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检索条件"任意字段=Symposium on Dynamics and Control of Process Systems"

共 1794 条记录，以下是71-80 订阅

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Unmanned and Autonomous systems: Future of Automation in process and Energy Industries 13th

Unmanned and Autonomous Systems: Future of Automation in Pro...

引用

13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Borghesan, Francesco Zagorowska, Marta Mercangoz, Mehmet Imperial Coll London Dept Chem Engn South Kensington Campus London SW7 2AZ England

process and energy industries have been recognised as adopters of high levels of automation compared to other sectors. Nonetheless, human cognitive input still plays a critical role in the operation of process plants and replication of these cognitive capabilities remains a key challenge for advancing automation levels. In this paper, we provide an analysis of process and energy industries based on a scenario of reduced availability of skilled labour and increased demands for safety, sustainability, and resilience. We consider the different mechanical, sensing, situational awareness, and decision-making tasks involved in the operation of plants and map them to possible realisations of unmanned and autonomous systems. We discuss the implications of current technology capabilities and future technology development perspectives, the factors influencing the complexity of operation in process plants, and the importance of human-machine collaboration. As part of autonomous system capabilities, we consider adaptation as a key capability and we make a connection to adaptation of model-based solutions. We argue that reaching higher and wider levels of autonomy requires a rethink of the design processes for both the physical plants as well as the way automation, control, and safety solutions are conceptualised. Copyright (C) 2022 The Authors.

关键词： Autonomous systems process automation process control machine learning artificial intelligence unmanned operation fault detection supervisory control functional safety human-machine interface process monitoring performance optimization

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Feedforward and output feedback control of the cell distribution in chemostat yeast fermentations 13th

Feedforward and output feedback control of the cell distribu...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Jerono, P. Schaum, A. Meurer, T. Univ Kiel Fac Engn Automat & Control Grp Kiel Germany

A two degree of freedom control strategy is developed and experimentally validated for a set-point change along a desired trajectory of the cell distribution in yeast fermentation. Based on a mathematical model of yeast growth and cell population dynamics, an inversion based feedforward controller is designed, utilizing a transformation between the biomass concentration and the cell distribution at the equilibrium points. To take disturbances and model inaccuracies into account, the controller is extended by a feedback control law. The controller is applied to a lab scale stirred tank reactor with Saccharomyces cerevisiae. Copyright (C) 2022 The Authors.

关键词： Cell population balance models partial integro-differential equation yeast fermentation process control tracking control

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Using SIR Epidemic Modeling and control to Teach process dynamics and control to Chemical Engineers 13

Using SIR Epidemic Modeling and Control to Teach Process Dyn...

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13th IFAC symposium on Advances in control Education (ACE)

作者： Rivera, D. E. El Mistiri, M. Shi, Z. Arizona State Univ Sch Engn Matter Transport & Energy Ira A Fulton Sch Engn Control Syst Engn Lab Tempe AZ 85281 USA

The COVID-19 pandemic has brought about unprecedented opportunities to introduce control systems topics in the undergraduate engineering curriculum. This paper describes two computer modeling assignments based on MATLAB with Simulink developed for CHE 461: process dynamics and control taught at Arizona State University during the fall 2020 semester. A myriad of important concepts, among these dynamic modeling using conservation and accounting principles, linearization, state-space system and transfer function model representations, PID feedback control and Internal Model control design can be applied to the problem and explained to students in the context of a significant world event representing a unique "process" system, notably the COVID-19 pandemic. Copyright (C) 2022 The Authors.

关键词： process control applications epidemic modeling and control model-based control computer-based control education

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Passivity of Single-phase Thermodynamic systems with Equilibrium Reactions 13th

Passivity of Single-phase Thermodynamic Systems with Equilib...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Cai, Zicheng Ydstie, B. Erik Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA

Classical thermodynamics has the standard assumption that there should exist a once differentiable and homogeneous of degree one entropy function with a strict maximum. Based on the concavity assumption, we derive a convex storage function called thermodynamic availability that can be used for passivity-based control design. In this paper, we show that a thermodynamic system with equilibrium reactions controlled by feedback controllers is stable. In particular, the mapping from the feed flow rate to pressure and from the heating/cooling rate to temperature is passive. Simulations of a methane steam reforming reactor provide an example of the control design. Copyright (C) 2022 The Authors.

关键词： Equilibrium Chemical Reaction process control Stability Analysis Thermodynamics

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Stable Deployment control of a Multi-tethered Formation System Considering the Spinning Motion of Parent Satellite

Stable Deployment Control of a Multi-tethered Formation Syst...

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Asia-Pacific International symposium on Aerospace Technology (APISAT)

作者： Chen, Shumin Liu, Chenguang Zabolotnov, Yu. M. Li, Aijun Northwestern Polytech Univ Sch Automat Xian 710072 Shaanxi Peoples R China Samara Natl Res Univ Fac Informat Samara 443086 Russia

ISBN: (纸本)9789811926358;9789811926341

Tethered satellite formation systems have attracted significant attention in recent years, primarily because they offer potential advantages for certain space missions, such as space interferometry measurement. This work considers the stable deployment of a spinning multi-mass tethered system arranged in a hub-spoke configuration in the orbital plane. The system contains a parent satellite (hub) modeled as a rigid body, and several sub-satellites connected to the hub via inelastic tethers (spokes). The deployment dynamics are derived using Lagrange's equations. The spinning motion of the parent satellite is controlled by active torque, while tether deployment is conducted by release mechanisms on the parent satellite and low-thrust engines installed on each sub-satellite. Considering the physical restraints of tether tension during the deployment process, an optimal controller is proposed using Bellman dynamic programming, based on a simplified dynamic model. Then, the obtained controller is employed in the complete model, where the coupling effect between the spinning of parent body and tether deployment are taken into account. Finally, numerical simulations are presented to illustrate the effectiveness of the proposed control strategy.

关键词： Multi-tethered satellite formation Tether deployment Spinning motion Bellman dynamic programming Optimal control

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Distributionally Robust MPC for Nonlinear systems 13th

Distributionally Robust MPC for Nonlinear Systems

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Zhong, Zhengang Del Rio-Chanona, Ehecatl Antonio Petsagkourakis, Panagiotis Imperial Coll London Sargent Ctr Proc Syst Engn London England

Classical stochastic model predictive control (SMPC) methods assume that the true probability distribution of uncertainties in controlled systems is provided in advance. However, in real-world systems, only partial distribution information can be acquired for SMPC. The discrepancy between the true distribution and the distribution assumed can result in suboptimality or even infeasibility of the system. To address this, we present a novel distributionally robust data-driven MPC scheme to control stochastic nonlinear systems. We use distributionally robust constraints to bound the violation of the expected state-constraints under process disturbance. Sequential linearization is performed at each sampling time to guarantee that the system's states comply with constraints with respect to the worst-case distribution within the Wasserstein ball centered at the discrete empirical probability distribution. Under this distributionally robust MPC scheme, control laws can be efficiently derived by solving a conic program. The competence of this scheme for disturbed nonlinear systems is demonstrated through two case studies. Copyright (C) 2022 The Authors.

关键词： Model predictive control

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Multilevel Monte-Carlo for Solving POMDPs Online 1

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19th International symposium of Robotics Research (ISRR)

作者： Hoerger, Marcus Kurniawati, Hanna Elfes, Alberto Australian Natl Univ Res Sch Comp Sci Canberra ACT Australia CSIRO Data61 Robot & Autonomous Syst Grp Pullenvale Qld Australia

ISBN: (数字)9783030954598

ISBN: (纸本)9783030954598;9783030954581

Planning under partial obervability is essential for autonomous robots. A principled way to address such planning problems is the Partially Observable Markov Decision process (POMDP). Although solving POMDPs is computationally intractable, substantial advancements have been achieved in developing approximate POMDP solvers in the past two decades. However, computing robust solutions for systems with complex dynamics remains challenging. Most on-line solvers rely on a large number of forward-simulations and standard Monte-Carlo methods to compute the expected outcomes of actions the robot can perform. For systems with complex dynamics, e.g., those with non-linear dynamics that admit no closed form solution, even a single forward simulation can be prohibitively expensive. Of course, this issue exacerbates for problems with long planning horizons. This paper aims to alleviate the above difficulty. To this end, we propose a new on-line POMDP solver, called Multilevel POMDP Planner (MLPP), that combines the commonly known Monte-Carlo-Tree-Search with the concept of Multilevel Monte-Carlo to speed-up our capability in generating approximately optimal solutions for POMDPs with complex dynamics. Experiments on four different problems of POMDP-based torque control, navigation and grasping indicate that MLPP substantially outperforms state-of-the-art POMDP solvers.

关键词： Partially Observable Markov Decision process POMDP Monte-Carlo

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Implementation of control Structure for Steel Pickling process using Model Predictive controller 13th

Implementation of Control Structure for Steel Pickling Proce...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Lee, Ho Sub Park, PooGyeon POSTECH Pohang 37573 South Korea

This paper addresses the suitable control structure for a pickling process. Each pickling tank is modeled based on machine learning using recurrent neural network structure. In the steel pickling process, four pickling tanks are addressed. To control the acid concentration of the four pickling tanks effectively, each model predictive controller that controls two pickling tanks is designed. In addition, by designing the model predictive controller that considers the characteristics of the steel type, a suitable structure that can wash the oxide scale on the steel surface is proposed. The simulation results show the validity of the proposed structure and controller. Copyright (C) 2022 The Authors.

关键词： Steel pickling process process control pickling tank acid concentration control

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Gaussain process-based MPC for DC Motor Servo System with State Constraints and Load Disturbance

Gaussain Process-based MPC for DC Motor Servo System with St...

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International symposium on Autonomous systems (ISAS)

作者： Ning Wang Xiaojie Qiu Changqing Long Jun Chen Wenchao Meng College of Control Science and Engineering Zhejiang University Hangzhou P. R. China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

The effectiveness of Model Predictive control (MPC) heavily depends on the precision of the model in accurately representing the dynamics of the plant. However, identifying such models can be challenging due to complex dynamics and unknown or changing disturbances. One strategy is to utilize a simple and fixed nominal model while learning the unknown deviation between this nominal model and the actual plant dynamics. Gaussian process (GP) regressions have demonstrated their value as a reliable tool for predicting disturbances and model mismatches, thereby facilitating their incorporation into MPC predictions. This paper introduces a framework for learning the dynamics of load disturbance in the DC motor servo system. We evaluate and compare the performance of the GP-MPC controller with that of a conventional MPC controller. Results indicate that the GP-MPC controller outperforms the conventional MPC controller in position servo performance during load disturbance while adhering to input saturation and state constraints.

关键词： Predictive models DC motors Prediction algorithms Mathematical models Real-time systems Reliability Servomotors

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A Distributed Least-Squares Solver for Linear systems of Algebraic Equations 13th

A Distributed Least-Squares Solver for Linear Systems of Alg...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Jahvani, Mohammad Guay, Martin Queens Univ Dept Chem Engn Kingston ON Canada

This paper introduces a consensus-based continuous-time distributed algorithm to find the least-squares solution to overdetermined systems of linear algebraic equations over directed multi-agent networks. It is assumed that each agent has only access to a subsystem of the algebraic equations, and the underlying communication network is strongly connected. We show that, along the flow of the proposed algorithm, the local estimate of each agent converges exponentially to the exact least-squares solution, provided that the aggregate system of linear equations has full column rank, and each agent knows an upper bound on the total number of the participating agents in the network. Copyright (C) 2022 The Authors.

关键词： Multi-agent systems Distributed control and estimation Sensor networks

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