Control Systems Engineering Research Articles

Cancer Immune Control Dynamics (CICD): A clinical data driven model of systemic immunity.

e21058 Background: Recent clinical advances in cancer immuno-therapeutics underscore the need for improved understanding of the complex relationship between cancer and the multiple, multi-functional, inter-dependent, cellular and humoral mediators/regulators of the human immune system. This will lead to improvements in the efficacy of existing immune therapies (patient selection; drug combinations) and insight into new therapeutic targets that may significantly reduce the time to new therapeutics discoveries capable of meaningful clinical impacts. Methods: A systems analysis approach (Cancer Immune Control Dynamics, CICD) attempts to identify the differences between systemic immune homeostasis of healthy volunteers versus patients with metastatic malignant melanoma based on serial measurements of a set of conventional peripheral blood biomarkers of immune function (15 cell subsets and 35 cytokines) assessed daily over 7-10 days. The modeling strategy takes aerospace engineering control theory and system identification algorithms to analyze the state of an individual’s immune system based on the biomarkers’ dynamic non-linear oscillatory behaviors. The analysis framework builds on the classical predator-prey equations commonly used to model biological system dynamics. Tens of thousands of biologically possible biomarker interactions are modeled by a set of matrix equations that creates a system interaction model. CICD quantifies the system equations with biomarker values and solves it to measure the interactions. Results: Quantification of thousands of unique biomarker relationships per participant enables classification based on their immune profile rather than their clinical diagnosis and a basis for a visualization of a person’s immunity state. Conclusions: Results provide initial evidence that a model-based analysis of serially collected biomarker data can lead to biological insights into human immunity, of potential benefit in clinical decisions. It is anticipated that CICD-based capabilities will provide tools to specifically address cancer and treatment modulated (immune checkpoint inhibitors) parameters of human immunity, revealing clinically relevant biological interactions.

Backstepping Linear Quadratic Gaussian Controller Design for Balancing an Inverted Pendulum

Numerous real-time implementations such as a robust balancing process, segway and hover board riding, and rocket propeller operation are done with the control principle of Inverted Pendulum (IP). Dynamically, the IP is considered as an extremely unstable, non-linear in nature, and under-actuated system. Thus, the control strategy formulation for its equilibrium is generally accepted as an initial attempt for any new control technique as a proof towards further application in the control system engineering. This paper presents a novel Backstepping Linear Quadratic Gaussian Controller (BLQGC) based on the Lyapunov function to control the cart position and angular displacement of the pendulum. The justification of enhanced control performance of BLQGC is demonstrated by comparative result analysis with optimal PID control, Linear Quadratic Gaussian (LQG) control, and other robust control techniques suggested by different authors. The simulations are carried out through MATLAB/SIMULINK environment and the results indicate comparatively better control ability of the proposed method in terms of accuracy, stability, quick damping, and robustness.

Fractional fixed-structure [formula omitted] controller design using Augmented Lagrangian Particle Swarm Optimization with Fractional Order Velocity

The optimization of a fixed-structure controller is in general computationally intractable, posing a challenge to control system engineers. This paper introduces a novel technique for designing such control techniques by formulating it as a nonlinear non-convex constrained problem. A novel PSO algorithm, namely the Augmented Lagrangian Particle Swarm Optimization with Fractional Order Velocity (ALPSOFV), is proposed for improving the convergence rate. The structure of the controller is selectable and, therefore, the Fractional Order Proportional Integral Derivative (FOPID) algorithm is chosen in the scope of the study. The results for three test examples show the good performance of the ALPSOFV.

Design of Nano-scale Electrodes and Development of Avatar-Based Control System for Energy-Efficient Power Engineering

In this article is described the avatar-based energy-efficient power engineering control system, which is based on authors results published previously related to development of an Internet of Things and people (IOTAP) and the digital twins systems. The authors are described the development of the innovation tools for digital twins system, based on Avatar-based Control Techniques.

Dynamics of semilattice networks with strongly connected dependency graph

Discrete-time dynamical systems on a finite state space have been used to model natural and engineered systems such as biological networks, social networks, and engineered control systems. They have the advantage of being intuitive and the models can be easily simulated on a computer in most cases; however, few analytical tools beyond simulation are available. The motivation for this paper is to develop such tools. It identifies a broad class of discrete dynamical systems with a finite phase space for which one can derive strong results about their long-term dynamics in terms of properties of their dependency graphs. The paper contains a complete classification of the periodic orbits of semilattice networks with strongly connected dependency graph, by finding analytically the exact number of periodic orbits of any given period.

Neural Field Theory of Corticothalamic Prediction With Control Systems Analysis.

Neural field theory is used to model and analyze realistic corticothalamic responses to simple visual stimuli. This yields system transfer functions that embody key features in common with those of engineering control systems, which enables interpretation of brain dynamics in terms of data filters. In particular, these features assist in finding internal signals that represent input stimuli and their changes, which are exactly the types of quantities used in control systems to enable prediction of future input signals, and adjustment of gains which is argued to be the analog of attention in control theory. Corticothalamic dynamics are shown to be analogous to the classical proportional-integral-derivative (PID) filters that are widely used in engineering.

A Modified Disturbance Observer Structure Based on Acceleration Measurement for Disturbance Suppression in Tracking Control System

The micro-electro-mechanical system (MEMS) accelerometer is widely adopted in many engineering control systems due to its extraordinary performance with high bandwidth, small size and low weight. However, massive drift caused by its insensitively at low frequency is the main factor which limits its performance. It leads to integral saturation when the feedforward method is used and hinders the improvement of disturbance suppression ability at low frequency, which is a significant factor for evaluating the closed-loop performance of a high-precision tracking system. To solve this problem, a modified disturbance observer structure and its corresponding new controller, which can improve disturbance suppression performance at low frequency by effectively rejecting more drift and weakening the occurrence possibility of integral saturation when drift exists, are proposed. Detailed analyses and a series of comparative experimental results verify that the proposed method can effectively enhance disturbance suppression performance at low frequency.

Improvement of Engineering Accounting and Control System in Construction on the Basis of Accounting Engineering Tools

The article describes the prerequisites and possibilities of creation of a microeconomic platform of the digital economy in the construction industry. The chronology of development and improvement of mechanisms and methods of accounting is given. The article presents a system of mechanisms of accounting engineering and management of economic processes, developed by the authors.

Best practices for model-based and simulation-aided engineering of power transmission and motion control systems

This paper deals with the modelling and simulation of aircraft systems, in particular for power transmission and control. It is intended to review, propose and disseminate best practices for making model-based/simulation-aided engineering more efficient at any phase of the system life cycle. The proposals are aimed at creating value, not only by increasing the performance of the product under study but also by shortening the time to market, capitalizing knowledge, mitigating risks and facilitating concurrent engineering. The needs associated with the engineering activities are firstly identified to define a set of requirements for the models. Then, these requirements are used to drive the considerations leading to model development, focusing in particular on the process, modelled physical effects, modelling level, model architecting and concurrent engineering. The third part deals with the model implementation, giving special consideration to the different types of models, causalities, parameterization, implementation and verification. Each part is illustrated by examples related to safety critical actuators.

Robust 2-DOF FOPID Controller Design for Maglev System Using Jaya Algorithm

In this paper, 1-Degree of Freedom (1-DOF) and 2-Degree of Freedom (2-DOF) Integer Order (IO) and Fractional Order (FO) Proportional–Integral–Derivative (PID) Controller has been designed for the Magnetic Levitation (Maglev) system. Maglev is one of the most versatile research-oriented laboratory instruments in the field of control systems engineering. The controller for the Maglev system is designed in such a way that a ferromagnetic ball can precisely levitate in a controlled electromagnetic field with particular design specifications. The controller parameters can be appropriately identified by optimizing the objective function with various popular artificial intelligence based evolutionary optimization algorithms. The 2-DOF controller structure takes care of both reference signal tracking and disturbance rejection through the feedback path. The 1 and 2-DOF controller structures with both Integer as well as Fractional Order approaches for different nonlinear systems were already discussed in various papers. However, the application of the Fractional Order control technique on a highly nonlinear Maglev system using the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Hybrid Particle Swarm Optimization (Hybrid PSO), and recently developed Jaya Algorithm is of our interest. Observation of the final analysis based on different simulation results states that as the controller structure moves from 1-DOF to 2-DOF, the performance of the system and the control signal gets improved. The robustness analysis has also been incorporated in this study which shows that the 1 and 2-DOF IOPID and FOPID controllers designed using different optimization algorithms for the Maglev plant are robust in nature.

Tutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions.

BackgroundAdaptive behavioral interventions are individualized interventions that vary support based on a person's evolving needs. Digital technologies enable these adaptive interventions to function at scale. Adaptive interventions show great promise for producing better results compared with static interventions related to health outcomes. Our central thesis is that adaptive interventions are more likely to succeed at helping individuals meet and maintain behavioral targets if its elements can be iteratively improved via data-driven testing (ie, optimization). Control systems engineering is a discipline focused on decision making in systems that change over time and has a wealth of methods that could be useful for optimizing adaptive interventions.ObjectiveThe purpose of this paper was to provide an introductory tutorial on when and what to do when using control systems engineering for designing and optimizing adaptive mobile health (mHealth) behavioral interventions.OverviewWe start with a review of the need for optimization, building on the multiphase optimization strategy (MOST). We then provide an overview of control systems engineering, followed by attributes of problems that are well matched to control engineering. Key steps in the development and optimization of an adaptive intervention from a control engineering perspective are then summarized, with a focus on why, what, and when to do subtasks in each step.ImplicationsControl engineering offers exciting opportunities for optimizing individualization and adaptation elements of adaptive interventions. Arguably, the time is now for control systems engineers and behavioral and health scientists to partner to advance interventions that can be individualized, adaptive, and scalable. This tutorial should aid in creating the bridge between these communities.

Explore the Development and Application of Electrical Automation Control Systems

The development of electrical automation engineering control system is the foundation of modern industrial development in China, and also an important embodiment of the rapid development of modern technology. The application of electric automation technology can not only improve work efficiency, but also reduce enterprise cost. Based on this, the current situation and development trend of electrical automation engineering control system are studied for reference by relevant staff.

Explore the Development and Application of Electrical Automation Control Systems

The development of electrical automation engineering control system is the foundation of modern industrial development in China, and also an important embodiment of the rapid development of modern technology. The application of electric automation technology can not only improve work efficiency, but also reduce enterprise cost. Based on this, the current situation and development trend of electrical automation engineering control system are studied for reference by relevant staff.

Synthetic Toolkit for Complex Genetic Circuit Engineering in Saccharomyces cerevisiae.

Sustainable production of chemicals, materials, and pharmaceuticals is increasingly performed by genetically engineered cell factories. Engineering of complex metabolic routes or cell behavior control systems requires robust and predictable gene expression tools. In this challenging task, orthogonality is a fundamental prerequisite for such tools. In this study, we developed and characterized in depth a comprehensive gene expression toolkit that allows accurate control of gene expression in Saccharomyces cerevisiae without marked interference with native cellular regulation. The toolkit comprises a set of transcription factors, designed to function as synthetic activators or repressors, and transcription-factor-dependent promoters, which together provide a broad expression range surpassing, at high end, the strongest native promoters. Modularity of the developed tools is demonstrated by establishing a novel bistable genetic circuit with robust performance to control a heterologous metabolic pathway and enabling on-demand switching between two alternative metabolic branches.

Developing Learning Tool of Control System Engineering Using Matrix Laboratory Software Oriented on Industrial Needs

The purpose of this research is to develop learning media of control technique using Matrix Laboratory software with industry requirement approach. Learning media serves as a tool for creating a better and effective teaching and learning situation because it can accelerate the learning process in order to enhance the quality of learning. Control Techniques using Matrix Laboratory software can enlarge the interest and attention of students, with real experience and can grow independent attitude. This research design refers to the use of research and development (R & D) methods that have been modified by multi-disciplinary team-based researchers. This research used Computer based learning method consisting of computer and Matrix Laboratory software which was integrated with props. Matrix Laboratory has the ability to visualize the theory and analysis of the Control System which is an integration of computing, visualization and programming which is easy to use. The result of this instructional media development is to use mathematical equations using Matrix Laboratory software on control system application with DC motor plant and PID (Proportional-Integral-Derivative). Considering that manufacturing in the field of Distributed Control systems (DCSs), Programmable Controllers (PLCs), and Microcontrollers (MCUs) use PID systems in production processes are widely used in industry.

Study on flood control risk of flood control engineering system based on the clustering of measured data

In this paper, the flood control system of Nanning is taken as the research object. Select calculating the flood control risk rate of reservoir combining with dikes of the urban flood control engineering system as the starting point; According to measured flood sequence, use the SAR(1) model to simulate the flooding; Comprehensively consider the hydrological uncertainty composed of reservoir flood and flood area, the cognitive uncertainty of the flood forecasting error and impact of the uncertainty of the operational management of the cascade reservoir when dispatching delays. The results show that the scientific forecasting and decision-making can reduce the impact of uncertainty factors and improve the flood control capacity of the flood control engineering system of Nanning, and combined dispatching of two reservoirs of Baise and Laokou can meet designed requirements of flood control safety of Nanning when once-in-two centuries flood occurs. With the increase of the flood, the flood risk correspondingly increases, but the risk rate is not large in a certain range. In paper, random combinations of reservoir and interval frequency flood and simulated flood is used to calculate the risk ratio of flood control system to establish a rapid and effective risk calculation method of urban flood control engineering system and the relationship between flood frequency and risk rate, which provides references for flood control system to combine with operation decision of flood control.

Verification of safety integrity level of high demand system based on Stochastic Petri Nets and Monte Carlo Simulation

The paper proposed an approach of verifying the Safety Integrity Level (SIL) for the high demand safety system. This approach is based on the Stochastic Petri Nets models and Monte Carlo simulation (SPN-MC) and follows the requirements of IEC61508, which is a commonly accepted standard in the domain of functional safety of programmable electronic and control engineering systems. The paper provides a comparative analysis of the SIL with the application of SPN-MC and the Reliability Block Diagram (RBD) method given by EN61508 to prove the correctness and feasibility of the SPN-MC approach. Additionally, a typical large scaled complex system was adopted to apply the novel approach, which is hard for classical analytical methods.

A Modified PSO Algorithm for IIR Digital Filter Modeling

Modeling for an unknown plant is a very important and primary task in the control system engineering. By using this established model, an adequate controller can be further designed to meet certain control specifications. This paper will propose a new modeling method which is based on the infinite impulse response (IIR) digital filter structure. A modified particle swarm optimization (PSO) with multiple subpopulations is utilized to solve for the IIR filter coefficients to model an unknown digital system. The developed PSO algorithm is able to overcome the multimodal error surface problem that often occurs in the system modeling of IIR digital filter. Finally, three different kinds of digital systems are examined to demonstrate the feasibility of the proposed method, including the same system order and reduced order structures. It is concluded from several simulation results that the proposed method has an excellent performance on solving the IIR digital modeling problem.

Interactive educational tool for the design of compensators using frequency response analysis

This article provides a didactic platform that is developed to improve the teaching as well as learning of “design of compensators” in control systems engineering. The educational tool comprises a graphical user interface in MATLAB. The graphical user interface permits an individual to investigate various parameters involved in the design of the required compensator, which includes percentage overshoot, peak time, and velocity error constant and observe the results dynamically as the parameters change. The approach used in this article for the design of compensators is based on the frequency response analysis. One can obtain various plots including unit step response plot, Bode plot, and root locus plot for uncompensated and compensated systems to have a better understanding of the designed compensator.

Perspective: The Fundamental Value of Engineering Pedagogy for Realizing Personalized Medicine

Fundamental benefits suggest that biomedical engineering and medicine should form a more convergent alliance, especially for the training of tomorrow’s physicians and biomedical engineers. Herein, we review the rationale underlying these benefits. Biological discovery has advanced beyond the era of molecular biology into the new, still largely uncharted era of molecular systems biology. The concomitant shift in focus toward understanding the fundamental rules and principles governing the behavior of complex living systems has important medical implications. To realize cost-effective personalized medicine, it is necessary to propagate the emerging advances in molecular systems biology to higher levels of biological organization that focus on organs, organ system, and whole organisms, and then to develop new, individualized medical therapeutics based on medical informatics and targeted computer simulations. To accomplish these goals, higher education in the biological and medical sciences must adapt to new training objectives. This adaptation must involve a shifting away from reductionist problem solving toward more integrative, systemic, and predictive modeling approaches that traditionally have been more associated with education in engineering. Future biomedical engineers and MDs must be able to predict clinical responses to therapeutic interventions. This necessity mandates that medical training must become more like engineering education, wherein future physicians are taught fundamental rules governing complex system behaviors and skill sets for manipulating these systems to achieve practically feasible, desired clinical outcomes. Correspondingly, graduate biomedical engineering programs must increase the students’ practical exposure to pressing clinical problems. The fundamental goal of medical intervention is to accomplish a sustained beneficial change in the behavior of a complex feedback-controlled homeostatic system (i.e., the patient). The direct and indirect responses of such systems to any substantial interventions are notoriously difficult to fathom by the unaided human mind, thus begging the question how modern science and engineering may help. There appears to be great potential for medical students to gain benefits from learning and engaging in models of engineering control systems applied to medical practice. A particularly powerful target domain is regenerative medicine, which is already accustomed to translating engineering concepts into medical advances. Tissue engineering, organoid development, and regenerative medicine are prime examples of promising new technologies that will ultimately require a grasp of fundamental bioengineering control theory, not simply for understanding how biological systems work, but also for envisioning how we can exert steady-state control in these living, adaptive systems. Here, we propose that near-future medical education should shift toward integrative analysis of system function and malfunction and toward the development of therapies utilizing control concepts. Such a shift will eventually make personalized medicine feasible, practical, and affordable, but requires a significant rethinking of existing pedagogical tools.