scholarly journals Studying Bone Remodelling and Tumour Growth for Therapy Predictive Control

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 679
Author(s):  
Raquel Miranda ◽  
Susana Vinga ◽  
Duarte Valério
Keyword(s):  
Drug Resistance ◽  
Predictive Control ◽  
Bone Remodelling ◽  
Proteasome Inhibitors ◽  
Drug Dose ◽  
Cell Resistance ◽  
Swarm Optimization ◽  
Dose Administration ◽  
Control Scheme ◽  
Metastasis Growth

Bone remodelling consists of cycles of bone resorption and formation executed mainly by osteoclasts and osteoblasts. Healthy bone remodelling is disrupted by diseases such as Multiple Myeloma and bone metastatic diseases. In this paper, a simple mathematical model with differential equations, which takes into account the evolution of osteoclasts, osteoblasts, bone mass and bone metastasis growth, is improved with a pharmacokinetic and pharmacodynamic (PK/PD) scheme of the drugs denosumab, bisphosphonates, proteasome inhibitors and paclitaxel. The major novelty is the inclusion of drug resistance phenomena, which resulted in two variations of the model, corresponding to different paradigms of the origin and development of the tumourous cell resistance condition. These models are then used as basis for an optimization of the drug dose applied, paving the way for personalized medicine. A Nonlinear Model Predictive Control scheme is used, which takes advantage of the convenient properties of a suggested adaptive and democratic variant of Particle Swarm Optimization. Drug prescriptions obtained in this way provide useful insights into dose administration strategies. They also show how results may change depending on which of the two very different paradigms of drug resistance is used to model the behaviour of the tumour.

Model predictive control based on chaos particle swarm optimization for nonlinear processes with constraints

Kybernetes ◽  
2014 ◽  
Vol 43 (9/10) ◽  
pp. 1469-1482 ◽  
Author(s):  
Adel Taeib ◽  
Moêz Soltani ◽  
Abdelkader Chaari
Keyword(s):  
Particle Swarm Optimization ◽  
Predictive Control ◽  
Fuzzy Controller ◽  
Nonlinear Process ◽  
Model Predictive Controller ◽  
Swarm Optimization ◽  
Content Type ◽  
Control Scheme ◽  
Predictive Controller ◽  
New Type

Purpose – The purpose of this paper is to propose a new type of predictive fuzzy controller. The desired nonlinear system behavior is described by a set of Takagi-Sugeno (T-S) model. However, due to the complexity of the real processes, obtaining a high quality control with a short settle time, a periodical step response and zero steady-state error is often a difficult task. Indeed, conventional model predictive control (MPC) attempts to minimize a quadratic cost over an extended control horizon. Then, the MPC is insufficient to adapt to changes in system dynamics which have characteristics of complex constraints. In addition, it is shown that the clustering algorithm is sensitive to random initialization and may affect the quality of obtaining predictive fuzzy controller. In order to overcome these problems, chaos particle swarm optimization (CPSO) is used to perform model predictive controller for nonlinear process with constraints. The practicality and effectiveness of the identification and control scheme is demonstrated by simulation results involving simulations of a continuous stirred-tank reactor. Design/methodology/approach – A new type of predictive fuzzy controller. The proposed algorithm based on CPSO is used to perform model predictive controller for nonlinear process with constraints. Findings – The results obtained using this the approach were comparable with other modeling approaches reported in the literature. The proposed control scheme has been show favorable results either in the absence or in the presence of disturbance compared with the other techniques. It confirms the usefulness and robustness of the proposed controller. Originality/value – This paper presents an intelligent model predictive controller MPC based on CPSO (MPC-CPSO) for T-S fuzzy modeling with constraints.

Models of a hybrid wheeled hopping self-balanced robot

2021 ◽  
pp. 095440622098419
Author(s):  
Qimin Li ◽  
Haibing Zeng ◽  
Long Bai ◽  
Zijian An
Keyword(s):  
Pid Control ◽  
Motion Pattern ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Swarm Optimization ◽  
Hopping Robot ◽  
Control Scheme ◽  
Hopping Robots ◽  
Hybrid Motion ◽  
Classical Control

Combining wheeled structure with hopping mechanism, this paper purposes a self-balanced hopping robot with hybrid motion pattern. The main actuator which is the cylindrical cam, optimized by particle swarm optimization (PSO), is equipped with the motor to control the hopping motion. Robotic system dynamics model is established and solved by Lagrangian method. After linearization, control characteristics of the system is obtained by classical control theory based on dynamics equations. By applying Adams and Matlab to simulate the system, hopping locomotion and self-balanced capability are validated respectively, and result shows that jump height can reach 750 mm theoretically. Then PID control scheme is developed and specific models of hardware and software are settled down accordingly. Finally, prototype is implemented and series of hopping experiments are conducted, showing that with different projectile angle, prototype can jump 550 mm in height and 460 mm in length, transcending majority of other existing hopping robots.

scholarly journals A nonlinear tracking model predictive control scheme for dynamic target signals

Automatica ◽  
2020 ◽  
Vol 118 ◽  
pp. 109030 ◽  
Author(s):  
Johannes Köhler ◽  
Matthias A. Müller ◽  
Frank Allgöwer
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Scheme ◽  
Nonlinear Tracking ◽  
Tracking Model ◽  
Dynamic Target

DEVELOPMENT OF ANN-BASED PID CONTROLLER FOR ACTIVE FLUID-FILM BEARING CONTROL

2020 ◽  
Vol 6 ◽  
pp. 94-100
Author(s):  
N.N. MAKHOVA ◽  
A.Yu. BABIN
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Particle Swarm Optimization Algorithm ◽  
Fluid Film ◽  
Training Dataset ◽  
Swarm Optimization ◽  
Controlled System ◽  
Active Fluid ◽  
Control Scheme ◽  
Controlled Object

The article proposes a method for controlling an active fluid-film bearing, based on the use of a classical PID controller in conjunction with an artificial neural network. The regulator coefficients are not constant numbers, but are chosen by the network depending on the state of the controlled system. To implement such a control scheme, the coefficients are selected using a particle swarm optimization algorithm, which constitutes the training dataset, and an ANN is trained using the dataset. The controlled object is represented with a model operating in the Simulink environment.

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