Combining Control Theory Research Articles

Analysis and control of a delayed population model with an Allee effect

Based on the population model with an Allee effect, the corresponding delayed model is built up. According to the qualitative theory of differential equations, the model is analyzed completely to describe various intriguing dynamic properties as singular point of high order, Hopf bifurcation, etc. Next, combined impulse control theory with the ecological principles, the control models with impulse effects are proposed and the corresponding controllers are designed not only to avoid the extinction of the populations but also to prevent outbreak of the populations. The study would not only reveal the complexity dynamic behavior of ecosystems, but also provide the theory reference for the management of populations.

Exciting Efficient Oscillations in Nonlinear Mechanical Systems Through Eigenmanifold Stabilization

Nonlinear modes are a well investigated concept in dynamical systems theory, extending the celebrated modal analysis of linear mechanical systems to nonlinear ones. This letter moves a first step in the direction of combining control theory and nonlinear modal analysis towards the implementation of hyper-efficient oscillatory behaviors in mechanical systems with non-Euclidean metric. Rather than forcing a prescribed evolution, we first investigate the regular behaviors that can be autonomously expressed by the system, and then we design a controller that excites them. A first implementation of this concept is proposed, analyzed, and tested in simulation.

Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events.

Nicotine consumption is considered a major health problem, where many of those who wish to quit smoking relapse. The problem is that overtime smoking as behaviour is changing into a habit, in which it is connected to internal (e.g., nicotine level, craving) and external (action, time, location) triggers. Smoking cessation apps have proved their efficiency to support smoking who wish to quit smoking. However, still, these applications suffer from several drawbacks, where they are highly relying on the user to initiate the intervention by submitting the factor the causes the urge to smoke. This research describes the creation of a combined Control Theory and deep learning model that can learn the smoker’s daily routine and predict smoking events. The model’s structure combines a Control Theory model of smoking with a 1D-CNN classifier to adapt to individual differences between smokers and predict smoking events based on motion and geolocation values collected using a mobile device. Data were collected from 5 participants in the UK, and analysed and tested on 3 different machine learning model (SVM, Decision tree, and 1D-CNN), 1D-CNN has proved it’s efficiency over the three methods with average overall accuracy 86.6%. The average MSE of forecasting the nicotine level was (0.04) in the weekdays, and (0.03) in the weekends. The model has proved its ability to predict the smoking event accurately when the participant is well engaged with the app.

Dual Combination Synchronization of Six Chaotic Systems

Based on combination synchronization of three chaotic systems and combination–combination synchronization of four chaotic systems, a novel scheme of dual combination synchronization is investigated for six chaotic systems in the paper. Using combined adaptive control and Lyapunov stability theory of chaotic systems, some sufficient conditions are attained to realize dual combination synchronization of six chaotic systems. The corresponding theoretical proofs and numerical simulations are presented to demonstrate the effectiveness and correctness of the dual combination synchronization. Due to the complexity of dual combination synchronization, it will be more secure and interesting to transmit and receive signals in application of communication.

A Multi-branch Mass-spring Model for Virtual Assembly of Cable Harness

The real-time assembly simulation of cables is challenging due to the complex topology and flexibility of cables. A multi-branch mass-spring model is proposed to deal with the difficulties. In which the tense and bending characteristic of cable could be dealt with and the point connecting cable branches are added to help express cable harness with complex topology. A solution combining control theory and kinematics is presented. In the solution an experiential formula is fitted with the help of control variable method to ensure the cable's length is same and an arithmetic fixing the position of particular mass points is presented to simulate the deformation when cables are bundled up. In collision detection for cable, spherical bounding box is used to build up the collision model of cable, and the more accurate collision point could be got in real time by dividing the collision model into more accurate expression and eliminating redundant collision. A prototype system based on the multi-branch mass-spring model is developed, in which assembly simulation of cable is carried out and the key techniques and presented solution are validated.

Optimal control of open quantum systems: A combined surrogate Hamiltonian optimal control theory approach applied to photochemistry on surfaces

In this paper, control of open quantum systems with emphasis on the control of surface photochemical reactions is presented. A quantum system in a condensed phase undergoes strong dissipative processes. From a theoretical viewpoint, it is important to model such processes in a rigorous way. In this work, the description of open quantum systems is realized within the surrogate hamiltonian approach [R. Baer and R. Kosloff, J. Chem. Phys. 106, 8862 (1997)]. An efficient and accurate method to find control fields is optimal control theory (OCT) [W. Zhu, J. Botina, and H. Rabitz, J. Chem. Phys. 108, 1953 (1998); Y. Ohtsuki, G. Turinici, and H. Rabitz, J. Chem. Phys. 120, 5509 (2004)]. To gain control of open quantum systems, the surrogate hamiltonian approach and OCT, with time-dependent targets, are combined. Three open quantum systems are investigated by the combined method, a harmonic oscillator immersed in an ohmic bath, CO adsorbed on a platinum surface, and NO adsorbed on a nickel oxide surface. Throughout this paper, atomic units, i.e., ℏ = m(e) = e = a(0) = 1, have been used unless otherwise stated.

Review of works on dynamics of multimass uncertain electromechanical systems carried out in ZNTU electric drive department

The authors compare the methods of uncertain multimass systems control. A brief review of works on dynamics of multimass uncertain electromechanical systems carried out in ZNTU electric drive department is presented. The possibilities of these systems control in conditions of parametrical, exogenous uncertainty and limited information on the system state vector are shown. The list of publications on combined robust control theory and application of this method for various objects control is given.

Agent Recruitment Planning in Knowledge-Intensive Call Centers

The key ingredient in a call center’s operational efficiency is labor. Agent turnover remains a major concern for call centers. The top three reasons for turnover are low salary, lack of career path, and burnout. On average, it costs about $10 to a call center for each call, and the cost to bring on a new agent is more than $6,000. The author treats call centers as knowledge-intensive operations that are characterized by extensive knowledge required for each agent, combines control theory and chance-constrained programming in a model for workforce planning that allows for agent learning, and derives steady state workforce levels for different knowledge groups within the call center to minimize total labor-related costs. The objective is to meet stochastic demands with a desired service level. The author applies his model to an actual call center situation in the high-tech industry with adjusted data and discusses the managerial implications.

Robust control of periodic bifurcation solutions

The topological bifurcation diagrams and the coefficients of bifurcation equation were obtained by C-L method.According to obtained bifurcation diagrams and combining control theory,the method of robust control of periodic bifurcation was presented,which differs from generic methods of bifurcation control.It can make the existing motion pattern into the goal motion pattern.Because the method does not make strict requirement about parametric values of the controller,it is convenient to design and make it.Numerical simulations verify validity of the method.

Controls and experiments: lessons learned

Presenting four basic lessons derived from combining control theory and experimental implementation: firstly, where possible, modeling should be done within a control-oriented framework; secondly, to avoid a control design quagmire, it is important to know what the appropriate goal is and when the system performance is sufficient to meet specifications developed prior to the control design; thirdly, the effects of actuation authority and measurement sensitivity choices on achievable closed-loop performance should be well understood; and fourthly, the act of serendipity associated with discovery is healthy for research.

Robust intelligent control of a speed sensorless induction motor

AbstractThis paper reports results of recent applications of intelligent control techniques to a field oriented induction machine. Induction motors are characterised by complex, highly non‐linear and time‐varying dynamics and inaccessibility of some states and outputs for measurements. The advent of vector control techniques has partially solved induction motor control problems because they are sensitive to drive parameter variations and performance may deteriorate if conventional controllers such as PID are used. With fuzzy logic and neural networks based control‐lers, uncertainties in the plant parameters and non‐linearities can be dealt with more efficiently than model‐based techniques. However, a commonly known disadvantage of these methods is the lack of systematisarion and rigorous design tools. Hybrid control architectures, combining control theory with artificial intelligent tools can solve efficiently complex control problems. Three control approaches are developed and applied to adjust the speed of the drive system. The first control design combines the variable structure theory with fuzzy logic concept. In the second approach neural networks are used in an internal model control structure. Finally, a fuzzy state feedback controller is developed based on pole placement technique. A simulation study of these methods is presented. The effectiveness of these controllers is demonstrated for different operating conditions of the drive system.

On systems analysis and performance assessment in complex, high-risk work environments

In this paper, we outline our work on analysis, evaluation and assessment of military and emergency response units performing complex, high-stake tactical operations. We pursued a broad research approach and adopted a combined theorist's and practitioner's perspective to discover novel and effective ways to accomplish this objective. Experiences from this work made it possible to develop theories, methods and tools for modelling, analysis and accident prevention in precarious time-critical systems control in military missions and emergency response operations. Critical skills of individual operators and teams, mission resource management, and overall unit performance were the primary fields of study. We performed case studies, field studies, and experiments using a combined control theory, Naturalistic Decision Making and Cognitive Systems Engineering framework. We also tested these concepts in several simulated tactical operations, and finally, validated the concepts in a number of full-scale exercises.

Evaluating Tactical Real-time Interaction in Multi-agent, Dynamic, Hazardous, High-stake Operations

We pursued a broad research approach and adopted a combined theorist's and practitioner's perspective to discover novel and effective ways to model and analyze tactical response units and their missions. Experiences from our initial work on military and emergency response mission efficiency analysis made it possible to develop a set of methods and tools for modeling, analysis, and evaluation of tactical forces and their abilities at the lower organizational levels: Mission-critical skills of individual operators and teams, mission resource management, and overall unit performance. We performed case studies, field studies, and experiments using a combined control theory, Naturalistic Decision Making and Cognitive Systems Engineering framework. A composite approach was necessary to fulfill this endeavor. In this paper we outline our work on development of theories and models intended to sustain analysis, evaluation and assessment of military and emergency response units performing complex, high-stake tactical operations. We also tested these concepts in several simulated tactical operations, and finally, validated the concepts in a number of full-scale exercises.

Joint Tactical Cognitive Systems: Modeling, Analysis, and Performance Assessment

The dynamics of tactical missions are of a specific nature. Determined and forward exploitation and control of associated real-time, safety-critical operational dynamics are vital for success. An implication is the need for a conceptual framework of time-critical command and control of dynamic tasks in hazardous environments. In this work we applied a combined theorist's and practitioner's approach to the problem of modeling command and control processes of military and emergency response units, and of the respective missions themselves. We performed a pilot study using a combined Human Factors, Control Theory and Cognitive Systems Engineering (CSE) framework. We performed identification, modeling, and analysis of Joint Tactical Cognitive Systems (JTCSs), and of related command, control, intelligence and decision making processes. The COntextual COntrol Model (COCOM) yielded the descriptive power necessary to fulfill this endeavor. Together with development of fast and accurate information processing systems and decision aids, CSE will decisively impact future mission command and control.

Development of fuzzy state controller and its application to a hydraulic position servo

By combining control theory and fuzzy set theory, a new kind of state controller is proposed. Full order feedback and membership functions, which utilize the experience of experts, are used in the design of the state controller which we call a fuzzy state controller. Hydraulic position servos with a nonsymmetrical cylinder are commonly used in industry. This kind of system is nonlinear in nature and generally difficult to control. For different ending position, moving direction, strokes, and load the system dynamics is totally different. Once the above-mentioned parameters of the system are known, it is relatively straightforward to tune the gains of state controller to obtain good dynamic response. But when these parameters change, especially in case of the load, using the same gains will cause overshoot or even loss of system stability. Adaptive control is not applicable in this case due to the complexity of the algorithm, its rate of convergence, and the fast response characteristic of the system. The fuzzy state controller has been successfully applied to a hydraulic position servo. The system shows excellent robustness against variations of system parameters.

Fuzzy state controller and its application in hydraulic position servo

By combining control theory and fuzzy set theory, a new kind of state controller is proposed. The ideas of time optimal set point control and membership function are used in the design of the state controller which we call fuzzy state controller (FSC). The FSC has been applied for the postion control of a hydraulic servo. The control effects of ordinary state controller and FSC are compared for different strokes. The experimental results confirm that FSC is much more robust than the state controller when the piston stroke is changed.

Determination of flutter derivatives for the great belt bridge

A new method which combines control theory and system identification techniques has been used to extract flutter derivatives from section model tests for the Great Belt East Bridge. Tests were conducted by exciting the section model simultaneously in vertical and torsional modes of vibration. Tests were primarily conducted in smooth flow for various ratios between vertical and torsional frequencies of vibration. Limited testing was also conducted in turbulent flow and for different angles of attack. The analysis technique described allowed extraction of seven aerodynamic derivatives including coupled motion derivatives from a simple set of tests.This paper describes the control theory and system identification approach used and discusses the limitations encountered. Results are compared with flutter derivatives obtained by other researchers. The method offers a reasonably robust technique for automating the process of extracting aerodynamic derivatives from recorded time histories of coupled vertical and torsional motion.

An Assessment of Gender Effects in Self Report Delinquency

Abstract This paper investigates the relative effect of parent, school, and peer influences in male and female delinquency. The theoretical framework is a combined control theory and differential association theory. Self-report data on 304 male and female adolescents are examined. The data demonstrate that males have significantly weaker “bonds” to their parents, the school, and to their peers than females. Males also report engaging in significantly more delinquency. However, a control theory explanation of the gender differential in delinquency is not supported. Differences in the gender role identity of males and females (i.e., masculinity and femininity) also are unable to explain the gender differential in delinquency. The data lend greater support to a differential association explanation of gender differences. The implications of the findings and some alternative explanations of the gender differential are discussed.

Application of combined optimum control and estimation theory to direct digital control

This paper demonstrates methods for applying combined optimum control and estimation theory to serial systems with time delay. The case of serial linear systems with time delay is considered in detail; a result analogous to the separation theorem of linear systems is presented. Illustrative examples of serial chemical reactors, rolling mills, and second-order plus dead-time approximations of higher order systems are discussed. Numerical results for a second-order plus dead-time system are presented: these results are compared with a suboptimal feedback controller (modified Smith predictor) and the open-loop response. It is shown that, in this case, the optimum estimation and control gains may be approximated by constants which further simplify the DDC algorithm. In the second-order plus dead-time approximation to higher order overdamped systems, the optimum algorithm can be reduced to recursive estimation with constant gain and linear state variable feed forward control. This algorithm may be used as a direct replacement for digital controllers used in the process industries.