With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing, aviation, and healthcare, the data driven vibration control (DDVC) has attracted broad interests from both the industrial and academic communities. Input shaping (IS), as a simple and effective feedforward method, is greatly demanded in DDVC methods. It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure, thereby suppressing the residual vibration separately. Based on a thorough investigation into the state-of-the-art DDVC methods, this survey has made the following efforts: 1) Introducing the IS theory and typical input shapers; 2) Categorizing recent progress of DDVC methods; 3) Summarizing commonly adopted metrics for DDVC; and 4) Discussing the engineering applications and future trends of DDVC. By doing so, this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives, aiming at promoting future research regarding this emerging and vital issue.

Autonomous navigation for intelligent mobile robots has gained significant attention, with a focus on enabling robots to generate reliable policies based on maintenance of spatial memory. In this paper, we propose a learning-based visual navigation pipeline that uses topological maps as memory configurations. We introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity estimation. This tackles the issues of topological node redundancy and incorrect edge connections, which stem from the distribution gap between the spatial and perceptual domains. Furthermore, we propose a differentiable graph extraction structure, the topology multi-factor transformer (TMFT). This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy generation. Results from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory structures. Comprehensive validation through behavior visualization, interpretability tests, and real-world deployment further underscore the adaptability and efficacy of our method.

Battery production is crucial for determining the quality of electrode, which in turn affects the manufactured battery performance. As battery production is complicated with strongly coupled intermediate and control parameters, an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently required. This paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven modelling. To be specific, an explainable neural network named generalized additive model with structured interaction (GAM-SI) is designed to predict two key battery properties, including electrode mass loading and porosity, while the effects of four early production terms on manufactured batteries are explained and analysed. The experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating stages. In addition, the importance ratio ranking, global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural network. Due to the merits of interpretability, the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior, further benefitting smart battery production.

Decomposition of a complex multi-objective optimisation problem (MOP) to multiple simple subMOPs, known as M2M for short, is an effective approach to multi-objective optimisation. However, M2M facilitates little communication/collaboration between subMOPs, which limits its use in complex optimisation scenarios. This paper extends the M2M framework to develop a unified algorithm for both multi-objective and many-objective optimisation. Through bilevel decomposition, an MOP is divided into multiple subMOPs at upper level, each of which is further divided into a number of single-objective subproblems at lower level. Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another, and eventually to all the subMOPs. The bilevel decomposition is readily combined with some new mating selection and population update strategies, leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multi- and many-objective optimisation. Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm.

This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system. A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic efficiency. Firstly a regional multi-agent Q-learning framework is proposed, which can equivalently decompose the global Q value of the traffic system into the local values of several regions. Based on the framework and the idea of human-machine cooperation, a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to real-time traffic flow densities. In order to achieve better cooperation inside each region, a lightweight spatio-temporal fusion feature extraction network is designed. The experiments in synthetic, real-world and city-level scenarios show that the proposed RegionSTLight converges more quickly, is more stable, and obtains better asymptotic performance compared to state-of-the-art models.

This paper studies the target controllability of multi-layer complex networked systems, in which the nodes are high-dimensional linear time invariant (LTI) dynamical systems, and the network topology is directed and weighted. The influence of inter-layer couplings on the target controllability of multi-layer networks is discussed. It is found that even if there exists a layer which is not target controllable, the entire multi-layer network can still be target controllable due to the inter-layer couplings. For the multi-layer networks with general structure, a necessary and sufficient condition for target controllability is given by establishing the relationship between uncontrollable subspace and output matrix. By the derived condition, it can be found that the system may be target controllable even if it is not state controllable. On this basis, two corollaries are derived, which clarify the relationship between target controllability, state controllability and output controllability. For the multi-layer networks where the inter-layer couplings are directed chains and directed stars, sufficient conditions for target controllability of networked systems are given, respectively. These conditions are easier to verify than the classic criterion.