This paper proposes a novel composite integrated guidance and control (IGC) law for missile intercepting against unknown maneuvering target with multiple uncertainties and control constraint. First, by using back-stepping technique, the proposed IGC law design is separated into guidance loop and control loop. The unknown target maneuvers and variations of aerodynamics parameters in guidance and control loop are viewed as uncertainties, which are estimated and compensated by designed model-assisted reduced-order extended state observer (ESO). Second, based on the principle of active disturbance rejection control (ADRC), enhanced feedback linearization (FL) based control law is implemented for the IGC model using the estimates generated by reduced-order ESO. In addition, performance analysis and comparisons between ESO and reduced-order ESO are examined. Nonlinear tracking differentiator is employed to construct the derivative of virtual control command in the control loop. Third, the closed-loop stability for the considered system is established. Finally, the effectiveness of the proposed IGC law in enhanced interception performance such as smooth interception course, improved robustness against multiple uncertainties as well as reduced control consumption during initial phase are demonstrated through simulations.

Back-stepping active disturbance rejection control design for integrated missile guidance and control system via reduced-order ESO.

Calibration transfer for use with spectroscopic instruments, particularly for near-infrared, infrared, and Raman analysis, has been the subject of multiple articles, research papers, book chapters, and technical reviews. There has been a myriad of approaches published and claims made for resolving the problems associated with transferring calibrations; however, the capability of attaining identical results over time from two or more instruments using an identical calibration still eludes technologists. Calibration transfer, in a precise definition, refers to a series of analytical approaches or chemometric techniques used to attempt to apply a single spectral database, and the calibration model developed using that database, for two or more instruments, with statistically retained accuracy and precision. Ideally, one would develop a single calibration for any particular application, and move it indiscriminately across instruments and achieve identical analysis or prediction results. There are many technical aspects involved in such precision calibration transfer, related to the measuring instrument reproducibility and repeatability, the reference chemical values used for the calibration, the multivariate mathematics used for calibration, and sample presentation repeatability and reproducibility. Ideally, a multivariate model developed on a single instrument would provide a statistically identical analysis when used on other instruments following transfer. This paper reviews common calibration transfer techniques, mostly related to instrument differences, and the mathematics of the uncertainty between instruments when making spectroscopic measurements of identical samples. It does not specifically address calibration maintenance or reference laboratory differences.

https://journals.sagepub.com/doi/pdf/10.1177/0003702817736064

A Review of Calibration Transfer Practices and Instrument Differences in Spectroscopy

Distributed consensus with limited communication data rate is an interesting and important problem and has been studied for multi-agent networks with the first order and second order integrator dynamics. However, the problem remains challenging for higher-order systems due to the complexity of consensus analysis and data rate minimization. In this paper, the leaderless and leader-following quantized consensus for a special kind of high-order systems are considered. Each agent is assumed to be controllable and observable with single input and single output, and its system matrix admits $n$ identical eigenvalues of 1. The special case of $n$ - th order integrator multi-agent system with only the first state variable being measurable is first investigated, and it is shown that for a fixed undirected connected network, $n$ bits of information exchange between agents suffice to guarantee the leaderless and leader-following quantized consensus respectively with an exponential convergence rate by employing a suitable encoding-decoding scheme and perturbation analysis of matrices. The obtained results are then extended to the general case of multi-agent dynamics with $n$ identical eigenvalues of 1 by applying the same encoding-decoding scheme and control protocol to an observer system for each agent. The final consensus value and the application to formation control are also discussed.

Quantized leaderless and leader-following consensus of high-order multi-agent systems with limited data rate

The problems of instrumental variation and sensor drift are receiving increasing concerns in the field of electronic noses. Because the two problems can be uniformly viewed as a variation of the data distribution in the feature space, they can be handled by algorithms such as transfer learning. In this paper, we propose a novel algorithm framework called transfer sample-based coupled task learning (TCTL). It is based on transfer learning and multi-task learning. Given labeled samples in the source domain (i.e. from the master device or without drift) and a small set of transfer samples as inputs, TCTL simultaneously learns a prediction model for data in the source domain and one for data in the target domain (i.e. from the slave device or with drift). The transfer samples are incorporated into a regularization term of the objective function. TCTL is an extensible framework that can apply to various classification and regression models. When combined with the standardization error-based model improvement (SEMI) strategy, its accuracy can be further enhanced. Experiments on a multi-device dataset and a popular long-term drift dataset show that the proposed algorithms achieve better accuracy compared with typical existing methods with much fewer auxiliary samples needed, which proves their efficacy and usability in real-life applications.

Calibration transfer and drift compensation of e-noses via coupled task learning

Three dimensional impact angle constrained integrated guidance and control for missiles with input saturation and actuator failure

Support Vector Regression (SVR) has been widely used as a nonlinear regression method in multivariate calibration. This paper investigates the transfer of SVR models between different environmental conditions based on a transfer learning method. In this method, the difference of the SVR regression vectors between old and new conditions is penalized by a L2 norm penalty, which can prevent the new SVR model from changing too much. By utilizing the commonality of SVR models between different conditions, this method reduces the requirement of training samples for building a new SVR model and thus can update the old SVR model when a new instrument or new conditions are encountered. The new method was compared with several traditional calibration transfer methods including the Piecewise Direct Standardization (PDS) method and two kinds of model updating methods. Experimental results on three benchmark near-infrared datasets show that the transfer results obtained by the proposed approach can be better than those obtained by the existing calibration transfer methods, especially when the standardization samples are selected from the support vectors used in the primary model.

Near-infrared calibration transfer via support vector machine and transfer learning

A fault-tolerant integrated guidance and control law is proposed based on backstepping and input-to-state stability in the presence of actuator failures, target maneuvers and unknown bounded model uncertainties. An adaptive law is designed to estimate the unknown parameter with respect to the actuation effectiveness components. Theoretical analysis is shown that the line-of-sight (LOS) rate is input-to-state stable (ISS), and the stability of the missile dynamics can be guaranteed as well. The numerical simulation results confirm the effectiveness of the fault-tolerant IGC law.

Adaptive integrated guidance and control with actuator failures based on backstepping and input-to-state stability

A novel three-dimensional fault-tolerant control guidance law is proposed for interception of maneuvering targets in the presence of external disturbances, actuator failures, and control input constraints. The input-to-state stability ISS method is introduced to design the fault-tolerant control guidance law to guarantee robust tracking of a maneuvering target. Then, a saturated fault-tolerant control guidance law is constructed using a modified saturation function to ensure the resulting control signal will never incur input constraints, and the convergence to a small neighborhood of origin is ensured in theory. Simulation results show that the presented approach is effective in achieving a successful interception against target maneuvers, external disturbances, actuator failures, and control input constraints.

Guidance Laws With Input Constraints and Actuator Failures

In this study, the authors consider the consensus output regulation problem for a class of non-linear multi-agent systems with subsystems have the same relative degree. The multi-agent system is heterogeneous that means subsystems may have different dynamics. The author's goal is to design a dynamic controller such that the outputs of subsystems follow a desired trajectory which is the output of exosystem. A recursive procedure and internal model are employed to achieve this goal.

Cooperative output regulation for a class of non-linear multi-agent systems

The paper develops the robust Hinfin control to deal with the uncertain stochastic nonlinear systems driven by noise of unknown covariance. By introducing the notion of stochastic dissipation, we point out that the stochastic dissipative system has the L2 gain property. From this point, we solve the robust Hinfin control problem for the uncertain stochastic nonlinear systems with noise of unknown covariance via state feedback. Furthermore, we provide the sufficient conditions under which the dynamic output controller is obtained to handle the robust Hinfin problem for such stochastic nonlinear systems on the basis of the solvability of the Hinfin control problem by state feedback.

Ji Haibo

Papers

Near-infrared calibration transfer via support vector machine and transfer learning

Adaptive integrated guidance and control with actuator failures based on backstepping and input-to-state stability

Guidance Laws With Input Constraints and Actuator Failures

Cooperative output regulation for a class of non-linear multi-agent systems

Robust H ∞ control of uncertain stochastic nonlinear systems driven by noise of unknown covariance