Demin Xu

Intelligent Online Path Planning for UAVs in Adversarial Environments

Online path planning (OPP) for unmanned aerial vehicles (UAVs) is a basic issue of intelligent flight and is indeed a dynamic multi-objective optimization problem (DMOP). In this paper, an OPP framework is proposed in the sense of model predictive control (MPC) to continuously update the environmental information for the planner. For solving the DMOP involved in the MPC we propose a dynamic multi-objective evolutionary algorithm based on linkage and prediction (LP-DMOEA). Within this algorithm, the historical Pareto sets are collected and analysed to enhance the performance. For intelligently selecting the best path from the output of the OPP, the Bayesian network and fuzzy logic are used to quantify the bias to each optimization objective. The DMOEA is validated on three benchmark problems characterized by different changing types in decision and objective spaces. Moreover, the simulation results show that the LP-DMOEA overcomes the restart method for OPP. The decision-making method for solution selection can assess the situation in an adversarial environment and accordingly adapt the path planner.

Intelligent flight for UAV via integration of dynamic MOEA, Bayesian network and fuzzy logic

Intelligent flight is a key technology for an unmanned aerial vehicle (UAV) to react to the changing environment. Online path planning (OPP) is a basic issue for intelligent flight and is indeed a dynamic multi-objective optimization problem (DMOP). In this paper, we use an OPP scheme in the sense of model predictive control to continuously update the environmental information for the planner. This method is in fact a DMOP. For solving the problem at hand we propose a dynamic multi-objective evolutionary algorithm based on linkage and prediction (LP-DMOEA).Within this algorithm the historical Pareto sets are collected and analyzed to enhance the performance. For intelligently selecting the best path from the output (a set of Pareto solutions obtained by the LP-DMOEA) of the OPP, the Bayesian network and fuzzy logic are used to quantify the bias to each optimal objective. The experimental results show the LP-DMOEA works more effectively for OPP in contrast to the restart method and the intelligent methods for solution selection can automatically assess the changing environment and adapt the path planner.

A Sampling-Based Bayesian Approach for Cooperative Multiagent Online Search With Resource Constraints

This paper presents a cooperative multiagent search algorithm to solve the problem of searching for a target on a 2-D plane under multiple constraints. A Bayesian framework is used to update the local probability density functions (PDFs) of the target when the agents obtain observation information. To obtain the global PDF used for decision making, a sampling-based logarithmic opinion pool algorithm is proposed to fuse the local PDFs, and a particle sampling approach is used to represent the continuous PDF. Then the Gaussian mixture model (GMM) is applied to reconstitute the global PDF from the particles, and a weighted expectation maximization algorithm is presented to estimate the parameters of the GMM. Furthermore, we propose an optimization objective which aims to guide agents to find the target with less resource consumptions, and to keep the resource consumption of each agent balanced simultaneously. To this end, a utility function-based optimization problem is put forward, and it is solved by a gradient-based approach. Several contrastive simulations demonstrate that compared with other existing approaches, the proposed one uses less overall resources and shows a better performance of balancing the resource consumption.

Aperiodic Robust Model Predictive Control for Constrained Continuous-Time Nonlinear Systems: An Event-Triggered Approach

The event-triggered control is a promising solution to cyber-physical systems, such as networked control systems, multiagent systems, and large-scale intelligent systems. In this paper, we propose an event-triggered model predictive control (MPC) scheme for constrained continuous-time nonlinear systems with bounded disturbances. First, a time-varying tightened state constraint is computed to achieve robust constraint satisfaction, and an event-triggered scheduling strategy is designed in the framework of dual-mode MPC. Second, the sufficient conditions for ensuring feasibility and closed-loop robust stability are developed, respectively. We show that robust stability can be ensured and communication load can be reduced with the proposed MPC algorithm. Finally, numerical simulations and comparison studies are performed to verify the theoretical results.

Robust self-triggered min–max model predictive control for discrete-time nonlinear systems

Abstract In this paper, we propose a robust self-triggered model predictive control (MPC) algorithm for constrained discrete-time nonlinear systems subject to parametric uncertainties and disturbances. To fulfill robust constraint satisfaction, we take advantage of the min–max MPC framework to consider the worst case of all possible uncertainty realizations. In this framework, a novel cost function is designed based on which a self-triggered strategy is introduced via optimization. The conditions on ensuring algorithm feasibility and closed-loop stability are developed. In particular, we show that the closed-loop system is input-to-state practical stable (ISpS) in the attraction region at triggering time instants. In addition, we show that the main feasibility and stability conditions reduce to a linear matrix inequality for linear case. Finally, numerical simulations and comparison studies are performed to verify the proposed control strategy.

Fault isolation of thrusters under redundancy in frame-structure unmanned underwater vehicles

This article deals with fault isolation issue for the redundant thrusters of frame-structure unmanned underwater vehicles (UUVs). Consistency check is adopted to accomplish this task while solving the reformed control input equations that are produced after getting rid of some fault-free terms from the given equations. Specially selected column vectors from the given control matrix together with the corresponding hypothetical thrust output faults are taken as the known/unknown elements for these equations. Redundant relations among the thrusters support the vector selection, which are revealed by analyzing the maximally linearly independent vectors of the given control matrix. Simulation with faulty thrusters under redundancy in a frame-structure unmanned underwater vehicle illustrates the effectiveness of the proposed methodologies.

Robust event-triggered model predictive control for straight-line trajectory tracking of underactuated underwater vehicles

This paper considers the straight-line trajectory tracking control problem of a class of underactuated underwater vehicles with bounded disturbances and hard input constraints. A constrained robust straight-line tracking controller based on model predictive control (MPC) is designed, and an event-triggered scheduling strategy is incorporated to alleviate the computation load of MPC. Furthermore, sufficient conditions for ensuring feasibility and closed-loop stability of the event-triggered MPC strategy are presented. Finally, numerical simulations demonstrate the efficacy of the proposed controller.

Evolutionary Algorithms for the Multiple Unmanned Aerial Combat Vehicles Anti-ground Attack Problem in Dynamic Environments

This chapter aims to solve the online path planning (OPP) and dynamic target assignment problems for the multiple unmanned aerial combat vehicles (UCAVs) anti-ground attack task using evolutionary algorithms (EAs). For the OPP problem, a model predictive control framework is adopted to continuously update the environmental information for the planner. A dynamic multi-objective EA with historical Pareto set linkage and prediction is proposed to optimize in the planning horizon. In addition, Bayesian network and fuzzy logic are used to quantify the bias value to each optimization objective so as to intelligently select an executive solution from the Pareto set. For dynamic target assignment, a weapon target assignment model that considers the inner dependence among the targets and the expected damage type is built up. For solving the involved dynamic optimization problems, an environment identification based memory scheme is proposed to enhance the performance of estimation of distribution algorithms. The proposed approaches are validated via simulation with a scenario of suppression of enemy air defense mission.

Application of CAN-bus to the control system of the TESTBED AUV

A new distributed control system using CAN-bus for our long-range autonomous underwater vehicle named TESTBED is developed. In the well defined hierarchical architecture, this control system consists of a task layer with the multi-function Tele-operator, a coordination layer with the Mission Management Center, and a control layer with the Navigation and Control Center and other execution-level controllers. All the control nodes communicate with each other through the CAN bus with the proper defined message frames. The real-time control and non-realtime tasks management are assigned to different control nodes, which make the whole system more reliable, maintainable, and easy to upgrade. In the real in-water experiments, the distributed control system fulfilled the multi-waypoints tasks, and the AUV executed the preprogrammed mission completely without any fault.