Aug 2026· 2026 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM)· pp. 464-469· 0 citations· 9 references
Abstract
This paper investigates the discrete-time solution of time-varying quadratic programming (TVQP) problems with linear equality constraints in noisy environments. Starting from the Karush-Kuhn-Tucker conditions, a perturbation-suppressed zeroing neural dynamics model is established to describe the online evolution of the optimal solution. For sampled-data implementation, a seven-instant discretization scheme is developed, yielding the proposed SE-DT-TVQP algorithm. For comparison, Euler-type and Taylor-type discrete-time formulations are also considered. Numerical experiments consisting of a baseline case without noise under the fixed sampling interval g=0.01, a constant-noise case, and a linearly time-varying noise case show that the proposed algorithm consistently achieves lower steady-state residuals and stronger noise resilience than the benchmark methods. These results demonstrate that the proposed seveninstant sampled-data design provides an effective sampled-data approach for online TVQP computation under perturbations.
This paper develops a selective enhanced high-order SILC, in which historical tracking errors are treated as candidate learning data and are admitted only through a performance-driven probabilistic test, and the asymptotic convergence of the proposed method is proved.
Kun-Hong Chen, Zeyi Zhang, Yu-Jin Cai et al.· ISA transactions· 0 citations
This paper focuses on the joint non-fragile state and fault estimation issue for a class of stochastic nonlinear systems under the dynamic event-triggered transmission scheme (DETS). To better conform to practical engineering scenarios, we consider an additive fault whose second-order difference is piecewise zero. A ze...
Xue-Gang Tian, Shao-Ying Wang, Kai-Fu Jiang et al.· International Journal of Net...· 0 citations
Guaranteeing a prescribed convergence-time bound for stochastic nonlinear systems remains challenging when actuator faults and unmodeled dynamics are present. This difficulty becomes more pronounced when both loss-of-effectiveness and bias faults occur, since they may significantly degrade the transient regulation perf...
Yi-Xuan Yuan, Li-Ping Xie, Jun-Sheng Zhao et al.· IEEE Transactions on Automat...· 0 citations
A Koopman-based stochastic MPC framework with probabilistically truncated soft constraints that provides a linear latent predictor, enabling computationally efficient online optimization and effective tracking under stochastic measurement unavailability is developed.
Guanzhi Liu, Tong Wu, Li-Xian Zhang et al.· 0 citations
This paper addresses a robust tracking problem for linear discrete-time systems by proposing a reinforcement learning (RL) control method based on a diagonal-scaling strategy, offering a solution tailored to the demands of enhanced reliability. To overcome a common limitation in policy-iteration-based RL design, namely...
Kan-Yang Jiang, Zheng Gao, Ye Zeng et al.· Eksploatacja I Niezawodnosc-...· 0 citations
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