This paper studies the problem of multi-agent trajectory prediction in crowded unknown environments. A novel energy function optimization-based framework is proposed to generate prediction trajectories. Firstly, a new energy function is designed for easier optimization. Secondly, an online optimization pipeline for calculating parameters and agents’ velocities is developed. In this pipeline, we first design an efficient group division method based on Frechet distance to classify agents online. Then the strategy on decoupling the optimization of velocities and critical parameters in the energy function is developed, where the slap swarm algorithm and gradient descent algorithms are integrated to solve the optimization problems more efficiently. Thirdly, we propose a similarity-based resample evaluation algorithm to predict agents’ optimal goals, defined as the target-moving headings of agents, which effectively extracts hidden information in observed states and avoids learning agents’ destinations via the training dataset in advance. Experiments and comparison studies verify the advantages of the proposed method in terms of prediction accuracy and speed. Note to Practitioners—Autonomous robots and vehicles are rapidly integrated into social life and industry, and the scenarios that robots work with multiple people or other moving objects in a crowded environment such as streets and factories will be quite common. One of the most important problems for the robot to solve is the real-time and accurate trajectory prediction of multiple agents around itself to ensure safe navigation. However, existing methods either require prior information to train models or critical parameters in advance or have insufficient prediction accuracy, which are not suitable for robot safe navigation in real applications. In this paper, we investigate the real-time multi-agent trajectory prediction problem for a robot in crowded unknown environments. To obtain the accurate predicted trajectories in real-time, we propose a new energy function optimization-based framework to forecast multi-agent trajectories in crowded unknown scenarios. This framework utilizes the observed data to infer unknown information without the dataset and optimizes the trajectories very efficiently, which can be adopted for robot motion planning and navigation in real-world environments.

Steam, which plays an essential role in heat source medium for heating and cooling, is extensively utilized in industrial parks. Due to the high temperature and pressure characteristics of steam, which has the potential to cause damage to the steam pipeline easily. It is crucial to conduct fault diagnosis for the steam pipeline timely. This article is concerned with the active fault diagnosis (AFD) problem for the steam pipeline with multitime-scale properties. The global consensus AFD method based on the multitime-scale set-membership observer is proposed to solve the issue of inconsistent timescales, which not only achieves state estimation sets under different timescales but also guarantees the global consistency of fault diagnosis results. In view of the leakage fault modeling problem of the steam pipeline, a novel steam transportation model considering the leakage energy loss is established. Moreover, when establishing the optimization problem of the auxiliary signal, original inputs with dynamic characteristics are considered. The key of optimization problem comes down to the separation of healthy and faulty state estimation sets. Finally, experimental results are given to illustrate the effectiveness of the proposed AFD methodology with the steam pipeline data from a steel industrial park.

Many real-world optimization tasks suffer from noise. So far, the research on noise-tolerant optimization algorithms is still restricted to low-dimensional problems with less than 100 decision variables. In reality, many problems are high dimensional. Cooperative coevolutionary (CC) algorithms based on a divide-and-conquer strategy are promising in solving complex high-dimensional problems. However, noisy fitness evaluations pose a challenge in problem decomposition for CC. The state-of-the-art grouping methods, such as differential grouping (DG) and recursive DG, are unable to work properly in noisy environments. Because it is impossible to distinguish whether the change of one variable’s difference value is caused by noise or the perturbation of its interacting variables. As a result, every pair of variables will be identified as nonseparable in these methods. In this article, we study how to group decision variables with the covariance matrix adaptation evolution strategy (CMA-ES) in noisy environments and subsequently propose a landscape-aware grouping (LAG) method. Instead of detecting pairwise interacting variables, we directly identify a nonseparable subcomponent. To this end, we propose to use two convergence features: 1) variable convergence time and 2) accumulative path, to describe variables’ fitness landscapes; then, variables are clustered according to these two features. Numerical experiments show that LAG can more effectively identify interactive decision variables in the presence of multiplicative noise than the DG and some of its variants. Up to 500 dimensions, the performance of CC CMA-ES with landscape-aware grouping (CC-CMAES-LAG) is competitive compared with existing CC algorithms and uncertainty-handling CMA-ES (UH-CMA-ES).

Cooperative co-evolution (CC) algorithm is a promising method to deal with large scale optimization (LSGO) problem. One major challenge in CC is to design a good decomposition strategy to decompose original problem into subproblems. Linkage learning is a technique to search the linkage betweens decision variables. In this paper, we proposed a dynamic online grouping CC algorithm which employ the linkage learning method of extended compact genetic algorithm (ECGA). This linkage learning method build marginal product models of population and search for models according to minimum description length (MDL) criterion. A discretization technique and normalization method of MDL value is used to make the linkage learning more adaptive in large scale optimization. The decision variables will be regrouped according to the linkage learned when the optimization procedure is considered as stagnant. Experiments are conduct on CEC'10 LSGO benchmark function. The test results confirm the validity of this method.

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.

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.

A modulated model predictive control (M2PC) scheme for an indirect matrix converter is proposed in this paper, including an active damping method to mitigate the input filter resonance. The control strategy allows the instantaneous power control and the output current control at the same time, operating at a fixed frequency. An optimal switching pattern is used to emulate the desired waveform quality features of space vector modulation and achieve zero-current switching operations. The active damping technique emulates a virtual resistor which damps the filter resonance. Simulation results present a good tracking to the output-current references, unity input displacement power factor, the low input-current distortions and a reduced common-mode voltage (CMV).