This paper investigates the problem of state estimation for guarded semi-Markov switching systems with soft constraints. We first construct the switching system in a multiple model manner and derive the recursive Bayesian filter compatible with the sojourn time and base state dependent mode transitions. To solve the intractable conditioned mode transition probabilities, we develop deep learning based classifiers with long short-term memory networks capturing the temporal dependencies. Various network structures are designed to handle different situations where knowledge of the transition probability matrix is available or deficient. Furthermore, we incorporate sequential Monte Carlo techniques into the multiple model framework to resolve the local filtering task. A novel particle refinement procedure exploiting the constraint information is proposed to improve the efficiency of particle propagation and prevent the exponential increase of particle number simultaneously. Simulations on a robotic manipulator and an autonomous vehicle tracking task validate the effectiveness of the proposed state estimation method.

This paper discusses a learning algorithm for nonlinear aircraft systems, which targets the weaknesses of datadriven algorithms, mainly poor generalization ability and limited interpretability. It handles these constraints by integrating physical information with data-driven techniques. Referred to as the Physics-Informed SINDY (PI-SINDY) framework in this paper, it improves the standard SINDY algorithm, to tackle strongly time-varying nonlinear flight systems. This method incorporates the physical information described by the aircraft's differential kinematic equations into the SINDY algorithm, and also can deal with the effects of measurement noise, making it more robust and practical. The proposed method displays higher robustness and generalization ability in comparison with the original SINDY algorithm and the WSINDy method, as confirmed by simulation results. Finally, we use the nonlinear system model learned with the suggested method for tracking control to supplement its efficiency.

Morphing aircraft are capable of modifying their geometry configurations according to different flight conditions to improve their performance, such as by increasing the lift-to-drag ratio or reducing their fuel consumption. In this article, we focus on the airfoil morphing of wings and propose a novel morphing control method for an asymmetric deformable airfoil based on deep reinforcement learning approaches. Firstly, we develop an asymmetric airfoil shaped using piece-wise Bézier curves and modeled by shape memory alloys. Resistive heating is adopted to actuate the shape memory alloys and realize the airfoil morphing. With regard to the hysteresis characteristics exhibited in the phase transformation of shape memory alloys, we construct a second-order Markov decision process for the morphing procedure to formulate a reinforcement learning environment with hysteresis properties explicitly considered. Subsequently, we learn the morphing policy based on deep reinforcement learning techniques where the accurate information of the system model is unavailable. Lastly, we conduct simulations to demonstrate the benefits brought by our learning implementations and validate the morphing performance of the proposed method. The simulation results show that the proposed method provides an average 29.8% performance improvement over traditional methods.

This study proposes a novel data fusion algorithm in sensor networks with simultaneous presence of set‐membership and stochastic Gaussian measurement uncertainties. The proposed method is grounded in the marriage of ellipsoidal calculus theory and data compression algorithm. The point‐valued measurement and the set‐valued measurement are compressed into a uniform framework during the estimation. An optimal Kalman gain is obtained that minimises the upper bound of the mean square error of the estimation set. The proposed algorithm is applied to the target tracking problem and the estimation results show that the proposed algorithm improves the tracking performance.

An incremental deep deterministic policy gradient (IDDPG) algorithm is devised for the trajectory tracking control of a four-wing variable sweep (FWVS) aircraft with uncertainty. The IDDPG algorithm employs the line-of-sight (LOS) method for path tracking, formulates a reward function based on position and attitude errors, and integrates long short-term memory (LSTM) units into IDDPG algorithm to enhance its adaptability to environmental changes during flight. Finally, environmental disturbance factors are introduced in simulation to validate the designed controller’s ability to track climbing trajectories of morphing aircraft in the presence of uncertainty.