On Initial Rectifying Learning for Linear Time-Invariant Systems with Rank-Defective Markov Parameters. - Mingxuan Sun 002 (ICIC (1), 2008)

Varying-order iterative learning control against perturbed initial conditions. - Mingxuan Sun 002; Danwei Wang; Youyi Wang (J. Frankl. Inst., 2010)

Adaptive iterative learning control for SISO discrete time-varying systems. - Mingxuan Sun 002; Xiangbin Liu; Haigang He (ICARCV, 2012)

Adaptive Asymptotic Rejection of Unmatched General Periodic Disturbances in Output-Feedback Nonlinear Systems. - Pengnian Chen; Mingxuan Sun 002; Qiuzhen Yan; Xueyi Fang (IEEE Trans. Autom. Control., 2012)

Neural Network Based Finite-Time Adaptive Backstepping Control of Flexible Joint Manipulators. - Qiang Chen 006; Huihui Shi; Mingxuan Sun 002 (ICONIP (6), 2017)

Adaptive Backstepping Control Design for Uncertain Rigid Spacecraft With Both Input and Output Constraints. - Zhongtian Chen; Qiang Chen 006; Xiongxiong He; Mingxuan Sun 002 (IEEE Access, 2018)

Adaptive Finite-Time Command Filtered Fault-Tolerant Control for Uncertain Spacecraft with Prescribed Performance. - Zhongtian Chen; Qiang Chen 006; Xiongxiong He; Mingxuan Sun 002 (Complex., 2018)

Adaptive Nonsingular Fixed-Time Attitude Stabilization of Uncertain Spacecraft. - Qiang Chen 006; Shuzong Xie; Mingxuan Sun 002; Xiongxiong He (IEEE Trans. Aerosp. Electron. Syst., 2018)

Digital Control Strategies With Attractiveness and Invariance Specifications. - Mingxuan Sun 002; Lingwei Wu; Yi Hu; Wenwei Zhou (IEEE Trans. Control. Syst. Technol., 2018)

Neural AILC for Error Tracking Against Arbitrary Initial Shifts. - Mingxuan Sun 002; Tao Wu; Lejian Chen; Guofeng Zhang 014 (IEEE Trans. Neural Networks Learn. Syst., 2018)

有限值终态递归神经网络计算 (Time-variant Neurocomputing with Finite-value Terminal Recurrent Neural Networks). - Mingxuan Sun 002; Dingen Weng; Yu Zhang 146 (计算机科学, 2020)

Echo State Network-Based Backstepping Adaptive Iterative Learning Control for Strict-Feedback Systems: An Error-Tracking Approach. - Qiang Chen 006; Huihui Shi; Mingxuan Sun 002 (IEEE Trans. Cybern., 2020)

Two-Phase Attractors for Finite-Duration Consensus of Multiagent Systems. - Mingxuan Sun 002 (IEEE Trans. Syst. Man Cybern. Syst., 2020)

Adaptive Iterative Learning Control for Tracking Trajectories With Non-Equal Trail Lengths and Initial Errors. - Mingxuan Sun 002; Yizhao Zhan; Shengxiang Zou; Xiongxiong He (IEEE Access, 2021)

Adaptive Repetitive Learning Control of PMSM Servo Systems with Bounded Nonparametric Uncertainties: Theory and Experiments. - Qiang Chen 006; Xinqi Yu; Mingxuan Sun 002; Chun Wu 002; Zijun Fu (IEEE Trans. Ind. Electron., 2021)

Repetitive Learning Control for Control-Affine Systems With Time Delays. - He Li 016; Mingxuan Sun 002 (IEEE Trans. Syst. Man Cybern. Syst., 2021)

On Finite-Duration Convergent Attracting Laws. - Mingxuan Sun 002; He Li 016; Wei Li (IEEE Trans. Syst. Man Cybern. Syst., 2021)

Incremental Adaptive Control of a Class of Nonlinear Nonaffine Systems. - Yizhao Zhan; Shengxiang Zou; Xiongxiong He; Mingxuan Sun 002 (Complex., 2022)

Steady-State Error Band Improvement Using Dead-Beat Terminal Sliding Mode Control. - Lingwei Wu; Bicheng Lei; Mingxuan Sun 002 (IEEE Trans. Circuits Syst. II Express Briefs, 2022)

On a Finitely Activated Terminal RNN Approach to Time-Variant Problem Solving. - Mingxuan Sun 002; Yu Zhang 146; Yuxin Wu; Xiongxiong He (IEEE Trans. Neural Networks Learn. Syst., 2022)

Semi-global fixed/predefined-time RNN models with comprehensive comparisons for time-variant neural computing. - Mingxuan Sun 002; Xing Li; Guomin Zhong (Neural Comput. Appl., 2023)

Attractiveness and Invariance Assessments of Dead-Beat Sliding-Mode Control With Power Rule. - Wei Li; Mingxuan Sun 002; Xiongxiong He (IEEE Trans. Circuits Syst. II Express Briefs, 2023)

Integral Lyapunov Function-Based Adaptive Learning Control for Nonstrict-Feedback Nonlinear Systems. - Shengxiang Zou; Mingxuan Sun 002; Xiongxiong He (IEEE Trans. Syst. Man Cybern. Syst., 2023)

Adaptive Learning Control Algorithms for Infinite-Duration Tracking. - Mingxuan Sun 002; Shengxiang Zou (IEEE Trans. Neural Networks Learn. Syst., 2023)

Fixed-time convergent RNNs with logarithmic settling time for time-variant quadratic programming solving with application to repetitive motion planning. - Xing Li; Liming Wang; Guomin Zhong; Mingxuan Sun 002 (Neural Comput. Appl., 2024)

Performance enhancing ZNN models for time-variant equality-constraint convex optimization solving: A transition-state based attracting system approach. - Mingxuan Sun 002; Xing Li; Guomin Zhong (Expert Syst. Appl., 2024)

Fuzzy Adaptive Learning Bipartite Consensus for Strict-Feedback Structurally Unbalanced Multiagent Systems With State Constraints. - Shengxiang Zou; Mingxuan Sun 002; Xiongxiong He (IEEE Trans. Fuzzy Syst., 2024)

An Initial-Rectifying-Constraint Optimization Scheme for Repetitive Motion Planning by Using Prescribed-Time Zeroing Neural Networks. - Guomin Zhong; Liming Wang; Mingxuan Sun 002 (IEEE Trans Autom. Sci. Eng., 2025)

Deadzone-Modified Robust Adaptive Learning Bipartite Consensus for Heterogeneous Nonlinear Multiagent Systems. - Shengxiang Zou; Mingxuan Sun 002; Guomin Zhong; Xiongxiong He (IEEE Trans Autom. Sci. Eng., 2025)

Time-variant quadratic programming solving by using finitely-activated RNN models with exact settling time. - Mingxuan Sun 002; Yu Zhang 146; Liming Wang; Yuxin Wu; Guomin Zhong (Neural Comput. Appl., 2025)