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Comparison of single-input single-output and multi-input multi-output control strategies for performing sequential single-axis random vibration control test

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JOURNAL OF VIBRATION AND control 2020年第21-22期26卷 1988-2000页

作者： D'Elia, Giacomo Mucchi, Emiliano Univ Ferrara Ferrara Italy

This study investigates the use of single-input single-output and multi-input multi-output control strategies for performing single-axis vibration control tests. In particular, the work addresses the problem of high-level cross-axis responses during those tests. To compare the two control strategies, the study presents a test campaign carried out on an automotive component by exploiting two different test facilities: a single-axis shaker and a three-degree-of-freedom shaker table. The analysis points out the limitations of the single-input single-output control strategy. The coupling between the excitation system and the test specimen causes cross-axis excitations that compromise the test validity. In some cases, the cross-axis vibration level even exceeds the acceptable threshold of 14 dB. The multi-input multi-output control strategy instead, besides the feedback control of the main axis, allows the simultaneous vibration control along the two cross axes, thus, improving the quality of the single-axis test. Moreover, the work provides a detailed study followed by practical examples on how to better exploit the evident potential of the multi-input multi-output control strategy for definitely avoiding cross-axis vibration control problems.

关键词： Single-axis vibration testing multi-input multi-output control single-input single-output control random vibration cross-axis vibrations

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multi input multi output lateral dynamics control of an electric vehicle

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2024年

作者： Asperti, Michele Dhingra, Divy Vignati, Michele Sabbioni, Edoardo Politecn Milan Dept Mech Engn Via Masa 1 I-20156 Milan Italy

Full electric vehicles with multiple and independently controlled powertrains allow for an improvement of vehicle handling capabilities both in steady state and in transient manoeuvres. This paper focuses on active lateral dynamics control of an electric vehicle equipped with 4 in-wheel motors and active rear steering. An integral terminal sliding mode controller (ITSMC) is derived starting from the linearized single track model with the addition of the rear wheel steering angle. The controller has a multi-input multi-output structure and is designed to track vehicle yaw rate and sideslip angle reference quantities through torque vectoring and active rear steering actuation. A novel approach for calculating reference sideslip angle and yaw rate using a logistic function is also presented in this paper. The ITSMC relies on real time knowledge of sideslip angle which cannot be measured in the real vehicle, thus it is estimated through the addition of an extended Kalman filter to the control loop. The performance of the controller is tested with VI-CarRealTime 14 degrees of freedom nonlinear model both for steady state and transient manoeuvres. Simulation results show a good tracking of the reference value with no chattering issues and with an improved behaviour if compared to a sliding mode controller from literature.

关键词： Active rear steering extended Kalman filter integral terminal sliding mode control lateral dynamics control multi-input multi-output control torque vectoring

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Data-Driven Model Learning and control of RCCI Engines based on Heat Release Rate 2nd

Data-Driven Model Learning and Control of RCCI Engines based...

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2nd Modeling, Estimation and control Conference (MECC)

作者： Sitaraman, Radhika Batool, Sadaf Borhan, Hoseinali Velni, Javad Mohammadpour Naber, Jeffrey D. Shahbakhti, Mahdi Cummins Tech Ctr Columbus IN 47201 USA Michigan Technol Univ Houghton MI 49931 USA Univ Georgia Athens GA 30602 USA Univ Alberta Edmonton AB Canada

Reactivity controlled compression ignition (RCCI) technology not only offers high thermal efficiency but also produces low nitrogen oxides (NOx) and soot emissions. However, it is imperative to control the combustion in RCCI engines to prevent high pressure rise rates and combustion instability. In this study, a model-based control framework is developed to optimize the RCCI operating mode. To this end, the effects of variations in the premixed ratio, start of injection timing and fuel equivalence ratio on the combustion dynamics are analyzed by examining the heat release rates. Three distinct heat release rate patterns are identified together with two transition zones. Heat release rate traces are grouped together as a function of fractions of early and late heat release rates. Based on a classification algorithm, the fractions of early and late heat release rate are identified as scheduling variables for the data-driven modeling of an RCCI engine. Linear regression is used to model the fractions of early and late heat release. These models are then used to train linear parameter varying (LPV) models using least-squares support vector machine (LS-SVM). Using the learned LPV model, a model predictive controller (MPC) scheme is then developed for a 2-liter 4-cylinder RCCI engine to control combustion phasing (CA50) and indicated mean effective pressure (IMEP) while limiting the maximum pressure rise rate (MPRR) to avoid engine knocking. The simulation results show that the designed controller is capable of limiting MPRR below 6 bar/CAD while tracking CA50 and IMEP with average errors of 1.2 CAD and 6.2 kPa, respectively. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Machine Learning Support Vector Machines (SVM) Model Predictive control (MPC) multi-input multi-output control Reactivity controlled Compression Ignition (RCCI)

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Optimally designed Lyapunov-Krasovskii terminal costs for robust stable-feasible model predictive control of uncertain time-delay nonlinear dynamical systems

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND control ENGINEERING 2021年第5期235卷 664-679页

作者： Yaqubi, S. Homaeinezhad, M. R. KN Toosi Univ Technol Fac Mech Engn 17 Pardis StMollasadra AveVanak Sq Tehran *** Iran

This article details a new Model Predictive control algorithm ensuring robust stability and control feasibility for uncertain nonlinear multi-input multi-output dynamical systems considering uncertain time-delay effects. The proposed control algorithm is based on construction of a Lyapunov-Krasovskii functional as terminal cost. Incorporation of this terminal cost into the Model Predictive control optimization problem and calculation of the associated admissible set result in robust feasibility and robust stability of closed-loop system in presence of uncertain time-delay effects and bounded disturbance signals. The Lyapunov-Krasovskii functional term is constructed with respect to predicted sliding functions over the prediction horizon and considers the effects of dynamical variations over the prediction horizon in generation of control inputs. As dynamical variations are investigated in a sample-to-sample basis, feasible sliding regions are updated at each sample as well. Finally, based on expression of sliding functions as a combination of dynamical variations and input-based terms, required control inputs are calculated in the admissible bound by the optimization algorithm. Construction of control scheme on this basis permits straightforward calculation of robust stability and feasibility conditions for a general class of uncertain nonlinear system in finite prediction horizon whereas in the previous works, often-restrictive conditions were considered for the investigated dynamical systems. Numerical illustrations indicate precision and efficiency of control algorithm and improved stability and convergence rate for multivariable nonlinear dynamical systems considering uncertain time-delay effects. Finally, hardware-in-the-loop implementation indicates applicability of the proposed scheme in real-time control applications particularly in case appropriate compromises between optimality and calculation speed are considered.

关键词： Feasible model predictive control Lyapunov-Krasovskii function multi-input multi-output control uncertain control stability of time-delayed systems

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A nonlinear model predictive controller based on the gravitational search algorithm

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OPTIMAL control APPLICATIONS & METHODS 2021年第6期42卷 1734-1761页

作者： Nobahari, Hadi Alizad, Meysam Nasrollahi, Saeed Sharif Univ Technol Dept Aerosp Engn Tehran Iran

A heuristic nonlinear model predictive controller is proposed, based on the gravitational search algorithm. The proposed method models a constrained nonlinear model predictive control problem in the form of a dynamic optimization and uses a set of virtual particles, moving within the search space, to find the best control sequence in an online manner. Particles affect the movement of each other through the gravitational forces. The optimality of the points, experienced by the particles, is evaluated by a cost function. This function reduces the tracking error, control effort, and control chattering. The better control sequence a particle finds, the more mass is assigned to that particle. Therefore, it will apply more gravitational force to the other particles and will absorb them more strongly. Stability of the new controller is investigated. Finally, performance of the controller is evaluated in two nonlinear benchmark problems as well as an experimental attitude control of a quadrotor as a nonlinear multi-input multi-output system with input constraints. Results confirm successful performance of the proposed controller.

关键词： gravitational search algorithm multi-input multi-output control nonlinear model predictive control quadrotor real-time optimization

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Data-Driven Model Learning and control of RCCI Engines based on Heat Release Rate

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IFAC-PapersOnLine 2022年第37期55卷 608-614页

作者： Radhika Sitaraman Sadaf Batool Hoseinali Borhan Javad Mohammadpour Velni Jeffrey D. Naber Mahdi Shahbakhti Cummins Technical Center Columbus IN 47201 USA Michigan Technological University Houghton MI 49931 USA University of Georgia Athens GA 30602 USA University of Alberta Edmonton AB Canada

关键词： Machine Learning Support Vector Machines (SVM) Model Predictive control (MPC) multi-input multi-output control Reactivity controlled Compression Ignition (RCCI)

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A non-linear estimation and model predictive control algorithm based on ant colony optimization

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2019年第4期41卷 1123-1138页

作者： Nobahari, Hadi Nasrollahi, Saeed Sharif Univ Technol Dept Aerosp Engn Tehran *** Iran

A new heuristic controller, called the continuous ant colony controller, is proposed for non-linear stochastic Gaussian/non-Gaussian systems. The new controller formulates the state estimation and the model predictive control problems as a single stochastic dynamic optimization problem, and utilizes a colony of virtual ants to find and track the best estimated state and the best control signal. For this purpose, an augmented state space is defined. An integrated cost function is also defined to evaluate the points of the augmented state space, explored by the ants. This function minimizes simultaneously the state estimation error, tracking error, control effort and control smoothness. Ants search the augmented state space dynamically in a similar scheme to the optimization algorithm, known as the continuous ant colony system. Performance of the new model predictive controller is evaluated for three non-linear problems. The problems are a non-linear continuous stirred tank reactor, a non-linear cart and spring system, and the attitude control of a non-linear quadrotor. The results verify successful performance of the proposed algorithm from both estimation and control points of view.

关键词： Ant colony optimization heuristic control model predictive control multi-input multi-output control non-linear stochastic system

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Predicting Flight Environments with a Small-Scale, Direct-Field Acoustic Test Facility 1

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35th IMAC Conference and Exposition on Structural Dynamics

作者： Stasiunas, Eric C. Raymer, Matthew K. Nelson, Garrett D. Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA

ISBN: (数字)9783319549873

ISBN: (纸本)9783319549873;9783319549866

In order to predict flight environments for ground support equipment, a small-scale, direct-field acoustic test (DFAT) laboratory was recently constructed at Sandia National Laboratories. This unique laboratory setup-consisting of 24 commercial off-the-shelf monitor speakers driven by a multi-input multi-output control system-was capable of exciting the component with an acoustic environment of 103 dB overall sound pressure level (OASPL). The resulting measured data was used to predict the flight environment response of the component and to derive vibration test specifications for future mechanical shaker testing. This paper describes the small-scale DFAT laboratory setup, the applied acoustic test method, and the process used to predict the flight environments for the given ground support equipment.

关键词： Direct-field acoustic test DFAT multi-input multi-output control MIMO Flight environment prediction

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Virtual Feedback Error Learning control

Virtual Feedback Error Learning Control

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SICE Annual Conference

作者： Yamato Matsubara Makoto Noguchi Atsushi Satoh Kenji Sugimoto Graduate School of Information Science Nara Institute of Science and Technology Graduate School of Engineering Iwate University

ISBN: (纸本)9781457707148

Feedback error learning (FEL) is a model for biological motion control and has attracted much attention also in control engineering. This paper proposes a new scheme named Virtual Feedback Error Learning (VFEL), which does not use actual feedback error signals, by modifying the two-degree-of-freedom structure in conventional FEL schemes. The paper first points out that for some cases the conventional schemes have certain drawbacks due to this structure and explains how they will be overcome by the proposed VFEL scheme. It is further pointed out that VFEL has its own drawback. These aspects are discussed through a specific experimental system by means of simulation, at this moment.

关键词： Feedback Error Learning (FEL) multi-input multi-output control Adaptive Feedforward control

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Applying structured singular value to multi-machine power system stabilizer design

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ELECTRIC POWER SYSTEMS RESEARCH 1997年第2期43卷 113-123页

作者： Yang, TC School of Engineering University of Sussex Brighton BN1 9QT UK

A power system stabilizer design method for multi-machine power systems is proposed in this paper. In this design method, the design problem is translated into an equivalent problem of decentralized controller design for multi-input multi-output (MIMO) control systems. Subject to a condition based on the structured singular values, each stabilizer can be designed independently. The robust stability condition for power systems with stabilizers on can be easily stated as to achieve a sufficient interaction margin defined in this paper, and a sufficient gain and phase margin defined in the classical feedback theory during each independent design. Within this general framework, the conventional stabilizer design methodology based on the concept of synchronous and damping torques is used to decide the design details of each stabilizer. The suggested design method is applied to a model of a practical 10 machine power system. (C) 1997 Elsevier Science S.A.

关键词： power system stabilization multi-machine power system multi-input multi-output control

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