft because there is no necessity anymore to increase the number of sensors. Furthermore, the analytical redundancy can contribute to the structural design optimization. The analytical redundancy in this method is introduced using an adaptive form of the Extended Kalman Filter (EKF). This EKF uses the kinematic relations of the aircraft and makes a state reconstruction from the available measurements possible. From this estimated state, an estimated output is calculated and compared to the measurements. Through observing a metric derived from the innovation of the Extended Kalman Filter (EKF), the performance of each of the redundant sensors is monitored. This metric is then used to automatically isolate the failing sensors.

Framework for state and unknown input estimation of linear time-varying systems

The design of unknown-input decoupled observers and filters requires the assumption of an existence condition in the literature. This paper addresses an unknown input filtering problem where the existence condition is not satisfied. Instead of designing a traditional unknown input decoupled filter, a Double-Model Adaptive Estimation approach is extended to solve the unknown input filtering problem. It is proved that the state and the unknown inputs can be estimated and decoupled using the extended Double-Model Adaptive Estimation approach without satisfying the existence condition. Numerical examples are presented in which the performance of the proposed approach is compared to methods from literature.