Facets of the Evolving Healthcare Management Model

The pandemic represents an opportunity to reimagine future healthcare and rethink healthcare management unbound by preconceived notions based on the following three main drivers that emerged during the pandemic. These include transformed business models, new care delivery models disrupted by ubiquitous data and technology, intelligent spaces, and digitally-enabled hospitality. In this context, it is imperative to reexamine all facets of healthcare management, considering that applying linear models to healthcare management has improved our understanding of their system structure and function. However, such models often fall short of explaining experimental results or predicting future abnormalities in complex nonlinear systems. Nonlinear models may better explain how the individual components collectively act and interact to produce a dynamic system in constant flux. They also assist in filling in some of the results which linear models do not adequately explain. Finally, chaos theory might provide new insights into standard as well as abnormal behavior within systems.

Improving 3D Path Tracking of Unmanned Aerial Vehicles through Optimization of Compensated PD and PID Controllers

The development of quadrotor unmanned aerial vehicles (QUAVs) is a growing field due to their wide range of applications. QUAVs are complex nonlinear systems with a chaotic nature that require a controller with extended dynamics. PD and PID controllers can be successfully applied when the parameters are accurate. However, this parameterization process is complicated and time-consuming; most of the time, parameters are chosen by trial and error without guaranteeing good performance. The originality of this work is to present a novel nonlinear mathematical model with aerodynamic moments and forces in the Newton–Euler formulation, and identify metaheuristic algorithms applied to parameter optimization of compensated PD and PID controls for tracking the trajectories of a QUAV. Eight metaheuristic algorithms (PSO, GWO, HGS, LSHADE, LSPACMA, MPA, SMA and WOA) are reported, and RMSE is used to measure each dynamic performance of the simulations. For the PD control, the best performance is obtained with the HGS algorithm with an RMSE = 0.037247252379126. For the PID control, the best performance is obtained with the HGS algorithm with an RMSE = 0.032594309723623. Trajectory tracking was successful for the QUAV by minimizing the error between the desired and actual dynamics.

chaste codegen: automatic CellML to C++ code generation with fixes for singularities and automatically generated Jacobians

Hundreds of different mathematical models have been proposed for describing electrophysiology of various cell types. These models are quite complex (nonlinear systems of typically tens of ODEs and sometimes hundreds of parameters) and software packages such as the Cancer, Heart and Soft Tissue Environment (Chaste) C++ library have been designed to run simulations with these models in isolation or coupled to form a tissue simulation. The complexity of many of these models makes sharing and translating them to new simulation environments difficult. CellML is an XML format that offers a solution to this problem and has been widely-adopted. This paper specifically describes the capabilities of chaste_codegen, a Python-based CellML to C++ converter based on the new cellmlmanip Python library for reading and manipulating CellML models. While chaste_codegen is a Python 3 redevelopment of a previous Python 2 tool (called PyCML) it has some additional new features that this paper describes. Most notably, chaste_codegen has the ability to generate analytic Jacobians without the use of proprietary software, and also to find singularities occurring in equations and automatically generate and apply linear approximations to prevent numerical problems at these points.

Prescribed Performance-Based Event-Driven Fault-Tolerant Robust Attitude Control of Spacecraft under Restricted Communication

This paper explores the problem of attitude stabilization of spacecraft under multiple uncertainties and constrained bandwidth resources. The proposed control law is designed by combining the sliding mode control (SMC) technique with a prescribed performance control (PPC) method. Further, the control input signal is executed in an aperiodic time framework using the event-trigger (ET) mechanism to minimize the control data transfer through a constrained wireless network. The SMC provides robustness against inertial uncertainties, disturbances, and actuator faults, whereas the PPC strategy aims to achieve a predefined system performance. The PPC technique is developed by transforming the system attitude into a new variable using the prescribed performance function, which acts as a predefined constraint for transient and steady-state responses. In addition, the ET mechanism updates the input value to the actuator only when there is a violation of the triggering rule; otherwise, the actuator output remains at a fixed value. Moreover, the proposed triggering rule is constituted through the Lyapunov stability analysis. Thus, the proposed approach can be extended to a broader class of complex nonlinear systems. The theoretical analyses prove the uniformly ultimately bounded stability of the closed-loop system and the non-existence of the Zeno behavior. The effectiveness of the proposed methodology is also presented along with the comparative studies through simulation results.

Fuzzy-Model-Based Robust Control of Markov Jump Nonlinear Systems With Incomplete Transition Probabilities and Uncertain Packet Dropouts

Interval type-2 fuzzy Markov jump systems (IT2FMJSs) have received much attention because they can better describe complex nonlinear systems with uncertainties and stochastic system mode switching. Over the past decade, many excellent results of fuzzy MJSs (FMJSs) have been reported. However, the transition probabilities which govern the dynamic behaviour of MJSs have been assumed to be completely known, limiting real-world applications of existing results. Different from the previous studies, transition probabilities between system modes switching are partly unknown, and packet dropouts of data transmission are uncertain in this study. The main contributions of this work are: (1) To analyze stochastic stability and reduce conservatism, a novel Lyapunov function which both depends on system mode and fuzzy basis function is constructed; (2) The existence of a mode-dependent and fuzzy-basis-dependent state-feedback controller is investigated; (3) The closedloop system is stochastically stable with a desired H∞ performance, thereby addressing the problem of incomplete transition probabilities and uncertain packet dropouts. An illustrative example of a robot arm is used to demonstrate the effectiveness and practicality of the proposed approach. By virtue of the proposed approach, the effects of incomplete transition probabilities and uncertain packet dropouts on IT2FMJSs are alleviated.

Dynamics Analysis and Fractional-Order Approximate Entropy of Nonlinear Inventory Management Systems

Inventory management is complex nonlinear systems that are affected by various external factors, including course human action and policy. We study the inventory management model under special circumstances and analyse the equilibrium point of the system. The dynamics of the system is analysed by means of the eigenvalue trajectory, bifurcations, chaotic attractor, and largest Lyapunov exponent diagram. At the same time, according to the definition of fractional calculus, the fractional approximate entropy is used to analyse the system, and the results are consistent with those of the largest Lyapunov exponent diagram, which shows the effectiveness of this method.

An Optimization Procedure of Model’s Base Construction in Multimodel Representation of Complex Nonlinear Systems

The multimodel approach is a research subject developed for modeling, analysis and control of complex systems. This approach supposes the definition of a set of simple models forming a model’s library. The number of models and the contribution of their validities is the main issues to consider in the multimodel approach. In this chapter, a new theoretical technique has been developed for this purpose based on a combination of probabilistic approaches with different objective function. First, the number of model is constructed using neural network and fuzzy logic. Indeed, the number of models is determined using frequency-sensitive competitive learning algorithm (FSCL) and the operating clusters are identified using Fuzzy K- means algorithm. Second, the Models’ base number is reduced. Focusing on the use of both two type of validity calculation for each model and a stochastic SVD technique is used to evaluate their contribution and permits the reduction of the Models’ base number. The combination of FSCL algorithms, K-means and the SVD technique for the proposed concept is considered as a deterministic approach discussed in this chapter has the potential to be applied to complex nonlinear systems with dynamic rapid. The recommended approach is implemented, reviewed and compared to academic benchmark and semi-batch reactor, the results in Models’ base reduction is very important witch gives a good performance in modeling.

Parameters Identification of the Fractional-Order Permanent Magnet Synchronous Motor Models Using Chaotic Ensemble Particle Swarm Optimizer

In this paper, novel variants for the Ensemble Particle Swarm Optimizer (EPSO) are proposed where ten chaos maps are merged to enhance the EPSO’s performance by adaptively tuning its main parameters. The proposed Chaotic Ensemble Particle Swarm Optimizer variants (C.EPSO) are examined with complex nonlinear systems concerning equal order and variable-order fractional models of Permanent Magnet Synchronous Motor (PMSM). The proposed variants’ results are compared to that of its original version to recommend the most suitable variant for this non-linear optimization problem. A comparison between the introduced variants and the previously published algorithms proves the developed technique’s efficiency for further validation. The results emerge that the Chaotic Ensemble Particle Swarm variants with the Gauss/mouse map is the most proper variant for estimating the parameters of equal order and variable-order fractional PMSM models, as it achieves better accuracy, higher consistency, and faster convergence speed, it may lead to controlling the motor’s unwanted chaotic performance and protect it from ravage.

Design of Genetic Programming Control Algorithm for Low-Temperature PEM Fuel Cell

An effective control system for the air supply in fuel cell systems (FCS) is required to prevent oxygen starvation and to maximize the net power. For this purpose, conventional feedback and adaptive controllers are designed using genetic programming (GP). To minimize the time required for the GP convergence, FCS models of different complexity are studied and a comparison between them is carried out. Guidelines on applying the GP approach based on data obtained from simulations are developed along with a specially designed cost function that promotes closed-loop linearization. The advantage of this design method lies in its applicability to complex nonlinear systems for which nonlinear control methods may not be applicable. Adaptation is added to the oxygen excess ratio (OER) regulation problem by training a neural network that provides the optimal OER reference based on the stack current and temperature. The performance of both the regulation and adaptive controllers is tested under noise in the compressor flow and the stack current measurements. The robustness of the GP controllers is observed through the frequency response analysis.