This paper presents a systematic control system design for nano-positioning of a piezoelectric actuator (PEA). PEAs exhibit hysteresis nonlinearity, which can dramatically limit the application and performance of linear feedback control theory. Thus the hysteresis is compensated for based on the Maxwell resistive capacitor (MRC) model first. Then a proportional plus integral (PI) controller and a proportional double integral plus lead compensation (PII&L) controller are designed for the hysteresis-compensated PEA to account for model uncertainty, disturbance, and noise. The robust stability of both controllers is proved. The effectiveness of the proposed control scheme is demonstrated experimentally. Both controllers achieve fast precise positioning. The 2% settling times for the PI controller and the PII&L controller are 1.5 ms and 4.7 ms, respectively. The positioning resolution is upto 1 nm for both controllers.

This paper addresses the problem of the fixed-time cooperative guidance (FxTCG) law for multi-missiles against a single maneuvering target with constraints of both the interception time and the line-of-sight (LOS) angle. Firstly, by utilizing the consensus protocol and the fixed-time consensus theory, a new FxTCG in the LOS direction is presented to guarantee that the multiple missiles intercept the incoming target synchronously. Moreover, the acceleration of the target causes disturbance, so a simple fixed-time disturbance observer (FxTDO) is introduced to compensate FxTCG with the disturbance estimation. Subsequently, the FxTCG in the normal direction of the LOS is designed based on an adaptive fixed-time convergence reaching law and the proposed FxTDO, which ensures the fixed-time convergence of the LOS angular velocity and LOS angle between each missile and the maneuvering target. Finally, numerical simulations and adequate analyses are carried out to illustrate the accuracy, effectiveness, and robustness of the proposed FxTCG scheme.

Trajectory optimization of hypersonic glide vehicle (HGV) has highly nonlinear model and complex constraints. Therefore, it is difficult to deal with polygonal no-fly zone (PNFZ) constraints which are common in actual flight environment of HGV. To solve this problem, this article proposes a general PNFZ avoidance (GPNA) algorithm to solve HGV trajectory optimization problem with general PNFZ (convex and concave PNFZs) constraints. To the author's knowledge, this problem has never been solved before. First, trajectory optimization problem with convex PNFZ constraint is considered. Inspired by sequential convex programming (SCP), direct linearization and relaxation technique are used to transform this problem into a sequence of mixed-integer linear programming (MILP) problems, which can be solved efficiently by state-of-the-art software. Different from the convexification process of SCP, this article also linearizes the nonlinear convex functions. At the same time, PNFZs of actual mission are usually concave, so concave PNFZ is also considered. Convex decomposition technique is used to decompose it into multiple convex PNFZs, so that GPNA algorithm can solve more practical problems. In addition, we also consider inter-sample obstacle avoidance constraints. Simulations in various scenarios verify the effectiveness of GPNA algorithm. GPNA algorithm has strong adaptability and high computational efficiency, and can generate trajectories avoiding general PNFZ within 10 seconds, which has strong application value.

This article presents a fixed-time active disturbance rejection control approach for the attitude control problem of quadrotor unmanned aerial vehicle in the presence of dynamic wind, mass eccentricity and an actuator fault. The control scheme applies the feedback linearization technique and enhances the performance of the traditional active disturbance rejection control (ADRC) based on the fixed-time high-order sliding mode method. A switching-type uniformly convergent differentiator is used to improve the extended state observer for estimating and attenuating the lumped disturbance more accurately. A multivariable high-order sliding mode feedback law is derived to achieve fixed time convergence. The timely convergence of the designed extended state observer and the feedback law is proved theoretically. Mathematical simulations with detailed actuator models and real time experiments are performed to demonstrate the robustness and practicability of the proposed control scheme.

This article researches synergistic planning for multiple hypersonic vehicles. Taking significant differences in vehicle parameters and synergistic demands into account, it is significantly challenging for computing a series of trajectories simultaneously in an efficient way. This challenge will become severer when there exists a high degree of nonlinear coupling between dynamics, thermal and load factors, constraints, and objective functions. To tackle with this problem, our proposal begins with a profile, which features natural satisfaction of terminal states, nofly zones, and waypoints. Meanwhile, we research and conduct analytic dynamics using this profile for relieving the coupling. Then, we formulate a straightforward parameter-searching model to tackle the synergistic problem. In typical synergistic scenarios, this model can comprise more than thirty design variables, which further leads to challenges in initial guess, global searching, and convergence. Therefore, we propose a hybrid algorithm that combines particle swarm optimization (PSO) with reinforcement learning (RL). PSO is independent of initial guess and suitable for searching in high dimensional spaces, but is typically lack of efficiency. While a RL strategy is featured with immediate network computation and PSO global searching after training of PSO-based offline synergistic trajectories. In the end, we carry out simulation in different synergistic scenarios for validating the effectiveness and robustness of our algorithm.