Dewei Li

• PhD
• Professor (Associate) at Shanghai Jiao Tong University

This study proposes a cooperative platoon formation scheme in a multi-lane traffic environment with connected and autonomous vehicles (CAVs). It coordinates the lane-changing decisions and longitudinal trajectories of CAVs to form platoons based on each vehicle’s target lane, aiming to reduce the negative impact of lane-changing maneuvers on traffi...

Recent advances in bipartite consensus on matrix-weighted networks, where agents are divided into two disjoint sets with those in the same set agreeing on a certain value and those in different sets converging to opposite values, have highlighted its potential applications across various fields. Traditional approaches often depend on the existence...

The online computational burden and control performance are two main issues in implementing distributed model predictive control for piecewise affine systems. In the previous research, many methods have been proposed to solve these issues, such as a one‐step distributed model predictive control method that was proposed to reduce the heavy online co...

The standard Kuramoto model has been instrumental in explaining synchronization and desynchronization, two emergent phenomena often observed in biological, neuronal, and physical systems. While the Kuramoto model has turned out effective with one-dimensional oscillators, real-world systems often involve high-dimensional interacting units, such as b...

The coordination of traffic flow among regions is necessary for a large-scale road traffic network to avoid local congestions and improve the overall traffic efficiency. In this paper, by incorporating the random characteristic of traffic flow, we formulate the problem of perimeter traffic flow control for a multi-region traffic network as a Markov...

Batch processes are typically nonlinear systems with constraints. Model predictive control (MPC) and iterative learning control (ILC) are effective methods for controlling batch processes. By combining batch-wise ILC and time-wise MPC, this article proposes a multirate control scheme for constrained nonlinear systems. Two-dimensional (2-D) framewor...

This article examines for the first time an integrated structure of smart injection molding systems (IMS) based on Industry 4.0 technologies and provides a system-level solution for manufacturing smart products. The fully automated smart IMS structure allows manufacturers to produce thermoplastic products directly from raw materials without requiri...

This paper investigates the longitudinal control problem of the mixed platoon consisting of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs) in the multi-lane freeway. In most of the existing studies, the stochastic behavior of HDVs, including lane changing and cut-in, are not considered and the formation of the mixed platoons...

The data-driven sliding mode control (SMC) method proves to be highly effective in addressing uncertainties and enhancing system performance. In our previous work, we implemented a co-design approach based on an input-mapping data-driven technique, which effectively improves the convergence rate through historical data compensation. However, this a...

The classic two-stage object detection algorithms such as faster regions with convolutional neural network features (Faster RCNN) suffer from low speed and anchor hyper-parameter sensitive problems caused by dense anchor mechanism in region proposal network (RPN). Recently, the anchor-free method CenterNet shows the effectiveness of perceiving and...

The unsupervised anomaly detection in high-dimensional and complex settings poses a formidable challenge. To tackle the challenges associated with the recognition of high-dimensional data, this paper proposes a feedback channel between the Variational Auto Encoder and the Gaussian process to enhance its data feature extraction capabilities. In orde...

The positive semidefiniteness of Laplacian matrices is a critical guarantee of the consensus of unsigned multi-agent networks, which is not valid for signed Laplacian matrices. In this paper, we first analyze the stability of signed networks by introducing a novel graph-theoretic concept called negative cut set , which indicates that the existenc...

In this letter, a rigid-soft hybrid robot with visual servoing is designed to improve robotic properties of accuracy and safe interaction, where the hybrid robot is connected by a soft robot and six degrees of freedom rigid robot in series. The series structure of rigid and soft parts brings the coupling and complexity to modeling. For synthesis mo...

The positive/negative definite matrices are strong in the multi-agent protocol in dictating the agents' final states as opposed to the semidefinite matrices. Previous sufficient conditions on the bipartite consensus of the matrix-weighted network are heavily based on the positive-negative spanning tree whereby the strong connections permeate the ne...

Representation learning from unlabeled skeleton data is a challenging task. Prior unsupervised learning algorithms mainly rely on the modeling ability of recurrent neural networks to extract the action representations. However, the structural information of the skeleton data, which also plays a critical role in action recognition, is rarely explore...

A robust tube‐based distributed model predictive control method is proposed for spatially interconnected systems with constraints and disturbances. The system contains multiple discrete‐time piecewise affine subsystems, which are coupled to each other through states. The predictive states of each subsystem are dependent on its states, inputs, and n...

Synchronization and desynchronization are the two ends on the spectrum of emergent phenomena that somehow often coexist in biological, neuronal, and physical networks. However, previous studies essentially regard their coexistence as a partition of the network units: those that are in relative synchrony and those that are not. In real-world systems...

The identification of abnormal data in high-dimensional and high-complexity situations is a challenging subject. In order to improve the accuracy of abnormal data detection, in this article, we first use Variational Auto Encoder (VAE) to extract the features of high-dimensional data to achieve the effect of data dimensionality reduction. Then the G...

This paper discusses optimal batch-to-batch (B2B) control problems and presents a gradient descent method solution for unknown linear batch process systems. Using historical process data, we design a model-free method for B2B optimization that eliminates the need for model information about the system. By using quadratic programming (QP) to formula...

Injection molding, a polymer processing technique that converts thermoplastics into a variety of plastic products, is a complicated nonlinear dynamic process that interacts with a different group of variables, including the machine, the mold, the material, and the process parameters. As injection molding process operates sequentially in phases, we...

This paper studies distributed optimization problem over a fixed network. We develop and analyze an accelerated distributed gradient descent method, named Acc-DGDlm, which utilizes gradient tracking technique and local memory. Specifically, we add two memory slots per agent to store two past estimates, namely, an estimate of the optimal solution an...

Lane change for automated vehicles (AVs) is an important but challenging task in complex dynamic traffic environments. Due to difficulties in guarantee safety as well as a high efficiency, AVs are inclined to choose relatively conservative strategies for lane change. To avoid the conservatism, this paper presents a cooperation-aware lane change met...

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 us...

Achieving average consensus without disclosing sensitive information can be a critical concern for multi-agent coordination. This paper examines privacy-preserving average consensus (PPAC) for vector-valued multi-agent networks. In particular, a set of agents with vector-valued states aim to collaboratively reach an exact average consensus of their...

Network structure plays a critical role in functionality and performance of network systems. This paper examines structural adaptivity of diffusively coupled, directed multi-agent networks that are subject to diffusion performance. Inspired by the observation that the link redundancy in a network may degrade its diffusion performance, a distributed...

In contrast with the scalar-weighted networks, where bipartite consensus can be achieved if and only if the underlying signed network is structurally balanced, the structural balance property is no longer a graph-theoretic equivalence to the bipartite consensus in the case of signed matrix-weighted networks. To re-establish the relationship between...

This paper examines cluster consensus for multi-agent systems on matrix-weighted switching networks. Necessary and/or sufficient conditions under which cluster consensus can be achieved are obtained, as well as quantitative characterization of the steady-state of the cluster consensus. Specifically, when the underlying network switches amongst a fi...

Data‐driven model predictive control (MPC) is an effective control method in controlling unknown constrained systems. The existing data‐driven MPC methods either estimate the system online (adaptive) with extra computation efforts, or use the initially measured trajectory from offline trials to design controller. The offline trials are economically...

A critical prerequisite for controlling complex networks is to find a driver node set with a structural controllability guarantee. This paper introduces the control capacity for a driver node set and solves the problem of finding a complete minimum driver node set that not only guarantees network structural controllability but achieves the desired...

In this article, we study the optimal iterative learning control (ILC) for constrained systems with bounded uncertainties via a novel conic input mapping (CIM) design methodology. Due to the limited understanding of the process of interest, modeling uncertainties are generally inevitable, significantly reducing the convergence rate of the control s...

In this work, we develop an event-triggered distributed robust model predictive control algorithm to stabilize the origin of a class of interconnected systems with nonlinear coupling terms and additive bounded disturbances. Each subsystem is assumed to be able to exchange information with its neighboring subsystems. The transmitted information betw...

Bin-packing problem (BPP) is a typical combinatorial optimization problem whose decision-making process is NP-hard. This article examines BPPs in varying environments, where random number and shape of items are to be packed in different instances. The objective is to find a unified model to derive optimal decision process that maximizes the utiliza...

This paper examines the impact of local peer selection on global opinion dynamics evolving on signed social networks, where both cooperative and antagonistic ties coexist. Specifically, we focus on how opinions of stubborn individuals can be efficiently learned by a signed social network using peer selection mechanism. First, we examine the correla...

In image-based visual servoing (IBVS), parametric uncertainties tend to cause the model inaccuracy and limit the control performance. Considering these uncertainties can be embodied by the output-input data from the visual servoing system, this brief proposes an eye-in-hand visual servoing control (VSC) scheme based on the input mapping method, whi...

In this article, we develop data-driven optimal synchronization control architectures for leader-follower multiagent systems with additive disturbances and unknown system matrices. To minimize output synchronization error, algebraic Riccati equations (AREs) are derived, and unique feedback gains are determined by policy iteration. On that basis, tw...

This brief discusses data-driven design techniques for batch-to-batch optimization problems and proposes a new input-mapping-based online uncertainty compensation method for optimization-based iterative learning control (ILC) with limited memory. Since process uncertainties are generally inevitable, we collect historical data that incorporates past...

This paper proposes a synthetic robust model predictive control method with input mapping for the image-based visual servoing problem with constraints, where the novel control law is constructed by the robust control law designed offline and the online linear compensation of the past data. This proposed method can overcome the conservatism of robus...

This paper examines the event-triggered global consensus of matrix-weighted networks subject to actuator saturation. A distributed protocol design is proposed for this category of networks to guarantee its global consensus subject to both event-triggered communication and actuator saturation. It is shown that the largest singular value of matrix-va...

This paper develops a data-driven learning framework for approximating the feasible region and invariant set of a nonlinear system under the nonlinear Model Predictive Control (MPC) scheme. The developed approach is based on the feasibility information of a point-wise data set using low-discrepancy sequence. Using kernel-based Support Vector Machin...

The ubiquitous interdependencies among higher-dimensional states of neighboring agents can be characterized by matrix-weighted networks. This paper examines event-triggered global consensus of matrix-weighted networks subject to actuator saturation. Specifically, a distributed dynamic event-triggered coordination strategy, whose design involves sam...

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 the spatial interaction...

Stability of multi-agent systems on signed networks is intricate. To some extent, this is due to the associated signed Laplacian may lose its diagonal dominance property. This paper proposes a distributed self-loop compensation approach to rebuild the diagonal dominance of signed Laplacian, and subsequently, examine the stability and cluster consen...

This note examines the distributed nonconvex optimization problem with structured nonconvex objective functions and coupled convex inequality constraints on static networks. A distributed continuous-time primal-dual algorithm is proposed to solve the problem. We use the canonical transformation and Lagrange multiplier method to reformulate the nonc...

In batch processes, the ability to learn from previous process data results in high-value and batch-improved products. For batch processes with constraints and state-dependent uncertainties, this article presents a conic iterative learning control (ILC) approach, which uses cone theory to incorporate historical process data into optimization-based...

The high-dimensional generalization of the one-dimensional Kuramoto paradigm has been an essential step in bringing about a more faithful depiction of the dynamics of real-world systems. Despite the multi-dimensional nature of the oscillators in these generalized models, the interacting schemes so far have been dominated by a scalar factor unanimou...

This paper presents an online adaptive learning solution to the optimal synchronization control problem of heterogeneous multi-agent systems via a novel distributed policy iteration approach. For the leader-follower multi-agents, the dynamics of all the followers are heterogeneous with leader disturbance. To make the output of each follower synchro...

Achieving consensus via nearest neighbor rules is an important prerequisite for multi-agent networks to accomplish collective tasks. A common assumption in consensus setup is that each agent interacts with all its neighbors during the process. This paper examines whether network functionality and performance can be maintained-and even enhanced-when...

This paper examines the cluster consensus problem of multi-agent systems on matrix-weighted switching networks. Necessary and/or sufficient conditions under which cluster consensus can be achieved are obtained and quantitative characterization of the steady-state of the cluster consensus are provided as well. Specifically, if the underlying network...

This paper examines event-triggered consensus of multi-agent systems on matrix-weighted networks, where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the network. Specifically, a distributed dynamic event-triggered coordination strategy is proposed for this category of gene...

Consensus turns out to be an important paradigm in coordination of multi-agent systems [1]. Previous studies largely concentrate on consensus problem over networks where the weights on edges are scalars, which cannot completely characterize the correlation of different dimensions corresponding to the state of an agent in the network. In fact, matri...

Synthesis of model predictive control based on data-driven learning. Sci China Inf Sci, for review Dear editor, Model predictive control (MPC) is a practically effective and attractive approach in the field of industrial processes [1] owing to its excellent ability to handle constraints, nonlinearity, and perfor-mance/cost trade-offs. The core of a...

This paper 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...

In this paper, we consider distributed convex optimization problems on multi-agent networks. We develop and analyze the distributed gradient method which allows each agent to compute its dynamic stepsize by utilizing the time-varying estimate of the local function value at the global optimal solution. Our approach can be applied to both synchronous...

In this paper, we present a gradient algorithm for identifying unknown parameters in an open quantum system from the measurements of time traces of local observables. The open system dynamics is described by a general Markovian master equation based on which the Hamiltonian identification problem can be formulated as minimizing the distance between...

The robust iterative learning control (RILC) can deal with the systems with unknown time-varying uncertainty to track a repeated reference signal. However, the existing robust designs consider all the possibilities of uncertainty, which makes the design conservative and causes the controlled process converging to the reference trajectory slowly. To...

This paper investigates the data-driven predictive control problems for a class of continuous-time industrial processes with completely unknown dynamics. The proposed approach employs the data-driven technique to get the system matrices online, using input-output measurements. Then, a model-free predictive control approach is designed to implement...