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This new paper combines errors-in-variable (EIV) system with closed loop situation to consider a more complex closed loop EIV system. Consider the identification problem for closed loop EIV system, synthesis analysis is completed from the point of system identification field. Firstly, the reason about why closed loop EIV system is useful in academy and practice is explained in detail. Secondly, to identify that unknown plant, existing in closed loop EIV system with the measurement and process noise simultaneously, nonparametric estimate is proposed to be one plant estimate through our own mathematical derivation. Then statistical analysis above nonparametric estimate is studied to show the unbiased property. Thirdly, for the rational transfer function as the unknown plant, parameterized by one unknown parameter vector, the plant identification is transferred into one parameter estimate, so after reformulating the parameterized input-output relation for closed loop EIV system, least squares solution is derived to be biased. To avoid this explicit biased term, bias compensated estimate and an iterative least squares estimate are proposed to improve the overall identification performance. Finally, one practical example is to show the existence of our considered closed loop EIV system, and the detailed numerical results prove our proposed identification strategies.

We present a direct data driven model reference control framework for flight simulation table from both the theoretical analysis and engineering application. Sate of the art direct driven model reference control designs the unknown controller based on the input-output data and guarantees the actual model converge to the reference model, under a case of no any priori knowledge for the unknown plant. We improve this direct data driven model reference control strategy by considering its stability validation and synthesis analysis for nonlinear controller. The resulting direct data driven model reference control scheme can be implemented to ensure flight simulation table rotate accuracy, resulting in improved performance index. As the main technical contribution, we show that the proposed direct data driven model reference control framework also ensures closed loop stability and suitable extension for nonlinear controller.

Based on our previous contributions about UAV target tracking and recognition, this paper continues to do the synthesis analysis for the ellipsoidal method for above practical problem. Given a nonlinear system or linear system, corresponding to the motion equation for the considered target, the UAV target tracking and recognition is to identify the unknown but bounded noise, then ellipsoidal method is proposed to achieve this goal. The main step for ellipsoidal method is to compute one intersection operation between one ellipsoid and a strip. Through our own mathematical derivations, this intersection part is also an ellipsoid. The center of the final ellipsoid is deemed as the accurate state estimation from the generated ellipsoidal sequences. To alleviate the deficiency of our previous contributions, synthesis analysis for ellipsoidal method is also derived to complete the efficiency. Finally, one simulation example confirms our ellipsoidal method for UAV target tracking and recognition.