Methods of system identification, parameter estimation and optimisation applied to problems of modelling and control in engineering and physiology

Mathematical and computer-based models provide the foundation of most methods of engineering design. They are recognised as being especially important in the development of integrated dynamic systems, such as “control-configured” aircraft or in complex robotics applications. These models usually involve combinations of linear or nonlinear ordinary differential equations or difference equations, partial differential equations and algebraic equations. In some cases models may be based on differential algebraic equations. Dynamic models are also important in many other fields of research, including physiology where the highly integrated nature of biological control systems is starting to be more fully understood. Although many models may be developed using physical, chemical, or biological principles in the initial stages, the use of experimentation is important for checking the significance of underlying assumptions or simplifications and also for estimating appropriate sets of parameters. This experimental approach to modelling is also of central importance in establishing the suitability, or otherwise, of a given model for an intended application – the so-called “model validation” problem. System identification, which is the broad term used to describe the processes of experimental modelling, is generally considered to be a mature field and classical methods of identification involve linear discrete-time models within a stochastic framework. The aspects of the research described in this thesis that relate to applications of identification, parameter estimation and optimisation techniques for model development and model validation mainly involve nonlinear continuous time models Experimentally-based models of this kind have been used very successfully in the course of the research described in this thesis very in two areas of physiological research and in a number of different engineering applications. In terms of optimisation problems, the design, experimental tuning and performance evaluation of nonlinear control systems has much in common with the use of optimisation techniques within the model development process and it is therefore helpful to consider these two areas together. The work described in the thesis is strongly applications oriented. Many similarities have been found in applying modelling and control techniques to problems arising in fields that appear very different. For example, the areas of neurophysiology, respiratory gas exchange processes, electro-optic sensor systems, helicopter flight-control, hydro-electric power generation and surface ship or underwater vehicles appear to have little in common. However, closer examination shows that they have many similarities in terms of the types of problem that are presented, both in modelling and in system design. In addition to nonlinear behaviour; most models of these systems involve significant uncertainties or require important simplifications if the model is to be used in a real-time application such as automatic control. One recurring theme, that is important both in the modelling work described and for control applications, is the additional insight that can be gained through the dual use of time-domain and frequency-domain information. One example of this is the importance of coherence information in establishing the existence of linear or nonlinear relationships between variables and this has proved to be valuable in the experimental investigation of neuromuscular systems and in the identification of helicopter models from flight test data. Frequency-domain techniques have also proved useful for the reduction of high-order multi-input multi-output models. Another important theme that has appeared both within the modelling applications and in research on nonlinear control system design methods, relates to the problems of optimisation in cases where the associated response surface has many local optima. Finding the global optimum in practical applications presents major difficulties and much emphasis has been placed on evolutionary methods of optimisation (both genetic algorithms and genetic programming) in providing usable methods for optimisation in design and in complex nonlinear modelling applications that do not involve real-time problems. Another topic, considered both in the context of system modelling and control, is parameter sensitivity analysis and it has been found that insight gained from sensitivity information can be of value not only in the development of system models (e.g. through investigation of model robustness and the design of appropriate test inputs), but also in feedback system design and in controller tuning. A technique has been developed based on sensitivity analysis for the semi-automatic tuning of cascade and feedback controllers for multi-input multi-output feedback control systems. This tuning technique has been applied successfully to several problems. Inverse systems also receive significant attention in the thesis. These systems have provided a basis for theoretical research in the control systems field over the past two decades and some significant applications have been reported, despite the inherent difficulties in the mathematical methods needed for the nonlinear case. Inverse simulation methods, developed initially by others for use in handling-qualities studies for fixed-wing aircraft and helicopters, are shown in the thesis to provide some important potential benefits in control applications compared with classical methods of inversion. New developments in terms of methodology are presented in terms of a novel sensitivity based approach to inverse simulation that has advantages in terms of numerical accuracy and a new search-based optimisation technique based on the Nelder-Mead algorithm that can handle inverse simulation problems involving hard nonlinearities. Engineering applications of inverse simulation are presented, some of which involve helicopter flight control applications while others are concerned with feed-forward controllers for ship steering systems. The methods of search-based optimisation show some important advantages over conventional gradient-based methods, especially in cases where saturation and other nonlinearities are significant. The final discussion section takes the form of a critical evaluation of results obtained using the chosen methods of system identification, parameter estimation and optimisation for the modelling and control applications considered. Areas of success are highlighted and situations are identified where currently available techniques have important limitations. The benefits of an inter-disciplinary and applications-oriented approach to problems of modelling and control are also discussed and the value in terms of cross-fertilisation of ideas resulting from involvement in a wide range of applications is emphasised. Areas for further research are discussed

The OpenModelica integrated environment for modeling, simulation, and model-based development

OpenModelica is a unique large-scale integrated open-source Modelica- and FMI-based modeling, simulation, optimization, model-based analysis and development environment. Moreover, the OpenModelica environment provides a number of facilities such as debugging; optimization; visualization and 3D animation; web-based model editing and simulation; scripting from Modelica, Python, Julia, and Matlab; efficient simulation and co-simulation of FMI-based models; compilation for embedded systems; Modelica- UML integration; requirement verification; and generation of parallel code for multi-core architectures. The environment is based on the equation-based object-oriented Modelica language and currently uses the MetaModelica extended version of Modelica for its model compiler implementation. This overview paper gives an up-to-date description of the capabilities of the system, short overviews of used open source symbolic and numeric algorithms with pointers to published literature, tool integration aspects, some lessons learned, and the main vision behind its development.Fil: Fritzson, Peter. Linköping University; SueciaFil: Pop, Adrian. Linköping University; SueciaFil: Abdelhak, Karim. Fachhochschule Bielefeld; AlemaniaFil: Asghar, Adeel. Linköping University; SueciaFil: Bachmann, Bernhard. Fachhochschule Bielefeld; AlemaniaFil: Braun, Willi. Fachhochschule Bielefeld; AlemaniaFil: Bouskela, Daniel. Electricité de France; FranciaFil: Braun, Robert. Linköping University; SueciaFil: Buffoni, Lena. Linköping University; SueciaFil: Casella, Francesco. Politecnico di Milano; ItaliaFil: Castro, Rodrigo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Franke, Rüdiger. Abb Group; AlemaniaFil: Fritzson, Dag. Linköping University; SueciaFil: Gebremedhin, Mahder. Linköping University; SueciaFil: Heuermann, Andreas. Linköping University; SueciaFil: Lie, Bernt. University of South-Eastern Norway; NoruegaFil: Mengist, Alachew. Linköping University; SueciaFil: Mikelsons, Lars. Linköping University; SueciaFil: Moudgalya, Kannan. Indian Institute Of Technology Bombay; IndiaFil: Ochel, Lennart. Linköping University; SueciaFil: Palanisamy, Arunkumar. Linköping University; SueciaFil: Ruge, Vitalij. Fachhochschule Bielefeld; AlemaniaFil: Schamai, Wladimir. Danfoss Power Solutions GmbH & Co; AlemaniaFil: Sjolund, Martin. Linköping University; SueciaFil: Thiele, Bernhard. Linköping University; SueciaFil: Tinnerholm, John. Linköping University; SueciaFil: Ostlund, Per. Linköping University; Sueci

Space Station Systems: a Bibliography with Indexes (Supplement 8)

This bibliography lists 950 reports, articles, and other documents introduced into the NASA scientific and technical information system between July 1, 1989 and December 31, 1989. Its purpose is to provide helpful information to researchers, designers and managers engaged in Space Station technology development and mission design. Coverage includes documents that define major systems and subsystems related to structures and dynamic control, electronics and power supplies, propulsion, and payload integration. In addition, orbital construction methods, servicing and support requirements, procedures and operations, and missions for the current and future Space Station are included

Design Optimization and Dynamic Simulation of Steam Cycle Power Plants: A Review

After more than one century from its first use for electric power production, steam cycles are still the object of continuous research and development efforts worldwide. Indeed, owing to its favorable thermodynamic properties, steam cycles are not only used in coal-fired power plants but in a large variety of applications such as combined cycles, concentrated solar power plants and polygeneration plants. On the other hand, to cope with the efficiency and flexibility requirements set by today’s energy markets, the design and the operation of steam cycles must be carefully optimized. A key rule is played by the simulation and optimization codes developed in the last 30 years. This paper provides an introduction to the main types of simulation and optimization problems (design, off-design operation and dynamic), an overview of the mathematical background (possible solution approaches, numerical methods and available software), and a review of the main scientific contributions

Intelligent algorithm for the optimization of assembly and handling systems and processes for in-line production

Povečanje konkurenčnosti podjetij je v veliki meri odvisno od učinkovitosti montažnih in strežnih sistemov ter procesov (MiSSP). Njihovo učinkovitost lahko povečamo z različnimi metodami optimizacije, predvsem z vidika zmanjševanja stroškov, skrajševanja pretočnih časov, dobavnih terminov, povečanja izkoriščenosti opreme itd. Ena najbolj učinkovitih metod optimiranja takšnih sistemov je optimiranje s sprotno simulacijo. Takšen pristop zahteva podrobne raziskave, študije in analizo vseh gradnikov ter parametrov, ki so potrebni, da se postavi ekspertni sistem sprotne oziroma "On-line" simulacije MiSSP linijske proizvodnje. V doktorski disertaciji je zato podrobno obravnavan razvoj inteligentnih algoritmov in uporaba le-teh za razvoj digitalnih agentov ter ekspertnih sistemov MiSSP linijske proizvodnje. Ekspertni sistem, razvit v okviru doktorske naloge, v povezavi z digitalnim dvojčkom in digitalnimi agenti nenehno nadzoruje in sproti optimira MiSSP linijske proizvodnje. Prav tako je v doktorskem delu razvit inteligentni algoritem, imenovan "premešaj in vstavi", ki npr. samodejno predlaga najboljše rešitve razporejanja naročil, strojev itd. v krajšem času kot uveljavljeni primerljivi algoritmi. Za potrebe validacije ekspertnega sistema z inteligentnim algoritmom je bil v laboratorijskem okolju zgrajen realni montažni in strežni sistem linijske proizvodnje. Digitalni MiSSP smo združili z realnim sistemom preko oblaka in s tem postavili vse potrebne okvire sprotne ali "On-line" simulacije in tako razvili ekspertni sistem, ki je v nenehni povezavi z realnim sistemom in ga sproti nadzoruje ter optimira. Postavljena metodologija zasnove inteligentnega algoritma, digitalnih agentov in digitalnih dvojčkov omogoča okvir za njihovo praktično uporabo v realnem proizvodnem okolju.Successful improvement of the competitiveness of enterprises depends to a large extent on the efficiency of assembly and handling systems and processes (AHSP). Their efficiency can be enhanced through various optimization methods, in particular in terms of the cost reduction, reduction of the throughput times, delivery times, increased utilization of equipment, etc. One of the most effective methods for optimizing such systems is optimization with on-line simulation. Such approach requires detailed research, study and analysis of all the building blocks and parameters needed to set up an expert system of on-line simulation of AHSP of the production line. Therefore, in the doctoral thesis, the development of intelligent algorithms and the use of them for the development of digital agents and expert systems of AHSP production line is discussed in detail. The expert system, developed in the doctoral thesis, in connection with the digital twin and digital agents, constantly monitors and continuously optimizes AHSP of the production line. In the doctoral thesis, an intelligent algorithm, called "flip and insert" is developed that can automatically suggest a very competitive schedule of orders, machines, etc. in a shorter time than well-established comparable algorithms. For the needs of validating the expert system with an intelligent algorithm, a real system of production line has been built in the laboratory environment. We combined the digital AHSP with the real system over the cloud, and thus set up all the necessary frameworks of the on-line simulation and thus develop an expert system that is in constant connection with the real system and is constantly monitoring and optimizing it. The methodology for intelligent algorithm, digital agents and digital twins provides a framework for their practical application in a real production environment