This article focuses on the dynamic event-triggered gain-scheduled H∞ control problem for a polytopic linear parameter-varying (LPV) model of morphing aircraft while undergoing wingspan deformation. First, an LPV model of morphing aircraft is transformed into the polytopic form, which can avoid the infinite-dimensional linear matrix inequality (LMI) problem arising from the continuity of the scheduling parameter. Then, a gain-scheduled control algorithm is proposed to ensure the robust stability of morphing aircraft with H∞ performance, where the control gains are obtained by the derived LMI constraints. Further, a dynamic event-triggered mechanism is presented to determine the update time of the controller, and Zeno behavior can be eliminated by contradiction. By designing the internal dynamic variable, a dynamic event-triggered scheme extends the trigger interval compared to a static event-triggered scheme, leading to decreased communication transmission frequency and control consumption. Finally, the simulations are showcased to demonstrate the effectiveness of the developed control strategy

Aiming at the cooperative guidance problem of multi-hypersonic glide vehicles, a cooperative guidance method based on a parametric design and an analytical solution of time-to-go is proposed. First, the hypersonic reentry trajectory optimization problem was transformed into a parameter optimization problem. The parameters were optimized to determine the angle of attack profile and the time to enter the altitude velocity reentry corridor. Then, using the quasi-equilibrium glide condition, the estimation form of the remaining flight time was analytically derived to satisfy accurately the cooperative time constraint. Using the remaining time-to-go and range-to-go, combined with the heading angle deviation corridor, the bank angle command was further calculated. Finally, the swarm intelligence optimization algorithm was used to optimize the design parameters to obtain the cooperative guidance trajectory satisfying the time constraint. Simulations showed that the analytical time reentry cooperative guidance algorithm proposed in this paper can accurately meet the time constraints and cooperative flight accuracy. Monte Carlo simulation experiments verified that the proposed algorithm demonstrates a robust performance.

This paper is concerned with the problem of gain‐scheduled ℋ₂ controller synthesis for continuous‐time linear parameter‐varying systems. In this problem, the system matrices in the state‐space form are polytopic and patameterized and the admissible values of the parameters are assumed to be measurable on‐line in a polytope space. By employing a basis‐parameter‐dependent Lyapunov function and introducing some slack variables to the well‐established performance conditions, sufficient conditions for the existence of the desired gain‐scheduled ℋ₂ state feedback and dynamic output feedback controllers are established in terms of parameterized linear matrix inequalities. Based on the polytopic characteristic of the dependent parameters and a convexification method, the corresponding controller synthesis problem is then cast into finite‐dimensional convex optimization problem which can be efficiently solved by using standard numerical softwares. Numerical examples are given to illustrate the effectiveness and advantage of the proposed methods.

An unmanned surface vehicles (USV) set point tracking problem is investigated in this paper. The stochastic model predictive control (SMPC) scheme is utilized to design the controller in order to reject the environment disturbances and meet the physical constraints. The design problem is formulated as a chance-constrained stochastic optimization problem, which is non-convex. Thus, the problem is computationally prohibitive. For this, the convex conditional value at risk (CVaR) approximation is introduced to convert the chance constraints into deterministic convex constraints. The converted constraints are then further transformed into the second order cone (SOC) constraints. Therefore, the proposed method is computationally tractable and hence can be implemented online. A numerical example is provided to illustrate the effectiveness of the proposed method.

A micro-inertial measurement unit (MIMU) is usually used to sense the angular rate and acceleration of the flight carrier. In this study, multiple MEMS gyroscopes were used to form a spatial non-orthogonal array to construct a redundant MIMU system, and an optimal Kalman filter (KF) algorithm was established by a steady-state KF gain to combine array signals to improve the MIMU’s accuracy. The noise correlation was used to optimize the geometric layout of the non-orthogonal array and reveal the mechanisms of influence of correlation and geometric layout on MIMU’s performance improvement. Additionally, two different conical configuration structures of a non-orthogonal array for 4,5,6,8-gyro were designed and analyzed. Finally, a redundant 4-MIMU system was designed to verify the proposed structure and KF algorithm. The results demonstrate that the input signal rate can be accurately estimated and that the gyro’s error can also be effectively reduced through fusion of non-orthogonal array. The results for the 4-MIMU system illustrate that the gyro’s ARW and RRW noise can be decreased by factors of about 3.5 and 2.5, respectively. In particular, the estimated errors (1σ) on the axes of X_b, Y_b and Z_b were 4.9, 4.6 and 2.9 times lower than that of the single gyroscope.