All Outputs (2)

Dynamic performance enhancement for nonlinear stochastic systems using RBF driven nonlinear compensation with extended Kalman filter (2019)
Journal Article
Zhou, Y., Wang, A., Zhou, P., Wang, H., & Chai, T. (2020). Dynamic performance enhancement for nonlinear stochastic systems using RBF driven nonlinear compensation with extended Kalman filter. Automatica, 112, Article 108693. https://doi.org/10.1016/j.aut

In this paper, a novel hybrid control method is proposed to enhance the tracking performance of the Proportional–Integral (PI) based control system for a class of nonlinear and non-Gaussian stochastic dynamic processes with unmeasurable states. The s... Read More about Dynamic performance enhancement for nonlinear stochastic systems using RBF driven nonlinear compensation with extended Kalman filter.

Fully Probabilistic Design for Stochastic Discrete System with Multiplicative Noise (2019)
Presentation / Conference Contribution
Zhou, Y., Herzallah, R., & Zafar, A. (2019). Fully Probabilistic Design for Stochastic Discrete System with Multiplicative Noise. In 2019 IEEE 15th International Conference on Control and Automation (ICCA). https://doi.org/10.1109/icca.2019.8899607

In this paper, a novel algorithm based on fully probabilistic design (FPD) is proposed for a class of linear stochastic dynamic processes with multiplicative noise. Compared with the traditional FPD, the new procedure is presented to deal with multip... Read More about Fully Probabilistic Design for Stochastic Discrete System with Multiplicative Noise.