Cooperative interception with fast multiple model adaptive estimation

For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang-bang evasive maneuver with a random switching time.Combined Fast multiple model adaptive estimation(Fast MMAE)algorithm,the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration.Introduced the detection error model related to the line-of-sight(LOS)separation angle of two interceptors,an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance.Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion,Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately.The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.

Novel robust fault diagnosis method for flight control systems

A novel robust fault diagnosis scheme, which possesses fault estimate capability as well as fault diagnosis property, is proposed. The scheme is developed based on a suitable combination of the adaptive multiple model （AMM） and unknown input observer （UIO）. The main idea of the proposed scheme stems from the fact that the actuator Lock-in-Place fault is unknown （when and where the actuator gets locked are unknown）, and multiple models are used to describe different fault scenarios, then a bank of unknown input observers are designed to implement the disturbance de-coupling. According to Lyapunov theory, proof of the robustness of the newly developed scheme in the presence of faults and disturbances is derived. Numerical simulation results on an aircraft example show satisfactory performance of the proposed algorithm.

Fuzzy adaptive tracking control within the full envelope for an unmanned aerial vehicle

Motivated by the autopilot of an unmanned aerial vehicle（UAV） with a wide flight envelope span experiencing large parametric variations in the presence of uncertainties, a fuzzy adaptive tracking controller（FATC） is proposed. The controller consists of a fuzzy baseline controller and an adaptive increment, and the main highlight is that the fuzzy baseline controller and adaptation laws are both based on the fuzzy multiple Lyapunov function approach, which helps to reduce the conservatism for the large envelope and guarantees satisfactory tracking performances with strong robustness simultaneously within the whole envelope. The constraint condition of the fuzzy baseline controller is provided in the form of linear matrix inequality（LMI）, and it specifies the satisfactory tracking performances in the absence of uncertainties. The adaptive increment ensures the uniformly ultimately bounded（UUB） predication errors to recover satisfactory responses in the presence of uncertainties. Simulation results show that the proposed controller helps to achieve high-accuracy tracking of airspeed and altitude desirable commands with strong robustness to uncertainties throughout the entire flight envelope.

Robust state of charge and state of health estimation for batteries using a novel multi model approach

Estimation of state-of-charge and state-of-health for batteries is one of the most important feature for modern battery management system(BMS).Robust or adaptive methods are the most investigated because a more intelligent BMS could lead to sensible cost reduction of the entire battery system.We propose a new robust method,called ERMES(extendible range multi-model estimator),for determining an estimated state-of-charge(SoC),an estimated state-of-health(SoH)and a prediction of uncertainty of the estimates(state-of-uncertainty—SoU),thanks to which it is possible to monitor the validity of the estimates and adjust it,extending the robustness against a wider range of uncertainty,if necessary.Specifically,a finite number of models in state-space form are considered starting from a modified Thevenin battery model.Each model is characterized by a hypothesis of SoH value.An iterated extended Kalman filter(EKF)is then applied to each model in parallel,estimating for each one the SoC state variable.Residual errors are then considered to fuse both the estimated SoC and SoH from the bank of EKF,yielding the overall SoC and SoH estimates,respectively.In addition,a figure of uncertainty of such estimates is also provided.

Review:Advances in the control of mechatronic suspension systems

The suspension system is a key element in motor vehicles. Advancements in electronics and microprocessor technology have led to the realization of mechatronic suspensions. Since its introduction in some production motorcars in the 1980 s, it has remained an area which sees active research and development, and this will likely continue for many years to come. With the aim of identifying current trends and future focus areas, this paper presents a review on the state-of-the-art of mechatronic suspensions. First, some commonly used classifications of mechatronic suspensions are presented. This is followed by a discussion on some of the actuating mechanisms used to provide control action. A survey is then reported on the many types of control approaches, including look-ahead preview, predictive, fuzzy logic, proportional–integral–derivative(PID), optimal, robust, adaptive, robust adaptive,and switching control. In conclusion, hydraulic actuators are most commonly used, but they impose high power requirements, limiting practical realizations of active suspensions. Electromagnetic actuators are seen to hold the promise of lower power requirements, and rigorous research and development should be conducted to make them commercially usable. Current focus on control methods that are robust to suspension parameter variations also seems to produce limited performance improvements, and future control approaches should be adaptive to the changeable driving conditions.

EMMAE failure detection system and failure evaluation over flight performance

Purpose-The purpose of this paper is to present the research into fault detection and isolation(FDI)and evaluation of the reduction of performance after failures occurred in the flight control system(FCS)during its mission operation.Design/methodology/approach–The FDI is accomplished via using the multiple models scheme which is developed based on the Extend Kalman Filter(EKF)algorithm.Towards this objective,the healthy mode of the FCS under different type of failures,including the control surfaces and structural,should be considered.It developed a bank of extended multiple models adaptive estimation(EMMAE)to detect and isolate the above mentioned failures in the FCS.In addition,the performances including the flight envelope,the voyage and endurance in cruising are proposed to reference and evaluate the process of mission,especially for UAV under failure conditions.Findings-The contribution of this paper is to provide the information not only about the failures,but also considering whether the UAV can accomplish the task for the ground station.Originality/value-The main contribution of this paper is in the areas of the structural and control surface faults researching,which are occurred in the mission procedures and emphasized the identification of those failures’magnitudes.The FDI scheme includes the performance evaluation,while the evaluation obtained through the extensive numerical simulations and saved in the offline database.As a consequence,it is more accurate and less computationally demanding while evaluating the performance.