Neural Adaptive Distributed Formation Control of Nonlinear Multi-UAVs With Unmodeled Dynamics

Deep Progressive Reinforcement Learning-Based Flexible Resource Scheduling Framework for IRS and UAV-Assisted MEC System

The intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is widely used in temporary and emergency scenarios. Our goal is to minimize the energy consumption of the MEC system by jointly optimizing UAV locations, IRS phase shift, task offloading, and resource allocation with a variable number of UAVs. To this end, we propose a flexible resource scheduling (FRES) framework by employing a novel deep progressive reinforcement learning that includes the following innovations. First, a novel multitask agent is presented to deal with the mixed integer nonlinear programming (MINLP) problem. The multitask agent has two output heads designed for different tasks, in which a classified head is employed to make offloading decisions with integer variables while a fitting head is applied to solve resource allocation with continuous variables. Second, a progressive scheduler is introduced to adapt the agent to the varying number of UAVs by progressively adjusting a part of neurons in the agent. This structure can naturally accumulate experiences and be immune to catastrophic forgetting. Finally, a light taboo search (LTS) is introduced to enhance the global search of the FRES. The numerical results demonstrate the superiority of the FRES framework, which can make real-time and optimal resource scheduling even in dynamic MEC systems.

Interleaved Periodic Event-Triggered Communications-Based Distributed Formation Control for Cooperative Unmanned Surface Vessels

This article addresses the distributed formation control issue of cooperative unmanned surface vessels (USVs) under interleaved periodic event-triggered communications. First, an adaptive event-based control protocol is designed, where the event-based neural network (NN) scheme is developed to compensate for uncertain model dynamics. Upon the designed control protocol, an interleaved periodic event-triggered mechanism (IPETM) is subsequently proposed to achieve the communication objective. Unlike the common continuous event-triggered methods and periodic event-triggered methods, in which multiple nodes are allowed to trigger their events at the same time, the proposed IPETM ensures that USVs detect their events at different times to avoid the simultaneous event triggering of different nodes. By this virtue, traffic jamming in common wireless environments can be prevented, such that potential communication delays and faults are naturally avoided. In addition, the event detecting instants of the presented IPETM are also discrete and periodic, such that it can be performed under low-computational frequencies. Through Lyapunov-based analysis, it is verified that all closed-loop signals can converge to an arbitrary small compact set with exponential convergence rates. Simulation results demonstrate the effectiveness and superiority of the proposed control scheme.

Reinforcement Learning-Based Fractional-Order Adaptive Fault-Tolerant Formation Control of Networked Fixed-Wing UAVs With Prescribed Performance

This article investigates the fault-tolerant formation control (FTFC) problem for networked fixed-wing unmanned aerial vehicles (UAVs) against faults. To constrain the distributed tracking errors of follower UAVs with respect to neighboring UAVs in the presence of faults, finite-time prescribed performance functions (PPFs) are developed to transform the distributed tracking errors into a new set of errors by incorporating user-specified transient and steady-state requirements. Then, the critic neural networks (NNs) are developed to learn the long-term performance indices, which are used to evaluate the distributed tracking performance. Based on the generated critic NNs, actor NNs are designed to learn the unknown nonlinear terms. Moreover, to compensate for the reinforcement learning errors of actor-critic NNs, nonlinear disturbance observers (DOs) with skillfully constructed auxiliary learning errors are developed to facilitate the FTFC design. Furthermore, by using the Lyapunov stability analysis, it is shown that all follower UAVs can track the leader UAV with predesigned offsets, and the distributed tracking errors are finite-time convergent. Finally, comparative simulation results are presented to show the effectiveness of the proposed control scheme.

Composite Learning Adaptive Tracking Control for Full-State Constrained Multiagent Systems Without Using the Feasibility Condition

This article proposes a distributed consensus tracking controller for a class of nonlinear multiagent systems under a directed graph, in which all agents are subject to time-varying asymmetric full-state constraints, internal uncertainties, and external disturbances. The feasibility condition generally required in the existing constrained control is removed by using the proposed nonlinear mapping function (NMF)-based state reconstruction technology, and the Lipschitz condition usually needed in the consensus tracking is also canceled based on the adaptive command-filtered backstepping framework. The composite learning of the neural network-based function approximator (NN-FAP) and the finite-time smooth disturbance observer (DOB) provides a novel scheme for handling internal and external uncertainties simultaneously. One advantage of this scheme is that the use of online historical data of the closed-loop system strengthens the excitation of NN's learning. Another advantage is that the DOB with NN-FAP embedding realizes that the finite-time observation for external disturbance in the case of the system dynamics is unknown. A complete controller design, sufficient stability analysis, and numerical simulation are provided.

Switching ETM-based neural adaptive output feedback control for nonaffine stochastic MIMO nonlinear systems subject to deferred constraint

This article focuses on the neural adaptive output feedback control study related to nonaffine stochastic multiple-input, multiple-output nonlinear plants. First, a K-filter state observer based on a radial basis function neural network is designed to estimate the remaining unavailable states. Then, a novel adaptive command-filtered backstepping output feedback control framework is established, where an improved command filter with a fractional-order parameter is applied to conquer the calculation size problem. Specifically, the highlight of this work is that it designs a modified error compensation signal and incorporates the concept of deferred constraint to eradicate the negative effect caused by the filter errors. In addition, the network bandwidth resources, control impulse, and control accuracy are synthesized using an amended switching event-triggered mechanism. The theoretical analysis proved that the proposed control approach guarantees that the tracking error can converge to a preassigned region within a user-defined time while the violation of the deferred output constraint can be excluded. Two illustrative studies are provided to demonstrate the validity and superiority of the developed control method.

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

Unmanned Aerial Vehicles (UAVs) are important tool for various applications, including enhancing target detection accuracy in various surface-to-air and air-to-air missions. To ensure mission success of these UAVs, a robust control system is needed, which further requires well-characterized dynamic system model. This paper aims to present a consolidated framework for the estimation of an experimental UAV utilizing flight data. An elaborate estimation mechanism is proposed utilizing various model structures, such as Autoregressive Exogenous (ARX), Autoregressive Moving Average exogenous (ARMAX), Box Jenkin’s (BJ), Output Error (OE), and state-space and non-linear Autoregressive Exogenous. A perspective analysis and comparison are made to identify the salient aspects of each model structure. Model configuration with best characteristics is then identified based upon model quality parameters such as residual analysis, final prediction error, and fit percentages. Extensive validation to evaluate the performance of the developed model is then performed utilizing the flight dynamics data collected. Results indicate the model’s viability as the model can accurately predict the system performance at a wide range of operating conditions. Through this, to the best of our knowledge, we present for the first time a model prediction analysis, which utilizes comprehensive flight dynamics data instead of simulation work.