Adaptive robust control of cancer chemotherapy with extended Kalman filter observer

Rokhforoz, Pegah; Jamshidi, Arta A.; Sarvestani, Nazanin Namazi

doi:10.1016/j.imu.2017.03.002

Cited by 25 publications

(9 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The goal of an observer is to provide information about the inner state variables or parameters based on the measurements. The most common algorithm is the Kalman filter (KF) [10], however, other approaches like sliding mode observer, particle filter [11], linear observer [12] or moving horizon estimation (MHE) [13] are also employed. The latter one is an optimization-based approach, where the name suggests that the optimization is performed on a moving window.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, an MHE is developed and its performance is compared to an optimized extended Kalman filter (EKF). Often, the drawback of the studies, e.g., [10][11][12][13] is that they are performed on simulated data which is favorable from several aspects. In contrast, this study is evaluated on laboratory measurements, where noise, irregular sampling times and most importantly, structural mismatch are present.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

State and Parameter Estimation of a Mathematical Carcinoma Model under Chemotherapeutic Treatment

et al. 2020

View full text Add to dashboard Cite

One challenging aspect of therapy optimization and application of control algorithms in the field of tumor growth modeling is the limited number of measurable physiological signals—state variables—and the knowledge of model parameters. A possible solution to provide such information is the application of observer or state estimator. One of the most widely applied estimators for nonlinear problems is the extended Kalman filter (EKF). In this study, a moving horizon estimation (MHE)-based observer is developed and compared to an optimized EKF. The observers utilize a third-order tumor growth model. The performance of the observers is tested on measurements gathered from a laboratory mice trial using chemotherapeutic drug. The proposed MHE is designed to be suitable for closed-loop applications and yields simultaneous state and parameter estimation.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

State and Parameter Estimation of a Mathematical Carcinoma Model under Chemotherapeutic Treatment

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Another adaptive controller and feedback error learning has been presented by Rouhollahi et al [35] for rehabilitation therapy in Parkinson's disease. Rokhforoz et al [36] have developed a robust controller for cancer chemotherapy using an extended Kalman filter observer. Sharifi and Moradi [37] have proposed a new non‐linear robust adaptive sliding mode control strategy for the influenza epidemic subjected to modelling uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Control of malaria outbreak using a non‐linear robust strategy with adaptive gains

Rajaei

Vahidi-Moghaddam

Chizfahm

et al. 2019

IET Control Theory & Applications

View full text Add to dashboard Cite

“…Cancer is one of the most prominent causes of death in the world. So far, many methods such as chemotherapy, radiotherapy, surgery, and immunotherapy have been put forth to treat cancer (Rokhforoz et al, 2017; Wei and Lin, 2013). Among them, immunotherapy is “one of the most common approaches in cancer therapy” (Blattman and Greenberg, 2004: 3; Yang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Finite-time adaptive fuzzy backstepping control of drug dosage regimen in cancer treatment

Zirkohi

2019

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

In this paper, the design of robust finite-time adaptive fuzzy backstepping control for multi-input multi-output (MIMO) cancer immunotherapy system with fully unknown parameters is addressed. The aim of the treatment is to reduce the tumor cells by providing a suitable scheduling scheme for drug dosage. The main contribution of the proposed control scheme is that it not only has the advantages of the conventional backstepping control, but also guarantees the finite-time convergence. To approximate the unknown nonlinear parts of the system, the fuzzy system is employed. Using the Lyapunov stability theory, the fuzzy system parameters are tuned online using the derived adaptation laws. Compared with conventional backstepping control, the proposed controller does not include the time derivative of the virtual control law. Therefore, the proposed control structure has a simple robust structure and the problem of “explosion of terms” is avoided. Numerical simulation results are conducted that demonstrate that, as a result of drug treatment, the number of tumor cells is rapidly reduced to zero value.

show abstract

Adaptive robust control of cancer chemotherapy with extended Kalman filter observer

Cited by 25 publications

References 25 publications

State and Parameter Estimation of a Mathematical Carcinoma Model under Chemotherapeutic Treatment

State and Parameter Estimation of a Mathematical Carcinoma Model under Chemotherapeutic Treatment

Control of malaria outbreak using a non‐linear robust strategy with adaptive gains

Finite-time adaptive fuzzy backstepping control of drug dosage regimen in cancer treatment

Contact Info

Product

Resources

About