Reference author: Mahshad Valipour

First name

Mahshad

Last name

Valipour

Valipour, M. ., Toffolo, K. ., & Ricardez-Sandoval, L. A. (2021). State estimation and sensor location for entrained-flow gasification systems using Kalman filter. Control Engineering Practice, 108. Retrieved from https://www.sciencedirect.com/science/article/pii/S0967066120302720 (Original work published 2021)

Valipour, M. ., & Ricardez-Sandoval, L. A. (2021). Assessing the impact of EKF as the arrival cost in the moving horizon estimation under nonlinear model predictive control. Industrial & Engineering Chemistry Research, 60. Retrieved from https://pubs.acs.org/doi/full/10.1021/acs.iecr.0c06095 (Original work published 2021)

Valipour, M. ., & Ricardez-Sandoval, L. A. (2021). Constrained abridged Gaussian sum extended Kalman filter: constrained nonlinear systems with non-Gaussian noises and uncertainties. Industrial & Engineering Chemistry Research, 60. Retrieved from https://pubs.acs.org/doi/full/10.1021/acs.iecr.1c02804 (Original work published 2021)

Valipour, M. ., & Ricardez-Sandoval, L. A. (2022). An Extended Moving Horizon Estimation embedded with an Abridged Gaussian Sum Extended Kalman Filter to handle non-Gaussian noises. IFAC-PapersOnLine, 55. Retrieved from https://www.sciencedirect.com/science/article/pii/S2405896322008187 (Original work published 2022)

Valipour, M. ., & Ricardez-Sandoval, L. A. (2022). Extended moving horizon estimation for chemical processes under non-Gaussian noises. AIChE Journal, 68. Retrieved from https://aiche.onlinelibrary.wiley.com/doi/full/10.1002/aic.17545 (Original work published 2022)

Valipour, M. ., & Ricardez-Sandoval, L. A. (2022). A robust moving horizon estimation under unknown distributions of process or measurement noises. Computers & Chemical Engineering, 157. Retrieved from https://www.sciencedirect.com/science/article/pii/S0098135421003987 (Original work published 2022)

Valipour, M. ., & Ricardez-Sandoval, L. A. (2021). Abridged Gaussian sum extended Kalman filter for nonlinear state estimation under non-Gaussian process uncertainties. Computers & Chemical Engineering, 155. Retrieved from https://www.sciencedirect.com/science/article/pii/S0098135421003124 (Original work published 2021)