Zhongguo Li

Lecturer (Assistant Professor) in Robotics, Control, Communications and AI

The University of Manchester

United Kingdom

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Dr. Zhongguo Li is a Lecturer (Assistant Professor) in Robotics, Control, Communications, and Artificial Intelligence at the University of Manchester, UK. His research is focused on reinforcement learning, dual control strategies, multi-agent systems, and non-cooperative heterogeneous robot systems. Recent publications include investigations into fast finite-time consensus strategies and event-triggered tracking control for nonlinear systems.

Articles (20)

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2025

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2021

Collaborators (6)

Wenhua Chen

Loughborough University

UNITED KINGDOM

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Alessandra Parisio

Reader (Teaching and Research)

The University of Manchester

UNITED KINGDOM

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Zhengtao Ding

The University of Manchester

UNITED KINGDOM

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Jun Yang

Senior Lecturer

Loughborough University

UNITED KINGDOM

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Yunda Yan

Lecturer in Control Engineering

De Montfort University

UNITED KINGDOM

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Cunjia Liu

Prof. at Loughborough University

Loughborough University

UNITED KINGDOM

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Zhongguo Li

Research Interests

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About

Articles (20)

Reinforcement learning-based fixed-time tracking control for nonlinear systems with asymmetrical guaranteed performance

Dual control for autonomous airborne source search with Nesterov accelerated gradient descent: Algorithm and performance analysis

Fast finite-time consensus strategy for multi-agent systems based on switching of different protocols

Nash equilibrium seeking in non-cooperative heterogeneous multi-robot systems via output regulation

Rigid Geometry Formation Subject to Visibility Constraints Using Heading Angle Correlation Based on Leader-Follower System

Event-triggered prescribed-time tracking for uncertain nonlinear systems with unknown control gain and output constraints

Distributed Fixed-Time Control for Leader-Steered Rigid Shape Formation With Prescribed Performance

Cooperative Active Learning-Based Dual Control for Exploration and Exploitation in Autonomous Search

Dual Control of Exploration and Exploitation for Auto-Optimization Control With Active Learning

Optimization-based Ramping Reserve Allocation of BESS for AGC Enhancement

Concurrent Active Learning in Autonomous Airborne Source Search: Dual Control for Exploration and Exploitation

Adaptive Sliding Mode Fault Tolerant Control for Autonomous Vehicle With Unknown Actuator Parameters and Saturated Tire Force Based on the Center of Percussion

Model-Free Output Feedback Path Following Control for Autonomous Vehicle With Prescribed Performance Independent of Initial Conditions

AID-RL: Active information-directed reinforcement learning for autonomous source seeking and estimation

Consensus-Based Cooperative Algorithms for Training Over Distributed Data Sets Using Stochastic Gradients

Distributed Generalized Nash Equilibrium Seeking and Its Application to Femtocell Networks

On the game‐theoretic analysis of distributed generative adversarial networks

Surrogate-Assisted Cooperation Control of Network-Connected Doubly Fed Induction Generator Wind Farm With Maximized Reactive Power Capacity

Distributed Multiobjective Optimization for Network Resource Allocation of Multiagent Systems

Distributed neural network enhanced power generation strategy of large-scale wind power plant for power expansion

Collaborators (6)

Wenhua Chen

Alessandra Parisio

Zhengtao Ding

Jun Yang

Yunda Yan

Cunjia Liu

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