Showing papers in "Lecture Notes in Control and Information Sciences in 2010"
Journal Article•
TL;DR: An optimal two-stage identification algorithm is presented for Hammerstein–Wiener systems where two static nonlinear elements surround a linear block and is shown to be convergent in the absence of noise and convergence with probability one in the presence of white noise.
Abstract: An optimal two-stage identification algorithm is presented for Hammerstein–Wiener systems where two static nonlinear elements surround a linear block. The proposed algorithm consists of two steps: The first one is the recursive least squares and the second one is the singular value decomposition of two matrices whose dimensions are fixed and do not increase as the number of the data point increases. Moreover, the algorithm is shown to be convergent in the absence of noise and convergent with probability one in the presence of white noise.
398 citations
Journal Article•
TL;DR: In this paper, a deterministic approach based on separable least squares (SLS) is proposed for the identification of systems with input nonlinearities of known structure, where the identification problem is shown to be equivalent to a one dimensional minimization problem.
Abstract: This paper studies identification of systems with input nonlinearities of known structure. For input nonlinearities parameterized by one parameter, a deterministic approach is proposed based on the idea of separable least squares. The identification problem is shown to be equivalent to a one-dimensional minimization problem. The method is very effective for several common static and non-static input nonlinearities. For a general input nonlinearity, a correlation analysis based identification algorithm is presented which is shown to be convergent.
224 citations
Journal Article•
TL;DR: In this paper, a blind approach to the sampled Hammerstein-Wiener model identification is proposed, where no a priori structural knowledge about the input nonlinearity is assumed and no white noise assumption is imposed on the input.
Abstract: In this paper, we propose a blind approach to the sampled Hammerstein-Wiener model identification. By using the blind approach, it is shown that all internal variables can be recovered solely based on the output measurements. Then, identification of linear and nonlinear parts can be carried out. No a priori structural knowledge about the input nonlinearity is assumed and no white noise assumption is imposed on the input.
190 citations
TL;DR: In this article, the authors provide a self-contained introduction to some basic results, with a focus on contractions with respect to non-Euclidean metrics, and show that the incremental stability property that all trajectories converge to a unique solution is especially interesting when forcing functions are periodic and in the context of establishing synchronization results.
Abstract: Contraction theory provides an elegant way of analyzing the behaviors of systems subject to external inputs. Under sometimes easy to check hypotheses, systems can be shown to have the incremental stability property that all trajectories converge to a unique solution. This property is especially interesting when forcing functions are periodic (a globally attracting limit cycle results), as well as in the context of establishing synchronization results. The present paper provides a self-contained introduction to some basic results, with a focus on contractions with respect to non-Euclidean metrics.
145 citations
Journal Article•
TL;DR: This chapter reviews recent progress in the use of state-dependent event-triggering in embedded control, networked control systems, distributed estimation, and distributed optimization.
Abstract: Networked control systems often send information across the communication network in a periodic manner. The selected period, however, must assure adequate system performance over a wide range of operating conditions and this conservative choice may result in significant over-provisioning of the communication network. This observation has motivated the use of sporadic transmission across the network's feedback channels. Event-triggering represents one way of generating such sporadic transmissions. In event-triggered feedback, a sensor transmits when some internal measure of the novelty in the sensor information exceeds a specified threshold. In particular, this means that when the gap between the current and the more recently transmitted sensor measurements exceeds a state-dependent threshold, then the information is transmitted across the channel. The state-dependent thresholds are chosen in a way that preserves commonly used stability concepts such as input-to-state stability or L 2 stability. This approach for threshold selection therefore provides a systematic way of triggering transmissions that provides some guarantees on overall control system performance. While early work in event-triggering focused on control applications, this technique can also be used in distributed estimation and distributed optimization. This chapter reviews recent progress in the use of state-dependent event-triggering in embedded control, networked control systems, distributed estimation, and distributed optimization.
135 citations
Journal Article•
TL;DR: In this paper, the authors present the main results available in the literature about the analysis and design of linear consensus algorithms, for both synchronous and asynchronous implementations, and show that many control, optimization and estimation problems such as least squares, sensor calibration, vehicle coordination and Kalman filtering can be cast as the computation of some sort of averages, therefore being suitable for consensus algorithms.
Abstract: In this chapter we present a popular class of distributed algorithms, known as linear consensus algorithms, which have the ability to compute the global average of local quantities. These algorithms are particularly suitable in the context of multi-agent systems and networked control systems, i.e. control systems that are physically distributed and cooperate by exchanging information through a communication network. We present the main results available in the literature about the analysis and design of linear consensus algorithms,for both synchronous and asynchronous implementations. We then show that many control, optimization and estimation problems such as least squares, sensor calibration, vehicle coordination and Kalman filtering can be cast as the computation of some sort of averages, therefore being suitable for consensus algorithms. We finally conclude by presenting very recent studies about the performance of many of these control and estimation problems, which give rise to novel metrics for the consensus algorithms. These indexes of performance are rather different from more traditional metrics like the rate of convergence and have fundamental consequences on the design of consensus algorithms.
92 citations
TL;DR: This chapter provides a tutorial overview of distributed optimization and game theory for decision-making in networked systems, and discusses properties of first-order methods for smooth and non-smooth convex optimization, and reviews mathematical decomposition techniques.
Abstract: This chapter provides a tutorial overview of distributed optimization and game theory for decision-making in networked systems We discuss properties of first-order methods for smooth and non-smooth convex optimization, and review mathematical decomposition techniques A model of networked decision-making is introduced in which a communication structure is enforced that determines which nodes are allowed to coordinate with each other, and several recent techniques for solving such problems are reviewed We then continue to study the impact of noncooperative games, in which no communication and coordination are enforced Special attention is given to existence and uniqueness of Nash equilibria, as well as the efficiency loss in not coordinating nodes Finally, we discuss methods for studying the dynamics of distributed optimization algorithms in continuous time
81 citations
TL;DR: The aim of this chapter is to survey the main research lines in a comprehensive manner for stability analysis and controller design for so-called networked control systems.
Abstract: The presence of a communication network in a control loop induces many imperfections such as varying transmission delays, varying sampling/transmission intervals, packet loss, communication constraints and quantization effects, which can degrade the control performance significantly and even lead to instability. Various techniques have been proposed in the literature for stability analysis and controller design for these so-called networked control systems. The aim of this chapter is to survey the main research lines in a comprehensive manner.
77 citations
TL;DR: Control systems are present in every industry, they are used to control chemical reactors, distillation columns, and nuclear power plants, making the authors' life more comfortable and more efficient...until the system fails.
Abstract: Nowadays, control systems are involved in nearly all aspects of our lives. They are all around us, but their presence is not always really apparent. They are in our kitchens, in our DVD-players, computers and our cars. They are found in elevators, ships, aircraft and spacecraft. Control systems are present in every industry, they are used to control chemical reactors, distillation columns, and nuclear power plants. They are constantly and inexhaustibly working, making our life more comfortable and more efficient...until the system fails.
54 citations
TL;DR: In this paper, the problem of classical electrical network synthesis (flourished between 1920-1970) is subjected to scientific critique and the first attacks on the problem were frustrated and eventually defeated by a naive over-reliance on engineering/physical intuition and shoving the mathematical issues whenever possible under the rug; now, by concentrating on essential mathematics, much of it known since the 19th century, research will be revived with spectacular prospects of scientific progress.
Abstract: The problem of classical electrical network synthesis (flourished between 1920-1970) is subjected to scientific critique. Conclusions: the first attacks on the problem were frustrated and eventually defeated by a naive over-reliance on engineering/physical intuition and shoving the mathematical issues whenever possible under the rug; now, by concentrating on essential mathematics, much of it known since the 19th century, research will be revived with spectacular prospects of scientific progress.
48 citations
TL;DR: Fault Detection and Diagnosis (FDD) as mentioned in this paper is a development of the term Fault Detection and Isolation (FDI), which includes the possibility of estimating the effect of the fault and/or diagnosing the effect or severity of a fault.
Abstract: The term Fault Detection and Diagnosis (FDD) is a development of the term Fault Detection and Isolation (FDI). Generally speaking, FDD goes slightly further than FDI by including the possibility of estimating the effect of the fault and/or diagnosing the effect or severity of the fault. Hence, the term FDD also covers the capability of isolating or locating a fault. Both of these topics have received considerable attention worldwide and have been theoretically and experimentally investigated with different types of approaches, as can be seen from the general survey works [1, 2, 3, 4, 5, 6, 7].
TL;DR: In this article, the authors describe the application of Pontryagin's Maximum Principle and dynamic programming for vehicle driving with minimum fuel consumption, where the focus is on minimum fuel accelerations.
Abstract: This chapter describes the application of Pontryagin’s Maximum Principle and Dynamic Programming for vehicle drivingwith minimum fuel consumption. The focus is on minimum-fuel accelerations. For the fuel consumption modeling, a six-parameter polynomial approximation is proposed. With the Maximum Principle, this consumption model yields optimal accelerations with a linearly decreasing acceleration as a function of the velocity. This linear acceleration behavior is also observed in real traffic situations by other researchers. Dynamic Programming is implemented with a backward recursion on a specially chosen distance grid. This grid enables the calculation of realistic gear shifting behaviour during vehicle accelerations. Gear shifting dynamics are taken into account.
TL;DR: In this paper, the authors expose several recent challenging control problems for mono-dimensional fluids or reactive fluids, which have in common the existence of a moving interface separating two spatial zones where the dynamics are rather different.
Abstract: The purpose of this paper is to expose several recent challenging control problems for mono-dimensional fluids or reactive fluids. These problems have in common the existence of a moving interface separating two spatial zones where the dynamics are rather different. All these problems are grounded on topics of engineering interest. The aim of the author is to expose the main control issues, possible solutions and to spur an interest for other future contributors. As will appear, mobile interfaces play key roles in various problems, and truly capture main phenomena at stake in the dynamics of the considered systems.
TL;DR: This introductory chapter analyzes the rationale, the chances and the challenges of model predictive control and gives a flavor of the challenges and chances offered by this approach.
Abstract: Recent years have witnessed an increased interest in model predictive control (MPC) for fast applications. At the same time, requirements on engines and vehicles in terms of emissions, consumption and safety have experienced a similar increase. MPC seems a suitable method to exploit the potentials of modern concepts and to fulfill the automotive requirements since most of them can be stated in the form of a constrained multi input multi output optimal control problem and MPC provides an approximate solution of this class of problems. In this introductory chapter, we analyze the rationale, the chances and the challenges of this approach. This chapter does not intend to review all the literature, but to give a flavor of the challenges and chances offered by this approach.
Journal Article•
TL;DR: In this paper, an on-line sliding mode control allocation scheme for fault tolerant control is proposed, where the effectiveness level of the actuators is used to redistribute the control signals to the remaining actuators when a fault or failure occurs.
Abstract: This paper proposes an on-line sliding mode control allocation scheme for fault tolerant control. The effectiveness level of the actuators is used by the control allocation scheme to redistribute the control signals to the remaining actuators when a fault or failure occurs. The paper provides an analysis of the sliding mode control allocation scheme and determines the nonlinear gain required to maintain sliding. The on-line sliding mode control allocation scheme shows that faults and even certain total actuator failures can be handled directly without reconfiguring the controller. The simulation results show good performance when tested on different fault and failure scenarios.
TL;DR: In this paper, the authors discuss the physical modeling of engines and the subjects touch three topics in nonlinear engine models and parameter identification: gas and energy flows in engines and turbocharged engines.
Abstract: The common theme in this chapter is physical modeling of engines and the subjects touch three topics in nonlinear engine models and parameter identification. First, a modeling methodology is described. It focuses on the gas and energy flows in engines and covers turbocharged engines. Examples are given where the methodology has been successfully applied, covering naturally aspirated engines and both single and dual stage turbocharged engines. Second, the modeling with the emphasis on models for EGR/VGT equipped diesel engine. The aim is to describe models that capture the essential dynamics and nonlinear behaviors and that are relatively small so that they can be utilized in model predictive control algorithms. Special emphasis is on the selection of the states. The third and last topic is related to parameter identification in gray-box models. A common issue is that parameters with physical interpretation often receive values that lie outside their admissible range during the identification. Regularization is discussed as a solution and methods for choosing the regularization parameter are described and highlighted.
TL;DR: In this article, three key points have to be taken into account for pollutant emissions predictive physical modeling in diesel engines, including the following three points, i.i.d.
Abstract: For pollutant emissions predictive physical modeling in diesel engines, three key points have to be taken into account:
TL;DR: In this article, a short review on the popular and still very important area of controlling underactuated mechanical systems is presented, where new solutions to the simultaneous stabilization and tracking problem are proposed for nonholonomic mobile robots using state and output feedback.
Abstract: This chapter provides a short review on the popular yet still very important area of controlling underactuated mechanical systems. New solutions to the simultaneous stabilization and tracking problem are proposed for nonholonomic mobile robots using state and output feedback. Some open problems are discussed with a unique objective to solicit fundamentally novel techniques for the further development of modern nonlinear control theory.
TL;DR: Fault tolerant flight control (FTFC), or intelligent self-adaptive control, enables improved survivability and recovery from adverse flight conditions induced by faults, damage and associated upsets as mentioned in this paper.
Abstract: Fault tolerant flight control (FTFC), or intelligent self-adaptive control, enables improved survivability and recovery from adverse flight conditions induced by faults, damage and associated upsets. This can be achieved by ’intelligent’ utilisation of the control authority of the remaining control effectors in all axes consisting of the control surfaces and engines or a combination of both. In this technique, control strategies are applied to restore vehicle stability, manoeuvrability and conventional piloting techniques for continued safe operation and a survivable landing of the aircraft.
TL;DR: The block-oriented models as discussed by the authors have gained wide recognition and attention by the system identification and automatic control community by joining linear dynamic system blocks with static nonlinear mappings in various forms of interconnection.
Abstract: Within the class of nonlinear system models, the so-called block-oriented models have gained wide recognition and attention by the system identification and automatic control community. Typically, these models are constructed by joining linear dynamic system blocks with static nonlinear mappings in various forms of interconnection.
TL;DR: In this article, one dimensional boundary value problems with lumped controls are considered and such systems can be modeled as modules over a ring of Beurling ultradistributions with compact support.
Abstract: One dimensional boundary value problems with lumped controls are considered. Such systems can be modeled as modules over a ring of Beurling ultradistributions with compact support. This ring appears naturally from a corresponding Cauchy problem. The heat equation with different boundary conditions serves for illustration.
TL;DR: In this paper, the MPC feedback law is synthesized as a piecewise affine function, suitable for implementation in automotive microcontrollers, and a switched MPC controller is presented.
Abstract: Model Predictive Control (MPC) can enable powertrain systems to satisfy more stringent vehicle requirements. To illustrate this, we consider an application of MPC to idle speed regulation in spark ignition engines. Improved idle speed regulation can translate into improved fuel economy, while improper control can lead to engine stalls. From a control point of view, idle speed regulation is challenging, since the plant is subject to time delay and constraints. In this chapter, we first obtain a control-oriented model where ancillary states are added to account for delay and performance specifications. Then the MPC optimization problem is defined. The MPC feedback law is synthesized as a piecewise affine function, suitable for implementation in automotive microcontrollers. The obtained design has been extensively tested in a vehicle under different operating conditions. Finally, we show how competing requirements can be met by a switched MPC controller.
TL;DR: The requirements imposed on control design from a variety of sources are explored: the physics of the engine, the embedded software limitations, the existing software hierarchy, and standard industrial control development processes.
Abstract: The efficient development of high performance control is becoming more important and more challenging with ever tightening emissions legislation and increasingly complex engines. Many traditional industrial control design techniques have difficulty in addressing multivariable interactions among subsystems and are becoming a bottleneck in terms of development time. In this article we explore the requirements imposed on control design from a variety of sources: the physics of the engine, the embedded software limitations, the existing software hierarchy, and standard industrial control development processes. Decisions regarding the introduction of any new control paradigm must consider balancing this diverse set of requirements. In this context we then provide an overview of our work in developing a systematic approach to the design of optimal multivariable control for air handling in turbocharged engines.
TL;DR: In this article, a systematic approach for the design and tuning of an adaptive cruise control (ACC) system based on model predictive control (MPC) is presented, which makes it easy and intuitive to tune, even for nonexperts in MPC control.
Abstract: The combination of different desirable characteristics and situation-dependent behavior cause the design of adaptive cruise control (ACC) systems to be time consuming and tedious. This chapter presents a systematic approach for the design and tuning of an ACC, based on model predictive control (MPC). A unique feature of the synthesized ACC is its parameterization in terms of the key characteristics safety, comfort and fuel economy. This makes it easy and intuitive to tune, even for nonexperts in (MPC) control, such as the driver. The effectiveness of the design approach is demonstrated using simulations for some relevant traffic scenarios.
TL;DR: In this article, the authors present a black-box data-based mean value model which estimates engine raw emissions from quantities available in the engine control unit, and a gray-box model is shown in which also physical equations are used to describe emission formation over crank angle with the measured cylinder pressure as main input.
Abstract: The classical trade off between nitrogen oxides (NO x) and particulate matters (PM) is still one of the key topics for Diesel engine developers. This article gives an overview about models for these emissions usable for online engine and exhaust after treatment control, offline optimization and virtual sensors for monitoring. Two different ways for obtaining such models are presented in detail: first we present a black-box data-based mean value model which estimates engine raw emissions from quantities available in the engine control unit. Second a gray-box model is shown in which also physical equations are used to describe emission formation over crank angle with the measured cylinder pressure as main input.
Journal Article•
TL;DR: Decentralized and distributed model predictive control (DMPC) as discussed by the authors addresses the problem of controlling a multivariable dynamical process, composed by several interacting subsystems and subject to constraints, in a computation and communication efficient way.
Abstract: Decentralized and distributed model predictive control (DMPC) addresses the problem of controlling a multivariable dynamical process, composed by several interacting subsystems and subject to constraints, in a computation and communication efficient way. Compared to a centralized MPC setup, where a global optimal control problem must be solved on-line with respect to all actuator commands given the entire set of states, in DMPC the control problem is divided into a set of local MPCs of smaller size, that cooperate by communicating each other a certain information set, such as local state measurements, local decisions, optimal local predictions. Each controller is based on a partial (local) model of the overall dynamics, possibly neglecting existing dynamical interactions. The global performance objective is suitably mapped into a local objective for each of the local MPC problems. This chapter surveys some of the main contributions to DMPC, with an emphasis on a method developed by the authors, by illustrating the ideas on motivating examples. Some novel ideas to address the problem of hierarchical MPC design are also included in the chapter.
TL;DR: A review of the main approaches, results and open problems in nonlinear model predictive control can be found in this paper, where the style of the presentation is maintained at a high level, reducing to the minimum the mathematical details.
Abstract: This chapter reviews some of the main approaches, results and open problems in Nonlinear Model Predictive Control. The style of the presentation is maintained at a high level, reducing to the minimum the mathematical details.
TL;DR: It is shown that algorithms originally designed for vision-based control of manipulators can be easily converted into control algorithms that provide virtual fixtures, allowing for advanced human-machine cooperative manipulation systems that take complete advantage of information provided by vision, yet permit the user to retain control of essential aspects of a given task.
Abstract: This chapter discusses a class of control algorithms that provide enhanced physical dexterity by imposing passive motion constraints. Such motion constraints are often referred to as virtual fixtures. It is shown that algorithms originally designed for vision-based control of manipulators can be easily converted into control algorithms that provide virtual fixtures. As a result it is possible to create advanced human-machine cooperative manipulation systems that take complete advantage of information provided by vision, yet permit the user to retain control of essential aspects of a given task.
TL;DR: In this paper, two approaches to flatness necessary and sufficient conditions are surveyed and compared on examples, and the results of these approaches are compared with examples of flatness in the real world.
Abstract: We survey two approaches to flatness necessary and sufficient conditions and compare them on examples.
TL;DR: Despite their structural simplicity, Wiener and Hammerstein nonlinear model structures have been effective in many application areas, where linear modelling has failed as mentioned in this paper, e.g., the chemical process industry, microwave and radio frequency (RF) technology, seismology, biology and physiology and psychophysics.
Abstract: Despite their structural simplicity, Wiener and Hammerstein nonlinear model structures have been effective in many application areas, where linear modelling has failed, e.g., the chemical process industry [5, 13], microwave and radio frequency (RF) technology [4, 7, 19], seismology [21], biology [8], physiology and psychophysics [14]. They can also be used in model predictive control [28, 29].