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Economic nonlinear Model Predictive Control of Offshore Vertical-Axis Wind Turbines

Economic Nonlinear Model Predictive Control of Offshore Vert...

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American Control conference (ACC)

作者： Lao, Yejun Rotea, Mario A. Koeln, Justin P. Sakib, Mohammad S. Griffith, D. Todd Univ Texas Dallas Ctr Wind Energy Mech Engn Richardson TX 75013 USA

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

Economic nonlinear Model Predictive Control (ENMPC) is used to maximize power production in the presence of constraints for a floating Vertical-Axis Wind Turbine (VAWT) under development with the support of the ARPA-e ATLANTIS program. The ENMPC is a first-generation controller that considers only the rotor dynamics. The loads applied by the rotor on the floating platform are modeled using aerodynamic force expressions without explicit consideration of the floating platform degrees of freedom. These loads and other rotor states constitute the constraints in the ENMPC formulation. VAWTs have nonlinear and periodic behavior due to the cyclic variation of the aerodynamic torque with angular blade position. Fourier series approximation of aerodynamic coefficients coupled with the softening of certain constraints reduces computational complexity and enables real-time application of ENMPC. This first-generation control solution is demonstrated on a 5-MW VAWT, with encouraging results.

关键词： Wind Turbine Control Economic nonlinear MPC Offshore Floating Vertical-Axis Wind Turbine

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Non-linear or linear propagation in high intensity blasts from firearms: where is the boundary? 30

Non-linear or linear propagation in high intensity blasts fr...

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30th International conference on Noise and Vibration Engineering, ISMA 2022 and 9th International conference on Uncertainty in Structural dynamics, USD 2022

作者： Billot, G. Marinus, B.G. Harri, K. Moiny, F. Royal Military Academy Department of Mechanical Engineering Rue Hobbema 8 Bruxelles1000 Belgium UMONS Faculty of Engineering General Physics Unit Rue de Houdain 9 Mons7000 Belgium

ISBN: (纸本)9789082893151

Acoustic nonlinearity increases the degree of modelling complexity but is nevertheless necessary to fully capture the time-frequency content of an impulsive event. Both national and international standards commonly deployed to predict free-field outdoor sound propagation from firearms (ISO 9613, NT Acou 099, ISO 17201) not only neglect nonlinear phenomena, but also fail in providing a clear-cut methodology to discriminate between linear and nonlinear regimes. Correctly establishing the extent of the transition region between the nonlinear and linear solvers allows to optimize the prediction strategies, by allocating the right computational resources where most needed. The present study attempts to make clarity on the subject by observing the evolution of key metrics in the propagation of blasts from small and large caliber weapons. On-field test campaigns were carried out to obtain the datasets needed for the analysis and the free-field propagation of the muzzle blast is simulated with a nonlinear Progressive Wave Equation solver. © 2022 Proceedings of ISMA 2022 - International conference on Noise and Vibration Engineering and USD 2022 - International conference on Uncertainty in Structural dynamics. All rights reserved.

关键词： nonlinear equations

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A Piecewise Learning Framework for Control of Unknown nonlinear Systems with Stability Guarantees 4

A Piecewise Learning Framework for Control of Unknown Nonlin...

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4th Annual conference on Learning for dynamics and Control (L4DC)

作者： Farsi, Milad Li, Yinan Yuan, Ye Liu, Jun Univ Waterloo Dept Appl Math Waterloo ON Canada Clearpath Robot Kitchener ON Canada Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China

We propose a piecewise learning framework for controlling nonlinear systems with unknown dynamics. While model-based reinforcement learning techniques in terms of some basis functions are well known in the literature, when it comes to more complex dynamics, only a local approximation of the model can be obtained using a limited number of bases. The complexity of the identifier and the controller can be considerably high if obtaining an approximation over a larger domain is desired. To overcome this limitation, we propose a general piecewise nonlinear framework where each piece is responsible for locally learning and controlling over some region of the domain. We obtain rigorous uncertainty bounds for the learned piecewise models. The piecewise affine (PWA) model is then studied as a special case, for which we propose an optimization-based verification technique for stability analysis of the closed-loop system. Accordingly, given a time-discretization of the learned PWA system, we iteratively search for a common piecewise Lyapunov function in a set of positive definite functions, where a non-monotonic convergence is allowed. This Lyapunov candidate is verified on the uncertain system to either provide a certificate for stability or find a counter-example when it fails. This counter-example is added to a set of samples to facilitate the further learning of a Lyapunov function. We demonstrate the results on two examples and show that the proposed approach yields a less conservative region of attraction (ROA) compared with alternative state-of-the-art approaches. Moreover, we provide the runtime results to demonstrate potentials of the proposed framework in real-world implementations.

关键词： nonlinear systems learning-based control system identification piecewise affine Lyapunov analysis

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nonlinear Mathematical Model of the Systems dynamics with Geometric Constraints and its Simplification Due to the Justified Linearization of Constraints

Nonlinear Mathematical Model of the Systems Dynamics with Ge...

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International conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Aleksandr Ya. Krasinskiy Anastasia N. Ilyina Dept. of Computer Modeling and Probability Theory Moscow Aviation Institute (National Research University) Moscow Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

Success in examining the dynamics of any controlled object is determined to a decisive degree by the complexity of the study, which primarily depends on the quality of modeling. Analytical mechanics offers a wide range of coordinate types to describe the dynamics of mechanical components of mechatronic systems. Modeling of the dynamics of systems with geometrical constraints requires a rigor which is unusual for technical practice. This is determined by the necessity of taking into account not only linear but also at least quadratic terms in the expansion of nonlinear constraint equations when identifying the first approximation of the equations of perturbed motion in stability and stabilization problems. The method of rigorous modeling of the dynamics of systems with geometric constraints is presented in detail in the article. Possible simplifications of the study are substantiated on the basis of these results. The effectiveness of the proposed approach is demonstrated by considering two variants of the stand Ball & Beam equilibrium.

关键词： Mechatronics Automation System dynamics dynamics Control systems Mathematical models Stability analysis Energy efficiency complexity theory Thermal stability

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Vegee Brain Automata: Ultradiscretization of Essential Chaos Transversal in Neural and Ecosystem dynamics 1st

Vegee Brain Automata: Ultradiscretization of Essential Chaos...

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1st International conference on Complex Computational Ecosystems, CCE 2023

作者： Funabashi, Masatoshi Sony Computer Science Laboratories Inc. Tokyo Japan Research Division On Social Common Capital and the Future Kyoto University Institute for the Future of Human Society Kyoto Japan

ISBN: (纸本)9783031443541

Coupled logistic equations and their discretizations are important models in ecology and complex systems science. However, the chaotic dynamics produced by these nonlinear dynamical systems are lumped together, and the mathematical correspondence between continuous and discrete-time systems is not sufficiently clear. The method of ultradiscretization, which has recently been developed in the analysis of nonlinear integrable systems, can discretize both independent variables such as time and dependent variables such as time-evolving quantities in the dynamical system, while providing an analytical basis for the mathematical correspondence with the original continuous system. In this paper, we first show that the ultradiscretization of the logistic equation has the same form as that of a sigmoidal map, which cannot be derived from a customarily used logistic map. Consequently, recursively coupled systems of sigmoidal functions, such as those employed in neural networks, emerge as new candidate models for various dynamics important in agroecology, where both autonomous dynamics of ecosystems and human intervention could be represented. We then explore qualitative correspondences between neural networks and various modes of farming, including chaotic behavior, and propose an ultra-discretized model that serves as the essential underlying element. The newly proposed model has mathematical connectivity with logistic and tent maps, as well as Holling’s disc equations, providing interpretations rooted in ecology and neuroscience. The comprehensive results provide a new perspective for extracting the essence of complex agroecology via computation, which has the potential to link the properties of deep learning being studied in neural networks to the complexity of ecological management. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Dynamical systems

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Comparison of Different Gaussian Process Models and Applications in Model Predictive Control 23

Comparison of Different Gaussian Process Models and Applicat...

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23rd International conference on Control, Automation and Systems, ICCAS 2023

作者： Diepers, Florian Polke, Dominik Ahle, Elmar Softker, Dirk University of Applied Sciences Niederrhein Faculty of Electrical Engineering and Computer Science Krefeld Germany University of Duisburg-Essen Chair of Dynamics and Control Duisburg Germany

ISBN: (纸本)9788993215274

Most advanced control methods require a sufficiently accurate model of the system to be controlled. These models are becoming increasingly difficult to generate due to the increasing complexity of the underlying systems. To address this problem, data-driven models can be used. These kind of models are trained based on data of the dynamic system. They can be used for controller design and also be applied directly to control the system. In this work, a stochastic model predictive controller (SMPC) with a Gaussian process model (GP) is utilized. The main advantage of GPs is the construction of a dynamic system model with the possibility of determining the uncertainty of its own prediction. In this contribution, three different utilizations of prediction uncertainty in SMPC are compared. Additionally, two different implementations of dynamic GPs, nonlinear autoregressive exogenous GPs (GP-NARX) and a state-space model based on GPs (GP-SSM), are considered. All six test cases are compared based on an evaluation criterion that describes the resulting control performance. A simulated inverted pendulum, a benchmark for nonlinear and unstable systems, is used as test system. In this work, the combinations of different dynamic GPs and the application of their uncertainty prediction in SMPC are evaluated and compared. © 2023 ICROS.

关键词： Stochastic models

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POD—ANN Reduced Order Macromodel of nonlinear Autonomous Dissipative System Using Machine Learning 5th

POD—ANN Reduced Order Macromodel of Nonlinear Autonomous Di...

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5th International conference on Applications of Fluid dynamics, ICAFD 2020

作者： Nagaraj, S. Seshachalam, D. Jayalatha, G. BMS College of Engineering Bengaluru560019 India RV College of Engineering Bengaluru560059 India

ISBN: (纸本)9789811919282

Real-world problems are addressed effectively and safely using simulation modeling. Simulation modeling gives highly appreciable solutions by providing a clear picture of complex systems. The reason for the same is that nonlinear structure of real-world problems with complexities cannot be handled by linearized models. Further, computational complexities of large-scale dynamical system simulations occurring in such situations will be reduced by model order reduction (MOR). This is further improved with the use of artificial neural networks (ANNs). In this paper, the generalized neural network modeling of nonlinear autonomous dissipative system using machine learning and proper orthogonal decomposition (POD) has been addressed, which is a novel one. The fluid mechanical model considered here is trained with multiple temporal swatches evolved on different parametric variations and trialed out at different time instances. Machine learning tries to learn such models by analyzing snapshot observations from active systems. The neural network developed by machine learning is able to make better prediction for validating data which is different from the training data set. The complexity of the data set generated by nonlinear autonomous dissipative system is reduced by applying the POD on the original data set. This demonstrates that the neural network is successfully utilized to macromodel the autonomous dynamical system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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Data-Driven Safe Predictive Control Using Spatial Temporal Filter-based Function Approximators

Data-Driven Safe Predictive Control Using Spatial Temporal F...

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American Control conference (ACC)

作者： Vahidi-Moghaddam, Amin Chen, Kaian Li, Zhaojian Wang, Yan Wu, Kai Michigan State Univ Dept Mech Engn E Lansing MI 48824 USA Ford Motor Co Res & Adv Engn Dearborn MI 48121 USA

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

Model predictive control (MPC) is a state-of-the-art control method that can explicitly tackle system constraints. However, its high computational cost still remains an open challenge for embedded systems. To achieve satisfactory performance with manageable computational complexity, a spatial temporal filter (STF)-based data-driven predictive control framework is developed to systematically identify system dynamics and subsequently learn the MPC policy using STF-based function approximations. Specifically, an online nonlinear system identification method that satisfies persistence of excitation (PE) is developed by using a discrete-time concurrent learning technique. An STF-based function approximation is then employed to learn the nonlinear MPC (NMPC) policy based on the identified model. Furthermore, a discrete-time robust control barrier function (RCBF) is introduced to guarantee system safety in the presence of additive disturbances and system identification errors. Finally, simulations on the cart inverted pendulum are performed to demonstrate the efficacy of the proposed control synthesis.

关键词： Robust control Additives Uncertainty System dynamics Training data Safety System identification

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An Assessment of Modeling dynamics in EEG Signals Using Learned Sparse nonlinear Representations

An Assessment of Modeling Dynamics in EEG Signals Using Lear...

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IEEE International conference on Engineering, Technology and Innovation

作者： Dragos Constantin Popescu Andrei Mateescu Ioana Livia Stefan Ioana Miruna Vlasceanu Ioan Stefan Sacala Ioan Dumitrache Faculty of Automatic Control and Computers University POLITEHNICA Bucharest Romania

ISBN: (数字)9798350362435

ISBN: (纸本)9798350362442

This work focuses on the challenge of modeling the nonlinear dynamics components of EEG signals, with the long term goal to extract valuable information regarding brain state behavior or emotional and psychological condition. At the core of the approach is the SINDy (Sparse Identification of nonlinear dynamics) method, in which the task of choosing the adequate nonlinear signal transformations and hyperparameters of the sparse regression is accomplished by using a Genetic Algorithm and a Bayesian Optimization. The performance of the two solutions is analyzed and compared against the MindBigData-EP dataset. The first results show that very good accuracy in modeling the dynamics of EEG signals, of over 80% on 60 seconds long recordings, is achieved, however, the precision is a trade-off against the complexity.

关键词： Training Accuracy Heuristic algorithms Brain modeling Hyperparameter optimization Electroencephalography nonlinear dynamical systems Bayes methods Optimization Genetic algorithms

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State and multiplicative fault reconstruction for uncertain nonlinear system: Takagi-Sugeno approach 10

State and multiplicative fault reconstruction for uncertain ...

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10th International conference on Systems and Control (ICSC)

作者： Bzioui, Sana Channa, Rafik Cadi Ayyad Univ Fac Sci Semlalia Dept Phys Marrakech Morocco

ISBN: (纸本)9781665465076

The faults can be classified into additive and multiplicative according to their effects on the outputs of the system and its dynamics. Although component faults and some actuator/sensor faults appear in multiplicative form that correspond to parameter changes in the system model or a loss of precision in the actuator/sensor, which can lead to additional complexity in observer design in the estimation framework, due to parameter changes within the process. In a nonlinear system, it is not easy to distinguish the factor of the real multiplicative fault from the mixed term. Most of the work in the literature on the estimation of faults has paid attention to those with additive effects that lead to changes only in the mean value of the system output signal. This motivated us to develop a proportional integral unknown input (PIUI) observer for uncertain systems with multiplicative sensor faults in the presence of measurement noise. Based on the TS structure and the properties of the diagonal matrix of the multiplicative faults, we transformed the multiplicative effect into an additive effect to facilitate the estimation. The application of the proposed strategy will be illustrated for a non-isothermal CSTR continuous stirred tank reactor.

关键词： Fault estimation unknown input observer multiplicative fault uncertain nonlinear system Takagi-Sugeno fuzzy models

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