All Outputs (16)

Reinforcement Q-Learning for PDF Tracking Control of Stochastic Systems with Unknown Dynamics (2024)
Journal Article
Yang, W., Zhou, Y., Zhang, Y., & Ren, Y. (2024). Reinforcement Q-Learning for PDF Tracking Control of Stochastic Systems with Unknown Dynamics. Mathematics, 12(16), Article 2499. https://doi.org/10.3390/math12162499

Tracking control of the output probability density function presents significant challenges, particularly when dealing with unknown system models and multiplicative noise disturbances. To address these challenges, this paper introduces a novel tracki... Read More about Reinforcement Q-Learning for PDF Tracking Control of Stochastic Systems with Unknown Dynamics.

Event-Triggered Relearning Modeling Method for Stochastic System with Non-Stationary Variable Operating Conditions (2024)
Journal Article
Liu, J., Zhang, Y., Zhou, Y., & Chen, J. (2024). Event-Triggered Relearning Modeling Method for Stochastic System with Non-Stationary Variable Operating Conditions. Mathematics, 12(5), Article 667. https://doi.org/10.3390/math12050667

This study presents a novel event-triggered relearning framework for neural network modeling, designed to improve prediction precision in dynamic stochastic complex industrial systems under non-stationary and variable conditions. Firstly, a sliding w... Read More about Event-Triggered Relearning Modeling Method for Stochastic System with Non-Stationary Variable Operating Conditions.

Decentralised Fully Probabilistic Design for Stochastic Networks with Multiplicative Noise (2023)
Journal Article
Zhou, Y., Herzallah, R., & Zhang, Q. (2023). Decentralised Fully Probabilistic Design for Stochastic Networks with Multiplicative Noise. International Journal of Systems Science, 54(8), 1841-1854. https://doi.org/10.1080/00207721.2023.2210568

In this paper, we present an innovative decentralised control framework, designed to address stochastic dynamic complex systems that are influenced by multiple multiplicative noise factors. Our advanced approach builds upon the foundation of conventi... Read More about Decentralised Fully Probabilistic Design for Stochastic Networks with Multiplicative Noise.

Wearable and Invisible Sensor Design for Eye-Motion Monitoring Based on Ferrofluid and Electromagnetic Sensing Technologies (2023)
Journal Article
Tang, J., Luk, P., & Zhou, Y. (2023). Wearable and Invisible Sensor Design for Eye-Motion Monitoring Based on Ferrofluid and Electromagnetic Sensing Technologies. Bioengineering, 10(5), Article 514. https://doi.org/10.3390/bioengineering10050514

For many human body diseases, treatments in the early stages are more efficient and safer than those in the later stages; therefore, detecting the early symptoms of a disease is crucial. One of the most significant early indicators for diseases is bi... Read More about Wearable and Invisible Sensor Design for Eye-Motion Monitoring Based on Ferrofluid and Electromagnetic Sensing Technologies.

Tracking Control for Output Probability Density Function of Stochastic Systems Using FPD Method (2023)
Journal Article
Yang, Y., Zhang, Y., & Zhou, Y. (2023). Tracking Control for Output Probability Density Function of Stochastic Systems Using FPD Method. Entropy, 25(2), Article 186. https://doi.org/10.3390/e25020186

Output probability density function (PDF) tracking control of stochastic systems has always been a challenging problem in both theoretical development and engineering practice. Focused on this challenge, this work proposes a novel stochastic control... Read More about Tracking Control for Output Probability Density Function of Stochastic Systems Using FPD Method.

Variance and Entropy Assignment for Continuous-Time Stochastic Nonlinear Systems (2021)
Journal Article
Tang, X., Zhou, Y., Zou, Y., & Zhang, Q. (2022). Variance and Entropy Assignment for Continuous-Time Stochastic Nonlinear Systems. Entropy, 24(1), Article 25. https://doi.org/10.3390/e24010025

This paper investigates the randomness assignment problem for a class of continuous-time stochastic nonlinear systems, where variance and entropy are employed to describe the investigated systems. In particular, the system model is formulated by a st... Read More about Variance and Entropy Assignment for Continuous-Time Stochastic Nonlinear Systems.

An efficient message passing algorithm for decentrally controlling complex systems (2021)
Journal Article
Herzallah, R., & Zhou, Y. (2023). An efficient message passing algorithm for decentrally controlling complex systems. International Journal of Control, 96(3), 719-730. https://doi.org/10.1080/00207179.2021.2011422

This paper proposes a decentralised stochastic control framework for a class of large-scale and complex dynamic networks. The proposed framework describes a decentralised probabilistic control and message passing architecture of mutually interacting... Read More about An efficient message passing algorithm for decentrally controlling complex systems.

Probabilistic message passing control for complex stochastic switching systems (2021)
Journal Article
Zhou, Y., & Herzallah, R. (2021). Probabilistic message passing control for complex stochastic switching systems. Journal of The Franklin Institute, 358(10), 5451-5469. https://doi.org/10.1016/j.jfranklin.2021.04.040

In this paper, we propose a general decentralised probabilistic control framework for a class of complex stochastic systems with switching modes. Probabilistic state space models are exploited to characterise the subsystems’ dynamical behaviours cons... Read More about Probabilistic message passing control for complex stochastic switching systems.

Probabilistic decentralised control and message passing framework for future grid (2021)
Journal Article
Herzallah, R., & Zhou, Y. (2021). Probabilistic decentralised control and message passing framework for future grid. International Journal of Electrical Power and Energy Systems, 131, Article 107114. https://doi.org/10.1016/j.ijepes.2021.107114

In this paper, we propose a unified probabilistic decentralised control and message passing framework for real time control of the electrical grid which enables the development of the future smart grid. The key elements of the proposed framework are... Read More about Probabilistic decentralised control and message passing framework for future grid.

Optimal Electricity Trading Strategy for a Household Microgrid (2020)
Presentation / Conference Contribution
Qin, Z., Hua, H., Liang, H., Herzallah, R., Zhou, Y., & Cao, J. (2020, October). Optimal Electricity Trading Strategy for a Household Microgrid. Presented at 2020 IEEE 16th International Conference on Control & Automation (ICCA), Singapore

The recent integration of distributed generators (DGs) and renewable energy sources (RESs) into the power system led to the manifestation of a significant number of household microgrid (MG) systems in the electricity market. However, in most of the c... Read More about Optimal Electricity Trading Strategy for a Household Microgrid.

Probabilistic message passing control and FPD based decentralised control for stochastic complex systems (2020)
Journal Article
Zhou, Y., & Herzallah, R. (2020). Probabilistic message passing control and FPD based decentralised control for stochastic complex systems. AIMS Electronics and Electrical Engineering, 4(2), 216-233. https://doi.org/10.3934/electreng.2020.2.216

This paper offers a novel decentralised control strategy for a class of linear stochastic largescale complex systems. The proposed control strategy is developed to address the main challenges in controlling complex systems such as high dimensionality... Read More about Probabilistic message passing control and FPD based decentralised control for stochastic complex systems.

A tracking error–based fully probabilistic control for stochastic discrete-time systems with multiplicative noise (2020)
Journal Article
Herzallah, R., & Zhou, Y. (2020). A tracking error–based fully probabilistic control for stochastic discrete-time systems with multiplicative noise. Journal of Vibration and Control, 26(23-24), 2329-2339. https://doi.org/10.1177/1077546320921608

This article proposes the exploitation of the Kullback–Leibler divergence to characterise the uncertainty of the tracking error for general stochastic systems without constraints of certain distributions. The general solution to the fully probabilist... Read More about A tracking error–based fully probabilistic control for stochastic discrete-time systems with multiplicative noise.

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.automatica.2019.108693

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, July). Fully Probabilistic Design for Stochastic Discrete System with Multiplicative Noise. Presented at 2019 IEEE 15th International Conference on Control and Automation (ICCA), Edinburgh, United Kingdom

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.

EKF-Based Enhanced Performance Controller Design for Nonlinear Stochastic Systems (2017)
Journal Article
Zhou, Y., Zhang, Q., Wang, H., Zhou, P., & Chai, T. (2018). EKF-Based Enhanced Performance Controller Design for Nonlinear Stochastic Systems. IEEE Transactions on Automatic Control, 63(4), 1155-1162. https://doi.org/10.1109/tac.2017.2742661

In this paper, a novel control algorithm is presented to enhance the performance of the tracking property for a class of nonlinear and dynamic stochastic systems subjected to non-Gaussian noises. Although the existing standard PI controller can be us... Read More about EKF-Based Enhanced Performance Controller Design for Nonlinear Stochastic Systems.

Enhanced performance controller design for stochastic systems by adding extra state estimation onto the existing closed loop control (2016)
Presentation / Conference Contribution
Zhou, Y., Zhang, Q., & Wang, H. (2016, August). Enhanced performance controller design for stochastic systems by adding extra state estimation onto the existing closed loop control. Presented at 2016 UKACC 11th International Conference on Control (CONTROL), Belfast, United Kingdom

To enhance the performance of the tracking property, this paper presents a novel control algorithm for a class of linear dynamic stochastic systems with unmeasurable states, where the performance enhancement loop is established based on Kalman filter... Read More about Enhanced performance controller design for stochastic systems by adding extra state estimation onto the existing closed loop control.