Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and motion planning of autonomous vehicles. This paper proposes a graph-based spatial-temporal convolutional network (GSTCN) to predict future trajectory distributions of all neighbor vehicles using past trajectories. This network tackles spatial interactions using a graph convolutional network (GCN), and captures temporal features with a convolutional neural network (CNN). The spatial-temporal features are encoded and decoded by a gated recurrent unit (GRU) network to generate future trajectory distributions. Besides, we propose a weighted adjacency matrix to describe the intensities of mutual influence between vehicles, and the ablation study demonstrates the effectiveness of our scheme. Our network is evaluated on two real-world freeway trajectory datasets: I-80 and US-101 in the Next Generation Simulation (NGSIM). Comparisons in three aspects, including prediction errors, model sizes, and inference speeds, show that our network can achieve state-of-the-art performance.

This brief examines bipartite consensus problem on matrix-valued weighted networks. Firstly, it is shown that such networks achieve bipartite consensus if and only if the null space of the matrix-valued weighted Laplacian is spanned by a matrix-valued Gauge transformation of the vector of ones, extending results for scalar-valued weighted networks. Secondly, it is shown that if a structurally balanced matrix-valued weighted network has a “positive-negative spanning tree”, then the bipartite consensus can be achieved. Lastly, we show that in the case where edges are weighted by either positive definite or negative definite matrices, bipartite consensus is achieved if and only if the network is structurally balanced. Simulation results are provided to demonstrate these theoretical observations.

The iterative learning control (ILC) combining with model predictive control (ILC-MPC) is an effective control method for constrained batch processes. However, in real applications, model uncertainty usually makes it slow for the controlled process to converge to the reference trajectory. To eliminate the restrictions in previous works, a data-driven approach is proposed, which directly describes the relationship between inputs and outputs according to the past data. Based on this method, a novel data-driven ILC-MPC controller is proposed, where the two-mode framework and the invariant updating strategy are employed to guarantee the convergence. Since the outputs caused by model uncertainty are partly known from the past data, the better performance can be achieved by the proposed design which is verified by experimental studies on a manipulator.

This article examines the consensus problem on matrix-weighted undirected switching networks. First, we introduce the matrix-weighted integral network for analyzing such networks. Under mild assumptions on the switching pattern of a sequence of networks, conditions under which average consensus can be achieved are then provided. It is shown that for matrix-weighted switching networks, the existence of a positive spanning tree in the associated integral network plays a crucial role in achieving average consensus. In particular, for periodic matrix-weighted switching networks, necessary and sufficient conditions for reaching average consensus is obtained from an algebraic perspective. Simulation results are provided to demonstrate the theoretical analysis.

To cope with input delay involved with many industrial system operations, a predictor-based disturbance rejection control (PDRC) scheme is proposed in discrete-time domain for sampling implementation, which can be generally applied for open-loop stable, integrating, and unstable processes. An extended state observer (ESO) is introduced to estimate not only the deterministic system state but also the generalized disturbance composed of system uncertainties and external disturbance, based on a filtered Smith predictor to estimate the delay-free output response. Correspondingly, by specifying the desired poles of ESO and the closed-loop control system, the ESO gain vector and the PDRC controller are analytically derived. A notable merit is that there is a single tuning parameter in the proposed ESO, output predictor, and the PDRC controller, which can be monotonically tuned to achieve a good tradeoff between the prediction (or control) performance and its robustness. A sufficient robust stability condition of the closed-loop system is established in terms of linear matrix inequality. An illustrative example from the literature along with an application to the temperature control system for a crystallization reactor is used to demonstrate the effectiveness and advantage of the proposed control method.

Effective traffic signal control strategies are critical for traffic management in urban traffic networks. Most existing optimization-based urban traffic control approaches update the traffic signal at regular time instants, where the length of the fixed update time interval is determined based on a trade-off between the computational efficiency and the control performance. Since event-triggered control (ETC) allows for more flexible and more efficient control than conventional time-triggered control by triggering the control action by events, and since it can refrain from redundant optimization while retaining a satisfactory behavior, we use an ETC scheme for traffic light control. In addition, based on the geographically distributed feature of traffic networks, a distributed paradigm is adopted to reduce the computational complexity for the optimization. We propose a distributed threshold-based event-triggered control strategy, where the independent triggering of agents leads to an asynchronous update of traffic signals in the system. The triggered agent then solves a mixed-integer linear programming problem and updates its traffic signals. The proposed approach is evaluated under various traffic demands by simulation, and is shown to yield the best trade-off between control performance and computational complexity compared to other control strategies.