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nonlinear model-predictive control with hysteresis compensation of an electro-pneumatic clutch for truck applications

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MATHEMATICAL AND COMPUTER modelLING OF DYNAMICAL SYSTEMS 2014年第2期20卷 105-129页

作者： Schindele, Dominik Aschemann, Harald Prabel, Robert Univ Rostock Chair Mechatron D-18059 Rostock Germany

This contribution presents two real-time capable nonlinear model-predictive control (NMPC) approaches for an electro-pneumatic clutch for heavy trucks: a centralized control approach and a cascaded one. A clutch is necessary at start-up or during gear shifts to connect or disconnect the combustion engine and the gear box. This automated actuator disburdens the driver and provides the necessary actuation force according to the large torque typically transmitted through the drive train. The force characteristic of the clutch, however, is subject to hysteresis, which is described by a generalized Bouc-Wen model and used for a feedforward hysteresis compensation in the control algorithm. The proposed NMPC-algorithm involves (i) a minimization of the difference between the desired and predicted state vector at the end of the prediction horizon and (ii) flatness-based techniques to compute desired trajectories for the complete state vector as well as the control input. The optimal control is given by an additional, minimum-norm control input that minimizes the difference between the predicted state vector and the desired one at the end of the prediction horizon. Thereby, the computation effort of the NMPC approaches can be kept relatively small, and a real-time evaluation becomes possible. A reduced-order observer estimates an effective pressure in the clutch that also accounts for an uncertain disturbance force. Thereby, a disturbance compensation and a high tracking accuracy is achievable for the piston position as controlled variable. The efficiency of the two proposed control structures is emphasized by experimental results from a dedicated test rig.

关键词： pneumatic systems nonlinear model-predictive control hysteresis nonlinear systems observers

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nonlinear model-predictive control Based on Quasi-ARX Radial-Basis Function-Neural-Network 8

Nonlinear Model-Predictive Control Based on Quasi-ARX Radial...

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Asia modelling Symposium 2014 8th International Conference Mathematical modelling Computer Simulation

作者： Sutrisno, Imam Abu Jami'in, Mohammad Hu, Jinglu Marhaban, Mohammad Hamiruce Mariun, Norman Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan Politekn Perkapalan Negeri Surabaya Timur Indonesia Univ Putra Malaysia Fac Engn Dept Elect & Elect Engn Serdang 43400 Selangor Malaysia Univ Putra Malaysia Fac Engn Ctr Adv Power & Energy Res Serdang 43400 Selangor Malaysia

ISBN: (纸本)9781479964871

A nonlinear model-predictive control (NMPC) is demonstrated for nonlinear systems using an improved fuzzy switching law. The proposed moving average filter fuzzy switching law (MAFFSL) is composed of a quasi-ARX radial basis function neural network (RBFNN) prediction model and a fuzzy switching law. An adaptive controller is designed based on a NMPC. a MAFFSL is constructed based on the system switching criterion function which is better than the (ON/OFF) switching law and a RBFNN is used to replace the neural network (NN) in the quasi-ARX black box model which is understood in terms of parameters and is not an absolute black box model, in comparison with NN. The proposed controller performance is verified through numerical simulations to demonstrate the effectiveness of the proposed method.

关键词： nonlinear model-predictive control moving average filter fuzzy switching law quasi-ARX radial basis function neural network

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Neighboring-extremal updates for nonlinear model-predictive control and dynamic real-time optimization

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JOURNAL OF PROCESS control 2009年第8期19卷 1277-1288页

作者： Wuerth, Lynn Hannemann, Ralf Marquardt, Wolfgang Rhein Westfal TH Aachen AVT Lehrstuhl Prozesstech D-52056 Aachen Germany

nonlinear model-predictive control (NMPC) and dynamic real-time optimization (DRTO) lead to a substantial improvement of the operation of complex nonlinear processes. Whereas the focus in NMPC is primarily on control performance by minimizing the deviation from a given set-point, the objective in DRTO is to achieve a profitable and flexible operation adapted to changing market conditions and process uncertainties by employing an economic optimization criterion. A method for solving dynamic optimization problems based on neigh boring-extremal updates suitable for applications in NMPC and DRTO is presented in this paper. If process operation is affected by small perturbations, efficient techniques for updating the nominal trajectories based on parametric sensitivities can be applied [J. Kadam, W. Marquardt, Sensitivity-based solution updates in closed-loop dynamic optimization, in: Proceedings of the DYC-OPS 7 Conference, Cambridge, USA, 20041;these updates do not require the solution of the rigorous optimization problem but rely on first and second-order sensitivities computed by composite adjoints. However for larger perturbations and strong nonlinearities, the fast updates obtained by the neighboring-extremal solutions are not sufficiently accurate, and the solution of the nonlinear optimization problem requires further iterations with updated sensitivities to give a feasible and optimal solution. The application of the method to a simulated semi-batch reactor demonstrates its capabilities. The presented method is discussed in comparison to other methods in the literature. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Dynamic real-time optimization nonlinear model-predictive control Parametric sensitivity analysis Neighboring-extremal control Hessian Composite adjoints

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The nonlinear model-predictive control of a chemical plant using multiple neural networks

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NEURAL COMPUTING & APPLICATIONS 2004年第1期13卷 2-15页

作者： Jazayeri-Rad, H Univ Petr Ind Ahvaz 63431 Iran

A combination of multiple neural networks (NNs) is selected and used to model nonlinear multi-input multi-output (MIMO) processes with time delays. An optimisation procedure for a nonlinear model-predictive control (MPC) algorithm based on this model is then developed. The proposed scheme has been applied and evaluated for two example problems, including the MPC of a multi-component distillation column.

关键词： multi-component distillation column neural networks nonlinear model-predictive control

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A Test Environment to Evaluate the Integration of Operators in nonlinear model-predictive control of Chemical Processes

A Test Environment to Evaluate the Integration of Operators ...

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1st IFAC Conference on Cyber-Physical and Human-Systems (CPHS)

作者： Lindscheid, C. Bremer, A. Hasskerl, D. Tatulea-Codrean, A. Engell, S. TU Dortmund Univ D-44227 Dortmund Germany

In industrial process control, basic controllers such as linear PI(D) control and cascade controllers are widely accepted by the plant operators because they are easy to understand due to their clear cause-and-effect relation. When the control and performance objectives cannot be met by simple controllers and manual interventions, advanced process control (APC) solutions, usually MPC controllers are employed. The design of APC solutions requires a considerable understanding of the process and more mathematical background-but also during the operational phase (e.g. error diagnosis). Then the issue of the trust of the operators into such complex control solutions and in their ability to monitor their behavior during plant operation arises. It may happen that APC solutions are not accepted or are fully or partly switched off by the operators after a while, which means a wasted effort and lost opportunities for better plant performance. By providing carefully chosen information about the behavior of the controller and well-designed operator interfaces, the trust of the operators and their ability to monitor advanced controllers can be increased. In this contribution we present an environment to investigate trust in automation experimentally and to explore new opportunities for the user-interaction with APC methods. The test environment consists of a chemical process simulator which is controlled online by a NMPC scheme, an interface editor for the fast development of interface concepts as well as a control room simulation in order to evaluate the synergetic cooperation between the operator and the algorithmic control schemes. (C) 2016, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Human-Machine Interaction Operator Interaction Chemical Process control nonlinear model-predictive control Trust in Automation

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Parallelization of modes of the Multi-Level Iteration Scheme for nonlinear model-predictive control of an Industrial Process

Parallelization of modes of the Multi-Level Iteration Scheme...

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IEEE Conference on control Applications (CCA)

作者： Lindscheid, C. Hasskerl, D. Meyer, A. Potschka, A. Bock, H. G. Engell, S. TU Dortmund Univ Dynam & Operat Grp D-44221 Dortmund Germany Heidelberg Univ Interdisciplinary Ctr Sci Comp IWR Neuenheimer Feld 205 D-69120 Heidelberg Germany

ISBN: (纸本)9781509007554

For the widespread application of nonlinear model-predictive control (NMPC) in the chemical industry, the computational effort that is required for the solution of the underlying resulting nonlinear dynamic optimization problems is a major obstacle. For complex process models and long prediction and control horizons, the computation times lead to large sampling times and feedback delays, which may cause performance deterioration and constraint violations. A promising approach to speed up the computations has been proposed in [1]. In this approach, two main ideas are put forward: On the one hand, the structure of the quadratic programming (QP) subproblems that result from using Sequential Quadratic Programming for the solution of the NLP problems is exploited, and different approximations of the exact solution are used in a parallel and hierarchical fashion (multi-level iterations). On the other hand, in each iteration of the algorithm the QP problems can be divided into a preparation phase based on the previous solution and a feedback phase based on the current state of the process (real-time iterations). In the present work we employ parallel computation to investigate the effects of computational delays and realize the parallel execution of two different approximations of the multi-level modes to reduce the computational burden. We use a benchmark semi-batch polymerization reactor model to investigate and to compare the control performances for an NMPC scheme with an economically motivated cost function using a standard implementation of an online optimizing control scheme, a scheme using real-time iterations and a scheme using multi-level iterations in a parallelized and interleaved fashion.

关键词： Optimizing control multi-level iteration nonlinear model-predictive control polymerization processes semibatch processes

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Economic Dynamic Real-Time Optimization and nonlinear model-predictive control on Infinite Horizons

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IFAC Proceedings Volumes 2009年第11期42卷 219-224页

作者： Lynn Würth James B. Rawlings Wolfgang Marquardt AVT - Process Systems Engineering RWTH Aachen University D-52056 Aachen Germany Dept. Chem. & Biol. Eng. - Univ. of Wisconsin. Madison (WI) USA

Abstract This paper investigates the formulation of nonlinear model-predictive control problems with economic objectives on an infinite horizon. The proposed formulation guarantees nominal stability for closed-loop operation. Furthermore, a novel solution method of the infinite horizon method through a transformation of the independent time variable is employed. The closed-loop optimization with infinite horizon is compared to a finite-horizon formulation. A small case study is presented for illustration purposes.

关键词： Dynamic Real-Time Optimization Economic Objective nonlinear model-predictive control Infinite Horizon Stability

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nonlinear model predictive control Framework for Cooperative Three-Agent Target Defense Game

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2023年第2期108卷 21-21页

作者： Manoharan, Amith Baliyarasimhuni, Sujit IIIT Delhi Dept Elect & Commun Engn Okhla Phase 3 New Delhi 110020 India Indian Inst Sci Educ & Res Bhopal Dept Elect Engn & Comp Sci Bhopal Bypass Rd Bhopal 462066 Madhya Pradesh India

This paper presents a cooperative target defense strategy using nonlinear model predictive control (NMPC) for a three-agent target-attacker-defender (TAD) game. The TAD game consists of an attacker and a cooperative target-defender pair. The attacker's objective is to capture the target, whereas the target-defender team acts together such that the defender can intercept the attacker and ensure target survival. We assume that the cooperative target-defender pair do not have perfect knowledge of the attacker states;hence, they are estimated using an extended Kalman filter (EKF). NMPC is used to determine the optimal control commands for the target and the defender. A capture analysis based on the Apollonius circles was performed to identify the target survival regions. The findings from the analysis and the efficacy of the NMPC-based solution were evaluated through extensive numerical simulations. The results verified the escape and capture regions obtained through the analysis and showed that the NMPC-based solution offers robustness to the different unknown attacker models and performs better than the CLOS and A-CLOS based strategies.

关键词： Target-defense Game nonlinear model-predictive control Cooperation

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Infinite-Horizon Continuous-Time NMPC via Time Transformation

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IEEE TRANSACTIONS ON AUTOMATIC control 2014年第9期59卷 2543-2548页

作者： Wuerth, Lynn Marquardt, Wolfgang Rhein Westfal TH Aachen AVT Proc Syst Engn Aachen Germany

In this note, a solution method is presented for nonlinear model-predictive control of open-loop stable systems on an infinite horizon. The proposed method first reformulates the infinite-horizon continuous-time problem by a time-coordinate transformation as a finite horizon problem and computes the solution after discretization of the control variables. This method aims to ensure stability without imposing a terminal constraint set. The adaptive discretization algorithm allows an efficient and accurate solution of the infinite-horizon problem with a moderate number of discrete decision variables. The time transformation function is adapted such that the important dynamics of the system can be captured and the control variables can be discretized appropriately. An illustrative case study is presented.

关键词： Adaptation infinite horizon nonlinear model-predictive control stability

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A two-layer architecture for economically optimal process control and operation

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JOURNAL OF PROCESS control 2011年第3期21卷 311-321页

作者： Wuerth, Lynn Hannemann, Ralf Marquardt, Wolfgang Rhein Westfal TH Aachen AVT Lehrstuhl Prozesstech D-52056 Aachen Germany

A two-layer architecture for dynamic real-time optimization (or nonlinear modelpredictive control (NMPC) with an economic objective) is presented, where the solution of the dynamic optimization problem is computed on two time-scales. On the upper layer, a rigorous optimization problem is solved with an economic objective function at a slow time-scale, which captures slow trends in process uncertainties. On the lower layer, a fast neighboring-extremal controller is tracking the trajectory in order to deal with fast disturbances acting on the process. Compared to a single-layer architecture, the two-layer architecture is able to address control systems with complex models leading to high computational load, since the rigorous optimization problem can be solved at a slower rate than the process sampling time. Furthermore, solving a new rigorous optimization problem is not necessary at each sampling time if the process has rather slow dynamics compared to the disturbance dynamics. The two-layer control strategy is illustrated with a simulated case study of an industrial polymerization process. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Dynamic real-time optimization Economic optimization nonlinear model-predictive control Multi-layer architecture Neighboring-extremal control

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