The problem of anti-disturbance control has been an eternal topic along with the development of the control theory. However, most methodologies can only deal with systems subject to a single equivalent disturbance which was merged by various types of uncertainties. In this paper, a review on anti-disturbance control is presented for systems with multiple disturbances. First, the classical control methods are briefly reviewed for disturbance attenuation or rejection problems. Then, recent advances in disturbance observer based control (DOBC) theory are introduced and especially, the composite hierarchical anti-disturbance control (CHADC) is firstly addressed. A comparison of different approaches is briefly carried out. Finally, focuses in the field on the current research are also addressed with emphasis on the practical application of the techniques.

Anti-disturbance control theory for systems with multiple disturbances: a survey.

The robust optimised tracking control problem for a generic airbreathing hypersonic vehicle (AHV) subject to nonvanishing mismatched disturbances/uncertainties is investigated in this paper. A baseline nonlinear model predictive control (MPC) method is firstly introduced for optimised tracking control of the nominal dynamics. A nonlinear-disturbance-observer-based control law is then developed for robustness enhancement in the presence of both external disturbances and uncertainties. Compared with the existing robust tracking control methods for AHVs, the proposed composite nonlinear MPC method obtains not only promising robustness and disturbance rejection performance but also optimised nominal tracking control performance. The merits of the proposed method are validated by implementing simulation studies on the AHV system.

Composite predictive flight control for airbreathing hypersonic vehicles

Polarization navigation has been an active research direction in navigation fields due to its autonomy and robustness, especially in global navigation satellite system-denied environment. Most existing bionic polarization sensing schemes employed the linear polarizer as the analyzer. However, the quadrature error between the two sets of mutually orthogonal polarizers is usually inevitable, which may cause the sensor output distortion and directly affect the performance of polarization navigation sensors. In this paper, inspired by the special polarization-sensitive structure of insect compound eyes, a polarizing beam splitter (PBS) based polarization navigation sensor is designed. Concerned with the optical path coupling and extinction ratio inconsistency problem between the transmitted beam and the reflected one of the PBS, the novel sensor model is established, where we introduce a coupling coefficient to solve the problems mentioned above. An algorithm of computing the polarization degree and the polarization azimuth angle is presented based on the model. The unscented Kalman filter calibration method is adopted and the calibration experiments are carried out. Finally, the outdoor static accuracy of the sensor is tested, where the results showed that the sensor designed in this paper achieved high accuracy and good stability for a long time.

A Bionic Polarization Navigation Sensor Based on Polarizing Beam Splitter

A novel autonomous initial alignment for the stationary strapdown inertial navigation system (SINS) with the bio-inspired polarized skylight sensors (PSS) is proposed to improve the precision and convergence speed in this paper. A pair of perpendicularly polarized skylight sensors with six-channels are used to measure and calculate the polarization azimuth angles, which is finally constructed a new polarized measurement error equation. With the SINS error equations, the SINS/PSS integrated navigation error equations are developed to achieve autonomous and fast initial alignment. Two types of observability, geometric observability and stochastic observability, are introduced to evaluate the integrated navigation systems. The geometric observability, which is defined by calculating the rank of the observability matrix, is analysed. It is found that the integrated navigation systems are not completely observable. Further, the degree of observability based on singular value decomposition is also used to examine the observable degree of the SINS/PSS integrated navigation system. The stochastic observability is associated with observability analysis with the estimated precision of the designed filter. The Kalman filter is designed to estimate the unknown state based on the measurements from the inertial measurement unit and PSS. Simulations and experiments demonstrate that the SINS/PSS achieve a rapid initial alignment with higher precision. Compared with the SINS, the vertical misalignment angle accuracy of SINS/PSS is improved by more than 90%, and the time cost is shortened by 60% in the simulation test. For the initial alignment experiment, the vertical misalignment greatly improved the accuracy and shorten the time cost.

An Autonomous Initial Alignment and Observability Analysis for SINS With Bio-Inspired Polarized Skylight Sensors

One of the critical challenges in spacecraft attitude estimation is the handling nonlinear non-Gaussian noisy conditions originating from measurement errors, model drifts, and incorrect models. In this article, we propose a new, adaptive and robust nonlinear Kalman filter to address this issue. The proposed filter is derived by casting the problem as a nonlinear regression problem and then realized by building on the robust filter, based on multiple strong tracking with multiple fading factors. In particular, the optimal filter gain is achieved by minimizing the constrained cost function that combines the minimum mean square error and the norm constraint. The resulting algorithm, referred to as the adaptive Huber filter based on the multiple strong tracking (AHFMST), shows promising results. Several simulation scenarios considering the realistic initial errors for a low-orbiting spacecraft attitude estimation are studied to compare the performance of the new algorithm and the state-of-the-art algorithms. The proposed algorithm has demonstrated better performance in terms of robustness and estimation accuracy.

Improved Cubature Kalman Filter for Spacecraft Attitude Estimation

Initial alignment for inertial navigation system (INS) has been widely used in practice under the assumption of Gaussian noises. In most previous works, nonlinear dynamics was ignored and the disturbances were merged into either a Gaussian or norm-bounded variable, where the Kalman filtering or robust filtering can be applied, respectively. In this article, the unmodelled nonlinear dynamics, drifts, parametric uncertainties, as well as other disturbances are considered simultaneously and are formulated into different types of uncertain disturbances described by the exo-system, stochastic and norm-bounded variable, respectively. A nonlinear initial alignment approach for INS is first presented based on a new disturbance attenuation and rejection filtering scheme against multiple disturbances. The INS error model with both nonlinear dynamics and multiple disturbances is established and the initial alignment problem is transformed into a robust nonlinear filter design problem for a class of nonlinear systems...

Chuntang Qi

Papers

Initial alignment for nonlinear inertial navigation systems with multiple disturbances based on enhanced anti-disturbance filtering