Xiaoxiang Hu

A fault-tolerant controller design for reusable launch vehicles (RLVs) is discussed in this paper. The control precision of RLVs is very important, since it must be ensured that an RLV’s speed reaches zero while flying to the target point. More seriously, the rocket’s thrust system may suffer from faults, so the fault-tolerant control of RLVs is very important. The landing dynamic model of RLVs is very complex, and the thrust is coupled with time-varying states, which make the controller design of RLVs very difficult. Based on the specific control requirements of rocket landing, the control design problem is first transformed into a normal model in this paper. Then, considering potential thrust faults, an optimal fault-tolerant controller is designed using reinforcement learning. Considering sensor faults and actuator faults, this paper presents the corresponding fault-tolerant controller design method. Considering that the analytical problem of the proposed fault-tolerant controller is difficult to solve, this paper presents an approximation method for the analytical solution based on a neural network. The simulation results demonstrate that the proposed controller ensures the safe and stable landing of the rocket in both nominal and fault scenarios.

This article proposes a unified suboptimal controller design method for unknown general nonlinear systems subject to multiple constraints, including state, input, and output constraints. All inequality constraints are transformed into equality constraints using slack functions and Pade approximation. An unconstrained augmented system is then defined to describe the dynamics of original system and the equality constraints, where the optimal controller of the augmented system can be viewed as a suboptimal controller for the original system. Furthermore, considering the unmodeled dynamics of the original system, neural networks (NNs) are utilized and a data-based solution strategy of integral reinforcement learning (IRL) is presented for the augmented system. Ultimately, the simulation results are given to reflect the effectiveness of the unified design method.

Currently, most optimizes on large-scale optimization problems (LSOPs) rely on random search without directional guidance. This leads to two problems: insufficient optimization accuracy and reliance on simulations to evaluate the algorithm. Inspiblack by the competitive swarm optimizer (CSO), the swarm intelligence-based intergroup cross-learning swarm optimizer (ICLSO) is proposed to address the problems. A six-particle subpopulation is used as the evolutionary unit in the ICLSO. Based on this, a cross-learning mechanism is designed to generate directional guidance for competition. As a result, the swarm diversity and swarm convergence can be maintained more accurately, and the time complexity is smaller. Moreover, a quantitative evolutionary efficiency calculation method is proposed based on search dynamics for the first time, and the ICLSO is proven to outperform existing advanced algorithms. Numerical simulations based on the CEC benchmark set and the Wireless Sensor Network (WSN) power-control benchmark set verified the effectiveness and engineering application potential of the ICLSO.

A suboptimal tracking controller is designed for hypersonic flight vehicle (HFV) subject to input delay, input amplitude constraint, and input rate constraint in this article. The input delay, input amplitude constraint, and input rate constraint are all caused by physical constraints of HFV, and are inevitable, so they must be considered in controller design. The model of input delay, input amplitude constraint and input rate constraint are first developed and then they are all transformed into inequality constraints. With considered the inequality constraints, precompensation method is utilized and then the tracking controller design problem for HFV with inequality constraints is transformed into controller design of unconstrained augmented system. The optimal controller for the unconstrained augmented system can be viewed as suboptimal controller for HFV. Then with considered the advantage of reinforcement learning (RL), an online policy‐iteration (PI) algorithm for the optimal controller of the unconstrained augmented system is given. Furthermore, considering the uncertainty and unmodeled dynamics of HFV, neural networks (NN) are utilized and a data‐based solution strategy of integral reinforcement learning (IRL) is presented for the unconstrained augmented system. Simulation results on the nonlinear model of HFV are given to reflect the effectiveness of the designed method.

This paper proposes a novel framework for the online fault-tolerant control of hypersonic flight vehicles (HFV). The framework contains two steps. Firstly, based on the Internet of Things (IoT) and digital twin (DT) technology, a hypersonic flight vehicle digital twin (HFVDT) model is constructed. This HFVDT model can represent the real model and the parameter changes in HFV in real-time, and can update itself by the designed updating law through the flight data acquired by IoT. Then, the model changes caused by a fault can be fed back in real-time. Based on the real-time model, a model predictive static programming (MPSP) based controller design method is proposed to solve the online fault-tolerant control problem of HFV. MPSP provides a feasible online solution to optimal control problems with constraints. By converting the nonlinear optimal control problem to a static optimization the problem, the quantity of computation is greatly reduced, and then the optimal can be solved online. By the HFVDT and MPSP framework, the model change can be monitored and then an optimal controller can be constructed in real time. In this case, the fault-tolerant control problem of HFV can solved and the tracking performance is guaranteed. Finally, the novel framework is carried out on a numeral simulation to show its effectiveness.