Advances In Control Theory Research Articles

PATENT ANALYSIS OF EXOSKELETON MECHANISMS AND WAY TO IMPROVE THEIR COMFORT. Continuation*

The issue of creating robotic anthropomorphic mechanisms called exoskeletons for humans is relevant. The exoskeleton design became practically possible due to technological developments, creating powerful computers, sensors, and detectors, collecting info about human movements, and advances in control theory. Introduction of available achievements in the domain of exoskeleton engineering and design, as well as exoskeleton parts and their control methods, protected by patents, to a general audience is the objective of the article. The research methods are analysis of patents and scientific publications on the topic of exoskeletons, and graphical processing of research results. The result of the conducted research is the survey of the patented exoskeletons available in the scientific world. It has been found that the available exoskeleton models are still not user-friendly enough. Therefore, the authors of the article developed and patented a model of an exoskeleton with variable-length links. The model of practical implementation of variable-length link based on using magneto-rheological fluid and adjusting the link stiffness by applying an external magnetic field has been proposed. The proposed model augments the exoskeleton user experience. It has been found that intense publication growth in scientific journals on researched topics started after 2011. The number of published exoskeleton patents increased almost tenfold in the past ten years by 2021. All these demonstrate significant interest in exoskeletons design.

PATENT ANALYSIS OF EXOSKELETON MECHANISMS AND WAY TO IMPROVE THEIR COMFORT

The issue of creating robotic anthropomorphic mechanisms called exoskeletons for humans is relevant. The exoskeleton design became practically possible due to technological developments, creating powerful computers, sensors, and detectors, collecting info about human movements, and advances in control theory. Introduction of available achievements in the domain of exoskeleton engineering and design, as well as exoskeleton parts and their control methods, protected by patents, to a general audience is the objective of the article. The research methods are analysis of patents and scientific publications on the topic of exoskeletons, and graphical processing of research results. The result of the conducted research is the survey of the patented exoskeletons available in the scientific world. It has been found that the available exoskeleton models are still not user-friendly enough. Therefore, the authors of the article developed and patented a model of an exoskeleton with variable-length links. The model of practical implementation of variable-length link based on using magneto-rheological fluid and adjusting the link stiffness by applying an external magnetic field has been proposed. The proposed model augments the exoskeleton user experience. It has been found that intense publication growth in scientific journals on researched topics started after 2011. The number of published exoskeleton patents increased almost tenfold in the past ten years by 2021. All these demonstrate significant interest in exoskeletons design.

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years, with advancements in control theory and industrial practices, there has been a substantial increase in the volume of industrial data. Data-driven dynamic operation optimization techniques have emerged as effective solutions for handling complex industrial processes. By responding to dynamic environmental changes and utilizing advanced optimization algorithms, it is possible to achieve dynamic operational optimization in industrial processes, thereby reducing costs and emissions, improving efficiency, and increasing productivity. This correlates nicely with the goals set forth by conventional process operation optimization theories. Nowadays, this dynamic, data-driven strategy has shown significant potential in complex process industries characterized by multivariate correlations and nonlinear behavior. This paper approaches the subject from a data-driven perspective by establishing dynamic optimization models for complex industries and reviewing the state-of-the-art time series forecasting models to cope with changing objective functions over time. Meanwhile, aiming at the problem of concept drift in time series, this paper summarizes new concept drift detection methods and introduces model update methods to solve this challenge. In addressing the problem of solving dynamic multi-objective optimization problems, the paper reviews recent developments in dynamic change detection and response methods while summarizing commonly used as well as the latest performance measures for dynamic multi-objective optimization problems. In conclusion, a discussion of the research progress and challenges in the relevant domains is undertaken, followed by the proposal of potential directions for future research. This review will help to deeply understand the importance and application prospects of data-driven dynamic operation optimization in complex industrial fields.

Recent Advances in Control Theory for Complex Systems

Complex systems are characterized by their intricate interdependencies and emergent behavior, posing significant challenges for traditional control theory methodologies. Recent advances in control theory have emerged to address these challenges, enabling the effective control and optimization of complex systems across diverse domains. This abstract highlights key developments in control theory for complex systems, encompassing both theoretical advancements and practical applications.

Synthesis cascade estimation for aircraft system identification

PurposeThe purpose of this paper extends the authors’ previous contributions on aircraft system identification, such as open loop identification or closed loop identification, to cascade system identification. Because the cascade system is one special network system, existing in lots of practical engineers, more unknown systems are needed to identify simultaneously within the statistical environment with the probabilistic noises. Consider this problem of cascade system identification, prediction error method is proposed to identify three unknown systems, which are parameterized by three unknown parameter vectors. Then the cascade system identification is transferred as one parameter identification problem, being solved by the online subgradient descent algorithm. Furthermore, the nonparametric estimation is proposed to consider the general case without any parameterized process. To make up the identification mission, model validation process is given to show the asymptotic interval of the identified parameter. Finally, simulation example confirms the proposed theoretical results.Design/methodology/approachFirstly, aircraft system identification is reviewed through the understanding about system identification and advances in control theory, then cascade system identification is introduced to be one special network system. Secondly, for the problem of cascade system identification, prediction error method and online subgradient decent algorithm are combined together to identify the cascade system with the parameterized systems. Thirdly from the point of more general completeness, another way is proposed to identify the nonparametric estimation, then model validation process is added to complete the whole identification mission.FindingsThis cascade system corresponds to one network system, existing in lots of practice, such as aircraft, ship and robot, so it is necessary to identify this cascade system, paving a way for latter network system identification. Parametric and nonparametric estimations are all studied within the statistical environment. Then research on bounded noise is an ongoing work.Originality/valueTo the best of the authors’ knowledge, research on aircraft system identification only concern on open loop and closed loop system identification, no any identification results about network system identification. This paper considers cascade system identification, being one special case on network system identification, so this paper paves a basic way for latter more advanced system identification and control theory.

Input/output linearizing controller with Taylor series expansion for a nonminimum phase process by hardware‐in‐the‐loop approach

AbstractInput/output (I/O) linearization is a model‐based control method that receives much attention for many research studies in chemical industries. Despite many advances in control theory, the I/O linearization is not widespread for a real‐time implementation when compared with other model‐based techniques. It requires differentiation and inversion of the process model—which becomes cumbersome as the complexity of the model increases. Thus, this work presents the development and experiment of an embedded I/O feedback control device with a pilot process that emulates an exothermic reactor with nonminimum phase behavior. Algorithms of an approximate I/O linearization control system is truncated by using Taylor series expansion, and it is embedded into a field‐programmable gate array control device. The effectiveness of the embedded approximate I/O controller is compared with a digital proportional‐integral (PI) controller for a test with the pilot process with a hardware‐in‐the‐loop concept. The results show that the embedded model‐based controller provides high performance for the servo problem and disturbance rejection, compared with the PI controller. The proposed embedded model‐based controller shows the application to apply the advance control method into the field‐programmable gate array hardware as a stand‐alone control device, and it can perform the computation of a complex algorithm.

Trade-offs between driving nodes and time-to-control in complex networks

Recent advances in control theory provide us with efficient tools to determine the minimum number of driving (or driven) nodes to steer a complex network towards a desired state. Furthermore, we often need to do it within a given time window, so it is of practical importance to understand the trade-offs between the minimum number of driving/driven nodes and the minimum time required to reach a desired state. Therefore, we introduce the notion of actuation spectrum to capture such trade-offs, which we used to find that in many complex networks only a small fraction of driving (or driven) nodes is required to steer the network to a desired state within a relatively small time window. Furthermore, our empirical studies reveal that, even though synthetic network models are designed to present structural properties similar to those observed in real networks, their actuation spectra can be dramatically different. Thus, it supports the need to develop new synthetic network models able to replicate controllability properties of real-world networks.

Recent Advances in Control Theory and Applications in Turkey

TOK’14, the National Symposium on Automatic Control, is an annually organized event bringing together the researchers from various universities and industry in Turkey. The symposium in 2014 was hosted by the Kocaeli University on 11–13 September in Kocaeli. The current special issue contains eight papers from the symposium programme, which had more than 200 contributions. Control theory is used for strategically cutting-edge high technologies, which provide important support for the sustainable development of flexible and autonomous manufacturing, medicine, automative, space, aerospace, finance, robotics, power and energy systems, defence, networking, etc. The focus of the special issue is to report the recent advances in control theory and applications in Turkey. Recently, national conferences and symposia are becoming hosts for more qualified research studies and industrial projects, as the research and development funds supported by the Turkish government have been rapidly increased. The goal of this special issue is to provide a forum for the latest research and projects, focusing on control theory and their industrial applications. The first paper, contributed by Morgul et al., proposed an alternative approach that steers away from explicit mechanical modelling towards data-driven system identification employed in vertical hopping robots. This identification approach treats the system as a black box and produces empirical models using harmonic transfer functions (HTF). Using this method reduces the complexity of the mathematical models and approximates unmodelled dynamics in legged locomotion robots. They showed that HTFs can be used as predictors of simple locomotion models on training data and their performance on sinusoid inputs is much better than on step inputs. The second paper, by Ozdogan and Leblebicioglu, presented modelling of a single axis (inner azimuth gimbal) of a four-axis gyro-stabilized electro-optic gimbal system using frequency response function (FRF). FRF is existing technique in the literature but its application to an electro-optic gimbal system is a challenging problem. The third paper, contributed by Efe et al., compared the performance of the controllers, namely proportional–integral–derivative control, sliding mode control, backstepping control, feedback linearization-based control and fuzzy control, based on integral absolute error (IAE), maximum absolute error (MAE), integral squared error (ISE), integral time squared error (ITSE) and error variance (EV) for Quadrotor-type aerial vehicles. They found that sliding mode control is better than the others in terms of simplicity and closed-loop control performance. In the fourth paper, Iplikci and Bahtiyar developed the Runge–Kutta model predictive controller (RKMPC) implemented in real time on a field-programmable gate array (FPGA) in an electromagnetic levitation system. They compared performance of the RKMPC with a conventional nonlinear model predictive controller (NMPC) method for step and sinusoidal inputs. They found that the RKMPC provides very satisfactory control performance for a very fast experimental non-linear system. The fifth paper, authored by Alagoz and Kaygusuz, examined smart grid energy market management performance of a fractional-order proportional–integral (PI) controller in the case of communication and operation delays in a multisource energy market model. The simulation results showed that using of the fractional-order PI controller decreases the average energy shortage error and price volatility in the smart grid energy markets. Hence, it can be a good candidate for automated market management applications. In the sixth paper, Bagis and Konar studied a performance comparison of the Sugeno and Mamdani-type fuzzy models optimized by the artificial bee colony (ABC) algorithm with the other modelling approaches in the literature for a microstrip antenna. The ABC algorithm is found to be a powerful candidate for solving the numerical optimization of fuzzy models. In the seventh paper, Leblebicioglu et al. solved the path planning problem using a genetic algorithm (GA) with the proposal of novel evolutionary operators for multiple unmanned aerial vehicles (UAVs). They obtained nearly global optimum flight path solutions regarding UAV dynamics. Real-world experiments conducted using small UAVs with

Closed-Loop Turbulence Control: Progress and Challenges

Closed-loop turbulence control is a critical enabler of aerodynamic drag reduction, lift increase, mixing enhancement, and noise reduction. Current and future applications have epic proportion: cars, trucks, trains, airplanes, wind turbines, medical devices, combustion, chemical reactors, just to name a few. Methods to adaptively adjust open-loop parameters are continually improving toward shorter response times. However, control design for in-time response is challenged by strong nonlinearity, high-dimensionality, and time-delays. Recent advances in the field of model identification and system reduction, coupled with advances in control theory (robust, adaptive, and nonlinear) are driving significant progress in adaptive and in-time closed-loop control of fluid turbulence. In this review, we provide an overview of critical theoretical developments, highlighted by compelling experimental success stories. We also point to challenging open problems and propose potentially disruptive technologies of machine learning and compressive sensing.

Advances in Control Theory and Applications: Selected papers from TOK 2013

The Turkish National Committee of Automatic Control (TOK) is the National Member Organization (NMO) of IFAC in Turkey. The TOK meeting is a major national meeting on automatic control, which has been organized annually since 1994 with the aim of reporting on recent advances in control theory and applications in Turkey. The 15th TOK national meeting (TOK 2013) was held at Inonu University in Malatya, Turkey, on 26–28 September 2013. It was organized by Inonu University and Istanbul Technical University, and supported by TOK. All papers submitted to TOK 2013 were reviewed by three separate reviewers; 185 papers were accepted for oral presentation and 63 papers for poster presentation. The TOK 2013 technical programme consisted of 31 oral sessions (three being special sessions) and four poster sessions. The programme also included three plenary lectures, given by leading experts in the field – Professors Derek P. Atherton from the University of Sussex, UK, YangQuan Chen from the University of California, Merced, USA, and _ Ismail Yuksek from Yildiz Technical University, Turkey. A preconference workshop programme was organized on 25 September, with lectures presented by Professor Derek P. Atherton and Professor YangQuan Chen. All accepted papers for TOK 2013, which were written in Turkish, were screened for this special issue of the Transactions of the Institute of Measurement and Control. Eleven papers were selected and extended versions, written in English, were further reviewed by three different reviewers, whom we thank for their efforts. These papers constitute this special issue. ‘Design of a multiple-model switching controller for ABS braking dynamics’, by M. Dousti, S.C. Baslamisli, E. T. Onder and S. Solmaz: the purpose of this study is to design high-performance active braking control and observer algorithms for passenger vehicles equipped with electromechanical brake systems. These algorithms are designed to be adaptive with changing driving and road conditions in a switched multiple-model manner to ensure high performance and robustness. The effectiveness of a set of multiple-model switching lead-lag controllers is evaluated during transitions between different road friction coefficients. Meanwhile, a multiple-model switching observer algorithm is developed to estimate the shape of the tyre braking force curve with respect to the longitudinal slip. Each switched observer predicts signals according to its preset tyre model. The observers are designed based on different Burckhardt tyre models that are parameterized for different road conditions. In the simulations, the value of the friction coefficient is assumed to be unknown and our switching algorithms are observed to estimate successfully the varying friction coefficients by comparing a quadratic cost function of measured signals from the vehicle with signals generated by observers. It is demonstrated that the proposed algorithms provide high reliability and fast response, thus ensuring a stopping distance close to the theoretical minimum. ‘Receding horizon HN control of time-delay systems’, by F.Y. Tas xc xikaraoglu, L. Ucun and I.B. Kuc xukdemiral: this paper deals with the disturbance rejection problem for discrete-time linear systems having time-varying state delays and control constraints. The study proposes a novel receding horizon HN control method utilizing a linear matrix inequality based optimization algorithm, which is solved in each step of run-time. The proposed controller attenuates disturbances having bounded energies on controlled output, and ensures the closed-loop stability and dissipation while meeting the physical control input constraints. The originality of the work lies on the extension of the idea of the well-known HN receding horizon control technique developed for linear discretetime systems to interval time-delay systems having timevarying delays. The efficiency of the proposed method is illustrated through simulation studies that are carried out on a couple of benchmark problems. ‘Stability region analysis in Smith predictor configurations using a PI controller’, by F.N. Deniz, N. Tan, S.E. Hamamci and I. Kaya. This paper deals with the stabilization problem

Interface of Systems Biology and Engineering: Principles and Applications

Systems biology is not new but in recent years it has begun to receive renewed attention. Properties of biological systems such as robustness and fragility emerge as central issues which can be well understood by application of principles of engineering to the biological systems. Besides this automatic control has played a vital role in the advance of engineering and science. Advances in control theory provide the means for obtaining efficient performance of the dynamic systems and improving productivity. In this context some of the recent and important applications of engineering principles particularly control theory in biology are discussed in this review.

Editorial: Recent Advances in Control Theory and Applications in Turkey

Editorial: Recent Advances in Control Theory and Applications in Turkey

Towards improved automation for desalination processes, Part I: Advanced control

The desalination process industries have largely tried to solve control problems by conventional rather than modern control techniques that are attracting widespread industrial applications and revolutionizing process control as a result of advances in control theory. Control systems are designed from mathematical models that are generally imperfect descriptions of real processes. It is widely accepted that conventional PID or PI controllers may not provide satisfactory performance in view of non-linearity and uncertainty in the real world of desalination processes. It is essential that control systems operate satisfactorily over a wide range of process conditions. The practice of multi stage flash (MSF) desalination process control, process design, optimization, conspicuously lags behind contemporary theoretical developments. Conventional process control techniques do not ensure the safest and the most profitable operation of processes when compared with advanced process control which uses the knowledge of process economics and provides insight into the process to maintain essential variables at optimum setpoints. This paper is an attempt to promote excellence and innovation in desalination engineering and to remove barriers to advanced process control. Attempted here is brief clarification of some aspects of advanced control strategies in terms of answers to the questions: Advance control strategies: What are they? Where and when are they applicable? and What can be expected of them? Although the reference here is to MSF plants, some of the observations and remarks are also relevant to process control practice in general. In the end, the conclusions point out the complexity of the real world control problem which behoves us to advocate the application of techniques such as self-optimizing control strategies and artificial intelligence (AI) leading to an integrated system of intelligent control.

An Industrial Challenge to Control System Educators

Most engineers earn their livelihood in industry. Technological advancements and industry trends guarantee that these engineers will face significant challenges during their career. Meanwhile, advancements in control theory insure that many diverse control techniques will be used. This paper identifies seven challenges for educators who are preparing tomorrow’s control system engineers.

Robust controller design for a nonlinear CSTR

A design methodology is presented for the analysis and synthesis of robust linear controllers for a nonlinear CSTR. Operating regions are defined in the phase plane in which the maintenance of robust stability and the achievement of robust performance levels are guaranteed. The results are based upon new extensions of the structured singular value theory to a class of nonlinear and time-varying systems. Comparisons are drawn between these results and previous Lyapunov function approaches to stability analysis, and extensions are proposed for the application of this methodology to more complex schemes. This paper provides insight into a novel condition for nonlinear robust performance and the first application of recent advances in control theory to a chemical process.

Control system design for a fixed-bed methanation reactor

A systematic approach is applied to the design of robust controllers for a fixed-bed, non-adiabatic, laboratory methanation reactor, a unit inherently difficult to control. The fundamental limitations to the achievable closed-loop performance are established by both physical arguments and dynamic resilience considerations. The Internal Model Control (IMC) structure provides a suitable framework for this analysis. IMC controllers are designed and their performance in the face of model uncertainty is tested both by simulation and by means of the Structured Singular Value (μ) analysis technique. The paper provides a basic insight into the characterization and solution of control problems that are particular to fixed-bed reactor systems and constitutes the first account of the application of several recent advances in control theory to a complex chemical process.

Some recent computational advances in control theory

Some recent computational advances in control theory