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检索条件"主题词=Model predictive and optimization-based control"

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Adaptive Learning of Hybrid models for Nonlinear model predictive control of Distillation Columns 16

Adaptive Learning of Hybrid Models for Nonlinear Model Predi...

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16th IFAC Symposium on Advanced control of Chemical Processes (ADCHEM)

作者： Luethje, Jannik T. Schulze, Jan C. Caspari, Adrian Mhamdi, Adel Mitsos, Alexander Schaefer, Pascal Rhein Westfal TH Aachen Aachener Verfahrenstech Proc Syst Engn Aachen Germany

Our previous work has shown that replacing parts of the classical compartmentalization model reduction approach for distillation columns by offline-trained artificial neural networks (ANNs) improves computational performance. In real-life applications, the absence of a high-fidelity model for data generation can, however, prevent the deployment of this approach. Therefore, we propose a method that utilizes solely plant measurement data, starting from a small initial data set and then continuously adapting to newly measured data. We demonstrate the approach in closed-loop simulations and compare to benchmarks using either the high-fidelity model or an offline trained reduced model for control. Copyright (C) 2021 The Authors.

关键词： Nonlinear model reduction model predictive and optimization-based control Adaptive control Real time optimization and control Neural networks in process control

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Aperiodic Communication for MPC in Autonomous Cooperative Landing 7

Aperiodic Communication for MPC in Autonomous Cooperative La...

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7th IFAC Conference on Nonlinear model predictive control (NMPC)

作者： Lapandic, Dzenan Persson, Linnea Dimarogonas, Dimos V. Wahlberg, Bo KTH Royal Inst Technol Sch Elect Engn & Comp Sci Div Decis & Control Syst Stockholm Sweden

This paper investigates the rendezvous problem for the autonomous cooperative landing of an unmanned aerial vehicle (UAV) on an unmanned surface vehicle (USV). Such heterogeneous agents, with nonlinear dynamics, are dynamically decoupled but share a common cooperative rendezvous task. The underlying control scheme is based on distributed model predictive control (MPC). The main contribution is a rendezvous algorithm with an online update rule of the rendezvous location. The algorithm only requires the agents to exchange information when they can not guarantee to rendezvous. Hence, the exchange of information occurs aperiodically, which reduces the necessary communication between the agents. Furthermore, we prove that the algorithm guarantees recursive feasibility. The simulation results illustrate the effectiveness of the proposed algorithm applied to the problem of autonomous cooperative landing. Copyright (C) 2021 The Authors.

关键词： Autonomous cooperative landing Nonlinear predictive control model predictive and optimization-based control Distributed nonlinear control UAVs Tracking

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Nonconvex Consensus ADMM for Cooperative Lane Change Maneuvers of Connected Automated Vehicles 21st

Nonconvex Consensus ADMM for Cooperative Lane Change Maneuve...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Katriniok, Alexander Ford Res Innovat Ctr RIC Susterfeldstr 200 D-52072 Aachen Germany

Connected and automated vehicles (CAVs) offer huge potential to improve the performance of automated vehicles (AVs) without communication capabilities, especially in situations when the vehicles (or agents) need to be cooperative to accomplish their maneuver. Lane change maneuvers in dense traffic, e.g., are very challenging for non-connected AVs. To alleviate this problem, we propose a holistic distributed lane change control scheme for CAVs which relies on vehicle-to-vehicle communication. The originally centralized optimal control problem is embedded into a consensus-based Alternating Direction Method of Multipliers framework to solve it in a distributed receding horizon fashion. Although agent dynamics render the underlying optimal control problem nonconvex, we propose a problem reformulation that allows to derive convergence guarantees. In the distributed setting, every agent needs to solve a nonlinear program (NLP) locally. To obtain a real-time solution of the local NLPs, we utilize the optimization engine OpEn which implements the proximal averaged Newton method for optimal control (PANOC). Simulation results prove the efficacy and real-time capability of our approach. Copyright (C) 2020-The Authors..

关键词： Distributed control and estimation model predictive and optimization-based control Real time optimization and control Autonomous Vehicles Multi-vehicle systems

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A Strategy for the Exact Solution of Multiparametric/Explicit Quadratically Constrained NMPC Problems 21st

A Strategy for the Exact Solution of Multiparametric/Explici...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Pappas, Iosif Diangelakis, Nikolaos A. Pistikopoulos, Efstratios N. Texas A&M Univ Artie McFerrin Dept Chem Engn College Stn TX 77845 USA Texas A&M Univ Texas A&M Energy Inst College Stn TX 77845 USA

Multiparametric programming has proven to be an efficient strategy to alleviate the computational burden of solving model predictive control problems online. Recently, it has been shown that through a second-order Taylor approximation to the Basic Sensitivity Theorem, the exact solution of multiparametric/explicit quadratically constrained nonlinear model predictive control problems is enabled. As a result, the state space is nonlinearly partitioned, and the optimal control actions are expressed as nonlinear functions of the states of the system. In this work, an algorithm for the complete exploration of the parameter space and the derivation of the parametric solution of the aforementioned problem is provided. The proposed strategy is utilized to implicitly explore the parameter space by identifying the unique and optimal active sets which describe the parametric solution. The applicability of the presented methodology is demonstrated on a regulation problem of a nonisothermal continuously-stirred tank reactor near an unstable steady-state. Copyright (C) 2020 The Authors.

关键词： model predictive and optimization-based control Real time optimization and control Nonlinear process control

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A Hierarchical Architecture for the Coordination of an Ensemble of Steam Generators 21st

A Hierarchical Architecture for the Coordination of an Ensem...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Spinelli, Stefano Longoni, Elia Farina, Marcello Petzke, Felix Streif, Stefan Ballarino, Andrea Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy CNR Ist Sistemi & Tecnol Ind Intelligenti Manifatturi Rome Italy Tech Univ Chemnitz Dept Elect Engn & Informat Technol Automat Control & Syst Dynam Lab Chemnitz Germany

This work presents a hierarchical architecture for the optimal management of an ensemble of steam generators, which needs to jointly sustain a common load. The coordination of independent subsystems is provided by a multi-layer control scheme. A high-level optimizer computes the optimal shares of production to be allocated to single generators. At medium level, a robust tube-based model predictive control (MPC) is proposed to track the time-varying demand of the ensemble using a centralized, but aggregated model, whose order does not scale with the number of subsystems. At low level, decentralized controllers are in place to stabilize the internal boiler pressure. The control architecture enables the dynamic modification of the ensemble configuration and plug and play operations. Simulation results are reported to demonstrate the potentialities of the proposed approach. Copyright (C) 2020 The Authors.

关键词： model predictive and optimization-based control optimization and control of large-scale network systems Hierarchical multilevel and multi-layer control Steam generator Energy systems

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control of interacting vehicles using model-predictive control, generalized Nash equilibrium problems, and dynamic inversion 21st

Control of interacting vehicles using model-predictive contr...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Britzelmeier, Andreas Gerdts, Matthias Rottmann, Thomas Bundeswehr Univ D-85577 Munich Germany

The paper presents a control concept for interacting vehicles in a road network. The approach combines a high-level controller for the generation of collision-free trajectories and a low-level dynamic inversion controller for path tracking. The high-level controller uses model-predictive control for generalized Nash equilibrium problems, which are used to coordinate the vehicles. The control concept was implemented and validated on scale robots and experimental results are discussed. Copyright (C) 2020 The Authors.

关键词： multi-vehicle systems motion planning and decision making dynamic games model predictive and optimization-based control trajectory tracking and path following

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Deterministic Global Nonlinear model predictive control with Neural Networks Embedded 21st

Deterministic Global Nonlinear Model Predictive Control with...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Doncevic, Danimir T. Schweidtmann, Artur M. Vaupel, Yannic Schaefer, Pascal Caspari, Adrian Mitsos, Alexander Rhein Westfal TH Aachen Aachener Verfahrenstech Proc Syst Engn Aachen Germany Forschungszentrum Julich Inst Energy & Climate Res Energy Syst Engn IEK 10 Wilhelm Johnen Str D-52425 Julich Germany JARA Julich Aachen Res Alliance Ctr Simulat & Data Sci Julich Germany

Nonlinear model predictive control requires the solution of nonlinear programs with potentially multiple local solutions. Here, deterministic global optimization can guarantee to find a global optimum. However, its application is currently severely limited by computational cost and requires further developments in problem formulation, optimization solvers, and computing architectures. In this work, we propose a reduced-space formulation for the global optimization of problems with recurrent neural networks (RNN) embedded, based on our recent work on feed-forward artificial neural networks embedded. The method reduces the dimensionality of the optimization problem significantly, lowering the computational cost. We implement the NMPC problem in our open-source solver MAiNGO and solve it using parallel computing on 40 cores. We demonstrate real-time capability for the illustrative van de Vusse CSTR case study. We further propose two alternatives to reduce computational time: i) reformulate the RNN model by exposing a selected state variable to the optimizer;ii) replace the RNN with a neural multi-model. In our numerical case studies each proposal results in a reduction of computational time by an order of magnitude. Copyright (C) 2020 The Authors.

关键词： Nonlinear process control model predictive and optimization-based control Global optimization Recurrent neural networks Neural networks in process control

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Nonlinear model predictive control of the Hydraulic Fracturing Process 21st

Nonlinear Model Predictive Control of the Hydraulic Fracturi...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Lin, Kuan-Han Eason, John P. Yu, Zhou (Joyce) Biegler, Lorenz T. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA

Hydraulic fracturing has drawn significant attention over the past decade, as it can recover crude oil and natural gas from shale deposits previously considered inaccessible, which brings considerable economic benefits. However, hazardous operating conditions of extremely high pressure and environmental concerns require us to control this process carefully. Unfortunately, nonhomogeneous rock properties make this process difficult to control. Therefore, an accurate dynamic model and a well-designed controller are needed. In this work, we use the well-known Perkins-Kern-Nordgren (PKN) model with reformulation to solve the moving boundary problem. Next, the process is controlled by a standard Nonlinear model predictive controller (NMPC) and multistage NMPC. We find that the process performance deteriorates under the influence of uncertainty with standard NMPC. When we control the process with standard NMPC, the pressure violation happens in one of the parameter mismatch cases. Nonetheless, when we apply multistage NMPC and consider the uncertainty evolution with a scenario tree, no constraint violations occur for all cases for both time-invariant and time-varying uncertainties. We also discuss the computational performance of different robust horizons for multistage NMPC. Our results demonstrate that the multistage NMPC is a promising approach to handle uncertainty caused by nonhomogeneous rock properties in the hydraulic fracturing process. Copyright (C) 2020 The Authors.

关键词： model predictive and optimization-based control Hydraulic Fracturing Dynamic optimization Optimal control Robust Nonlinear model predictive control

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Why Does Symmetry Cause Deadlocks ? 21st

Why Does Symmetry Cause Deadlocks ?

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Grover, Jaskaran Liu, Changliu Sycara, Katia Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA

Collision avoidance for multirobot systems has been studied thoroughly. Recently, control barrier functions (CBFs) have been proposed to mediate between collision avoidance and goal achievement for multiple robots. However, it has been noted that reactive controllers (such as CBFs) are prone to deadlock, an equilibrium that causes the robots to stall before reaching their goals. In this paper, we formally analyze two and three robot systems and discover circumstances under which CBFs cause deadlocks using duality theory. For the two robot system, we consider mutually heterogeneous robots (such as one more vigorous or closer to its goal than the other) and prove that this heterogeneity does not help in preventing deadlock. We then consider three robots, and conclude from these two scenarios that the geometric symmetry resulting from robots' initial positions and goals constrains CBFs to generate velocities that render deadlock stable. Thus, conferring skewness to the system can help evade deadlock. Copyright (C) 2020 The Authors.

关键词： Mobile robots Multiagent systems Autonomous robotic systems Robotics technology model predictive and optimization-based control

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A Resource-Aware Approach to Self-Triggered model predictive control 21st

A Resource-Aware Approach to Self-Triggered Model Predictive...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Wildhagen, Stefan Jones, Colin N. Allgoewer, Frank Univ Stuttgart Inst Syst Theory & Automat Control Stuttgart Germany Ecole Polytech Fed Lausanne EPFL Automat Control Lab Lausanne Switzerland

In this paper, we consider a self-triggered formulation of model predictive control. In this variant, the controller decides at the current sampling instant itself when the next sample should be taken and the optimization problem be solved anew. We incorporate a pointwise-in-time resource constraint into the optimization problem, whose exact form can be chosen by the user. Thereby, the proposed scheme is made resource-aware with respect to a universal resource, which may pertain in practice for instance to communication, computation, energy or financial resources. We show that by virtue of the pointwise-in-time constraints, also a transient and an asymptotic average constraint on the resource usage are guaranteed. Furthermore, we derive conditions on the resource under which the proposed scheme achieves recursive feasibility and convergence. Finally, we demonstrate our theoretical results in a numerical example. Copyright (C) 2020 The Authors.

关键词： Event-based control model predictive and optimization-based control control under communication constraints control under computation constraints

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