Yuanli Cai

A Reliable Energy-Efficient Multi-Level Routing Algorithm for Wireless Sensor Networks Using Fuzzy Petri Nets

A reliable energy-efficient multi-level routing algorithm in wireless sensor networks is proposed. The proposed algorithm considers the residual energy, number of the neighbors and centrality of each node for cluster formation, which is critical for well-balanced energy dissipation of the network. In the algorithm, a knowledge-based inference approach using fuzzy Petri nets is employed to select cluster heads, and then the fuzzy reasoning mechanism is used to compute the degree of reliability in the route sprouting tree from cluster heads to the base station. Finally, the most reliable route among the cluster heads can be constructed. The algorithm not only balances the energy load of each node but also provides global reliability for the whole network. Simulation results demonstrate that the proposed algorithm effectively prolongs the network lifetime and reduces the energy consumption.

Predictive Functional Control-Based Missile Autopilot Design

Predictive functional control is applied to design a missile autopilot considering control constraints. The missile nonlinear dynamics are first transformed into a linear structure with state-dependent coefficient matrices. At each sampling instant, the internal state-space model for prediction is obtained through a normal discretization procedure. Based upon this model, a predictive functional controller for the nonlinear missile autopilot is proposed. Compared with the conventional predictive control algorithms, which usually involve an online quadratic programming in the practical implementation, the new controller demands less online computation resources. Simulation results validate the effectiveness and robustness of the proposed algorithm.

Influence of altitude on aero-optic imaging deviation

Aero-optic imaging deviation is a kind of aero-optic effect. It characterizes the image position displacement on an imaging plane. This paper studies the influence of altitude on aero-optic imaging deviation. The Reynolds-averaged Navier-Stokes solver provided in FLUENT was used for flow computations. The Runge-Kutta method based ray tracing was adopted for optics calculations. The orthogonal array was brought in for the experiment arrangement. Four representative suites of imaging deviations and imaging deviation slopes were obtained in the altitude range of 10-60 km. The results show that as altitude increases, the imaging deviation decreases, and the imaging deviation slope approaches zero from a negative value.

Least squares wavelet support vector machines for nonlinear system identification

A novel admissible support vector kernel, namely the wavelet kernel satisfying wavelet frames, is presented based on the wavelet theory. The wavelet kernel can approximate arbitrary functions, and is especially suitable for local signal analysis, hence the generalization ability of the support vector machines (SVM) is improved. Based on the wavelet kernel and the least squares support vector machines, the least squares wavelet support vector machines (LS-WSVM) are constructed. In order to validate the performance of the wavelet kernel, LS-WSVM is applied to a nonlinear system identification problem, and the computational process is compared with that of the Gaussian kernel. The results show that the wavelet kernel is more efficient than the Gaussian kernel.

Bifurcation and Chaos in the Duffing Oscillator with a PID Controller

We discuss in this paper the bifurcation, stability and chaos of the non-linear Duffing oscillator with a PID controller. Hopf bifurcation can occur and we show that there is a global stable fixed point. The PID controller works well in some fields of the parameter space, but in other fields of the parameter space, or if the reference input is not equal to zero, chaos is common for hard spring type system and so is fractal basin boundary for soft spring system. The Melnikov method is used to obtain the criterion of fractal basin boundary.

Iterated cubature Kalman filter and its application

We present the novel iterated cubature Kalman filter (ICKF) in which the measurement update of square root of cubature Kalman filter (SR-CKF) is refined to iterate process for fully exploiting the latest measurement so as to achieve the high accuracy of state estimation. The ICKF is implemented easily and inherits the virtues of SR-CKF. We apply ICKF to state estimation for reentry ballistic target with unknown ballistic coefficient. Simulation results indicate ICKF outperforms over the unscented Kalman filter (UKF) and SR-CKF in state estimation accuracy.

Nonlinear disturbance observer-based model predictive control for a generic hypersonic vehicle

This article investigates a nonlinear disturbance observer-based model predictive control algorithm for the longitudinal dynamics of a generic hypersonic vehicle under external disturbances and system parameter perturbations. The model predictive control is combined with the nonlinear disturbance observer technique for uncertainty compensation. The nonlinear dynamics are first transformed into the linear structure with state-dependent coefficient matrices. At each sampling instant, the internal state-space model for prediction is obtained through a normal discretization procedure. Based upon this model, the model predictive controller is designed in nominal condition. Finally, a nonlinear disturbance observer is presented to estimate the uncertainty and a disturbance compensation gain is designed to compensate the uncertainty. Particularly, the offset-free tracking feature of the output for the reference signal is proved. Simulations show that the controls and the states are all in their given constraint scopes, and velocity and altitude track the reference signals accurately in steady state even under mismatched disturbances. Compared with pure model predictive control, the proposed method provides stronger robustness against various perturbations.

Finite-time stability for impulsive switched delay systems with nonlinear disturbances

This paper investigates finite-time stability (FTS) for impulsive switched delay systems which allow the disturbances may have stronger nonlinearities. Based on novel inequalities technique and average dwell time approach, several stability criteria are established to guarantee that the state trajectory of the system does not exceed a certain threshold over a pre-specified finite time interval. Two examples are also provided to illustrate the effectiveness of the theoretical results.

Asymptotic behavior of switched delay systems with nonlinear disturbances

The objective of this paper is to study the asymptotic behavior of switched delay systems with nonlinear disturbances. By establishing a new delay Gronwall-Bellman integral inequality and an elementary inequality, we obtain some asymptotic results for the systems under arbitrary switching laws, which extend some existing results in the literature to the more general nonlinear case. Finally, two examples are provided to illustrate the effectiveness of our results.

Likelihood-based iteration square-root cubature Kalman filter with applications to state estimation of re-entry ballistic target

A new algorithm named the likelihood-based iteration square-root cubature Kalman filter (LISRCKF) is provided in this study. The LISRCKF inherits the virtues of the square-root cubature Kalman filter (SRCKF), which uses the cubature rule-based numerical integration method to calculate the mean and square root of covariance for the non-linear random function. The LISRCKF involves the use of the iterative measurement update and the use of the latest measurement, and the iteration termination criterion based on maximum likelihood is introduced in the measurement update. The LISRCKF algorithm is applied to the state estimation for re-entry ballistic target with unknown ballistic coefficient. Its performance is compared against that of the unscented Kalman filter and SRCKF. Moreover, the suitable choice of iteration number is studied; iteration number 5 is the most appropriate for the LISRCKF algorithm. Simulation results indicate that the LISRCKF algorithm has the features of short run time and fast convergence rate; the advantage in robustness is also demonstrated through the numerical simulation, and it is an effective state estimation method.