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检索条件"主题词=Nonlinear Model Predictive Control"

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Comparison of optimized pulse patterns with Direct model predictive control using co-simulation

Comparison of optimized pulse patterns with Direct Model Pre...

引用

8th IET International Conference on Power Electronics, Machines and Drives (PEMD 2016)

作者： Christoph Schulte Klaus Peter Michael Leuer Joachim Böcker Universitat Paderborn Paderborn Nordrhein-Westfalen DE

ISBN: (纸本)9781510825666

Accurate simulation of electric drives has always been desirable, as it allows the machine designer a deeper insight into the whole design. This publication presents a comparison of two different control approaches. To maximize the results' accuracy, no reduced-order models of the control structure or of the motor model are being used. The comparison includes a PI-controller with optimized pulse patterns and a Direct model predictive control. As a benchmark the current response and the magnet loss are compared. The comparison of both control strategies shows that the model predictive control offers high dynamics buts leading to higher magnet loss compared with the optimized pulse patterns. The simulated magnet loss values of the optimized pulse patterns correlates well with the results of the analytical approach.

关键词： Co-simulation Finite-Element-Analysis model predictive control Predetermined pulse patterns Magnet loss pulse pattern model predictive control predictive control Finite element method distributed model predictive control nonlinear model predictive control motor model control Architecture PI control electric drives

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A predictive control Design with Speed Previewing Information for Vehicle Fuel Efficiency Improvement

A Predictive Control Design with Speed Previewing Informatio...

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American control Conference

作者： Mehmet Fatih Ozkan Yao Ma Department of Mechanical Engineering Texas Tech University Lubbock Texas

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

The growing vehicle connectivity and autonomy in the ground transportation system are not only able to improve traffic safety but also fuel efficiency. This paper proposes a computational friendly receding-horizon optimization-based nonlinear model predictive control (NMPC) algorithm to achieve fuel-saving speed planning for connected vehicles. The NMPC method solves for the fuel-optimal speed profile of connected vehicles considering a short speed preview of the preceding vehicle. By utilizing such previewing information through vehicle connectivity, the fuel consumption of the connected vehicles is reduced by avoiding unnecessary braking and acceleration, particularly in transient operating conditions. In order to analyze the effectiveness of NMPC design, dynamic programming (DP) method is adopted as a benchmark algorithm where the full speed preview of the preceding vehicle is known. The performances of NMPC and DP designs in driving behavior and fuel economy are quantitatively explored and compared under several standard driving cycles. Results show a promising improvement of the performance by adopting the proposed design and reveal the potential fuel benefits brought by vehicle connectivity and autonomy.

关键词： vehicle Autonomy Fuel efficiency design control transport system Driver Behavior Traffic safety nonlinear model predictive control Fuel economy Operating conditions predictive control

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A Parametric Multi-Convex Splitting Technique with Application to Real-Time NMPC

A Parametric Multi-Convex Splitting Technique with Applicati...

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IEEE Annual Conference on Decision and control

作者： Jean-Hubert Hours Colin N. Jones Laboratoire d'Automatique Ecole Polytechnique Federale de Lausanne (EPFL) CH-1015 Lausanne

ISBN: (纸本)9781467360890

A novel splitting scheme to solve parametric multi-convex programs is presented. It consists of a fixed number of proximal alternating minimisations and a dual update per time step, which makes it attractive in a real-time nonlinear model predictive control (NMPC) framework and for distributed computing environments. Assuming that the parametric program is semi-algebraic and that its critical points are strongly regular, a contraction estimate is derived and it is proven that the sub-optimality error remains stable under some mild assumptions. Efficacy of the method is demonstrated by solving a bilinear NMPC problem to control a DC motor. In particular, the effect of the sampling period on the optimality tracking error is analysed for a fixed computational power.

关键词： DC motors Parametric sampling interval nonlinear model predictive control convex programming splitting scheme computational power

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Use of predictive Information for Battery Pack Thermal Management

Use of Predictive Information for Battery Pack Thermal Manag...

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American control Conference

作者： Hesam Zomorodi DoHyun Yoon Beshah Ayalew Clemson University-International Center for Automotive Research

ISBN: (纸本)9781509045839

A well-designed Battery Thermal Management System (BTMS) is essential for Li-ion batteries especially in high power applications such as electric vehicles. While most of the current BTMS technologies apply simple feedback control strategies, use of available predictive or preview information about traffic or weather patterns in BTMS is a rarely explored area. This paper focuses on the use of predictive information for a prototype BTMS that utilizes Thermoelectric Coolers (TEC) and a microchannel heat sink. A control-oriented nonlinear model is first developed for the system and a nonlinear model predictive control (NMPC) scheme is formulated to make it possible to use the knowledge of the predicted future drive cycle and the battery thermal system model for an efficient battery thermal management. Considering the availability of a predicted heat generation profile for the US06 drive cycle, it is shown that a considerable energy saving is possible with just a 30 seconds of prediction horizon without sacrificing the temperature tracking/regulation performance of the BTMS.

关键词： Battery thermal management Preview control Li-ion battery packs Thermoelectric coolers and heat sinks Thermal management predictive control Driving cycle lithium ion battery Batteries Battery Pack Weather patterns predictive nonlinear model predictive control Heat generation Electric vehicles

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Trajectory Planning and Tracking of Robotic Fish Using Ergodic Exploration

Trajectory Planning and Tracking of Robotic Fish Using Ergod...

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American control Conference

作者： Maria L. Castano Anastasia Mavrommati Todd D. Murphey Xiaobo Tan Department of Electrical and Computer Engineering Michigan State University East Lansing 48824 USA Department of Mechanical Engineering Northwestern University Evanston IL 60208 USA

ISBN: (纸本)9781509045839

In recent years, underwater robots that propel and maneuver themselves like real fish, often called robotic fish, have emerged as promising mobile sensing platforms for freshwater and marine environments. For these active monitoring applications, efficient exploration along with eco-nomical locomotion is highly important, in order to optimize sensing coverage and guarantee long field operation time. As a result, optimization of the sensing trajectory and energy-saving tracking of the planned trajectory are of interest. In this paper we adopt an ergodic exploration method to calculate an optimal sensing trajectory for a tail-actuated robotic fish, and propose a nonlinear model predictive control (NMPC) approach for tracking the generated trajectory. A high-fidelity, averaged nonlinear dynamic model is used for trajectory planning and control. In particular, the bias and amplitude of the tail-beat pattern are treated as the control inputs, and their physical bounds and the constraints on their changing rates are properly accounted for in the optimization process. Finally, simulation results are presented to illustrate the effectiveness of the proposed approach.

关键词： presented illustrate effectiveness trajectory planning tracking trajectory Exploration beat pattern nonlinear model predictive control sublittoral zone Fishes control input Optimization Procedure

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Hybrid trajectory planning approach for roundabout merging scenarios

Hybrid trajectory planning approach for roundabout merging s...

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International Conference on Connected Vehicles and Expo (ICCVE)

作者： Carlos Hidalgo Ray Lattarulo Joshué Pérez Estibaliz Asua Electricity and Electronics Department University of the Basque Country Bilbao Spain Industry and Transport Division Tecnalia Research & Innovation Bilbao Spain

ISBN: (纸本)9781728101439

Despite the progress made in the Intelligent Transportation Systems (ITS) field, there is still a large amount of scenarios to be solved, such as urban roundabouts. In particular, the entrance must be emphasized due to most of accidents take place in this part. In such way, the article presents a method to solve the roundabout merging considering a nominal trajectory generated through Bézier curves combining with a model predictive control (MPC) to assure safe future states. Tests are made using a real platform to execute the maneuver and a virtual platform to represent the other participant on the scenario. Experimental results demonstrate the good performance of the approach under merging scenarios.

关键词： Bezier curves model predictive control Roundabouts Merging Intelligent transportation systems Roundabout Intelligent transportation systems merger predictive control model predictive control distributed model predictive control nonlinear model predictive control

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Explicit feasible initialization for nonlinear MPC with guaranteed stability

Explicit feasible initialization for nonlinear MPC with guar...

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IEEE Conference on Decision and control

作者： Moritz Schulze Darup Martin Monnigmann Automatic Control and Systems Theory Ruhr-Universitat Bochum Bochum Germany

ISBN: (纸本)9781612848006

We present a method for the computation of control invariant (c.i.) sets and a simple suboptimal explicit controller for a large class of constrained nonlinear discrete time systems. The explicit controller provides, for any point in the c.i. set, a finite sequence of input values that drive the system to the origin. These input sequences may serve as feasible initializations for the nonlinear program (NLP) associated to nonlinear model predictive control (NMPC). The proposed method is a straightforward extension of an existing method for the computation of c.i. sets, which we augment by a mechanism to record feasible control actions. In contrast to existing explicit NMPC approaches, the explicit control law is calculated without solving the underlying NLP. The method is computationally expensive, but most of the computational effort can be moved offline, and the evaluation of the resulting explicit controller is quite fast.

关键词： Explicit control law Input sequence finite sequence nonlinear model predictive control controllers initialization OFF LINE

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predictive control of a wave energy converter with wave prediction using differential flatness

Predictive control of a wave energy converter with wave pred...

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IEEE Annual Conference on Decision and control

作者： Guang Li School of Engineering and Material Sciences Queen Mary University of London Mile End Road London E1 4NS UK

ISBN: (纸本)9781479978878

This paper presents a fast constrained optimization approach, which is tailored for nonlinear model predictive control of wave energy converters (WEC). The advantage of this approach relies on its exploitation of the differential flatness of the WEC's dynamics. This can reduce the dimension of the resulting nonlinear programming problem (NLP) derived from the optimal control of a WEC based on the pseudospectral method. The alleviation of computational burden using this approach promotes an economic implementation of nonlinear model predictive control strategy for WEC control problems. The method is applicable to nonlinear WEC models, nonconvex objective functions and nonlinear constraints, which are commonly encountered in WEC control problems. A case study demonstrates the efficacy of this method.

关键词： nonlinear model predictive control WAVE ENERGY CONVERTERS predictive control flatness nonlinear constraint Energy converters Objective function Wave energy A wave

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Economic model predictive control of the electric arc furnace using data-driven multi-rate models

Economic model predictive control of the electric arc furnac...

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American control Conference

作者： Mudassir M. Rashid Prashant Mhaskary Christopher L.E. Swartz Dept. of Chemical Engineering McMaster University Hamilton Ontario Canada L8S 4L7

ISBN: (纸本)9781467386838

This work considers the problem of economic model predictive control (EMPC) of electric arc furnaces (EAF), subject to the limited availability of process measurements and noise. The key issues addressed are: (1) the multi-rate sampling of process variables;and (2) the requirement of optimized operation that achieves desired product specifications and also minimizes the operating costs. To this end, we identify data-driven models that capture the temporal dynamics of process measurements sampled at different rates. The resulting multi-rate models are used to design a two-tiered predictive controller that enables achieving the target end-point while minimizing the associated costs. The EMPC is implemented on the EAF process and the closed-loop simulation results illustrate the improvement in economic performance over existing trajectory-tracking approaches.

关键词： Steel Process control Economics Computational modeling Slag Temperature measurement predictive models Arc furnaces temperature measurement Computational modeling Process control Prediction models slag economics Steels Economic models predictive control data driven Optimal operation distributed model predictive control nonlinear model predictive control

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Experimental Study: Neural Network based model predictive control of a Distributed Parameter System

Experimental Study: Neural Network based Model Predictive Co...

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IEEE International Conference on control and Automation

作者： Jay N. Sompura Jaivik K. Mankad Nitin U. Padhiyar Electrical Engineering Department Indian Institute of Technology Chemical Engineering Department Indian Institute of Technology

ISBN: (纸本)9781509017393

An experimental set up has been designed for the purpose of spatial property control of a distributed parameter system. The setup consists of a thin metal chip with four temperature sensors along the length of the chip and four electric heaters to regulate the spatial temperature profile of the metal chip. A sequence of random pulse input signals with varying sampling times for the four manipulated inputs were used for live data generation of the transient temperature values at four locations along the metal chip length. Two data-driven models, namely linear state space model and neural network based model are identified and applied for online control. model predictive control was used to control the spatial temperature profile for set-point tracking and disturbance rejection.

关键词： temperature profiles Distributed parameter systems model predictive control predictive control Neural network Computer chips DNA Microarrays Temperature Sensor Device Component data generation State space models Disturbance rejection distributed model predictive control nonlinear model predictive control Metals Set of points

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