Showing papers presented at "American Control Conference in 2010"

Energy efficient building climate control using Stochastic Model Predictive Control and weather predictions

Abstract: One of the most critical challenges facing society today is climate change and thus the need to realize massive energy savings. Since buildings account for about 40% of global final energy use, energy efficient building climate control can have an important contribution. In this paper we develop and analyze a Stochastic Model Predictive Control (SMPC) strategy for building climate control that takes into account weather predictions to increase energy efficiency while respecting constraints resulting from desired occupant comfort. We investigate a bilinear model under stochastic uncertainty with probabilistic, time varying constraints. We report on the assessment of this control strategy in a large-scale simulation study where the control performance with different building variants and under different weather conditions is studied. For selected cases the SMPC approach is analyzed in detail and shown to significantly outperform current control practice.

Networked control systems under cyber attacks with applications to power networks

Abstract: Networked control systems under certain cyber attacks are analyzed. The communication network of these control systems make them vulnerable to attacks from malicious outsiders. Our work deals with two types of attacks: attacks on the network nodes and attacks on the communication between the nodes. We propose a distributed scheme to detect and isolate the attacks using observers. Furthermore, we discuss how to reduce the number of observer nodes while maintaining the coverage of the entire network. The results are applied to two classes of networked control systems: a network running the consensus protocol and a power network defined by the linearized swing equation. Sufficient conditions for the existence of the proposed attack detection scheme are provided for the first class of systems. For the second class, we provide a necessary condition for the existence of the proposed detection scheme.

Proceedings of the 2010 American Control Conference

Abstract: ing is permitted with credit to the source. Libraries are permitted to photocopy beyond the limit of U.S. copyright law for private use of patrons those articles in this volume that carry a code at the bottom of the first page, provided the per-copy fee indicated in the code is paid through: Copyright Clearance Center 222 Rosewood Drive Danvers, MA 01 923 U.S.A. For other copying, reprint or republication permission, write to: American Automatic Control Council c/o Department of ECE Northwestern University 2145 Sheridan Road Evanston, IL 60208-31 18 U.S.A. Phone: (847) 491-8175 Fax: (847) 491 -4455 These conference proceedings will be distributed through: IEEE Service Center 455 Hoes Lane P.O. Box 1331 Piscataway, NJ 08855-1 331 U.S.A. Phone: (800) 678-4333 or (732) 981-0060 Fax: (732) 981-9667 Email: customer.service @ ieee.orq IEEE Catalog Number: 00CH36334

Adaptive leader-follower formation control for autonomous mobile robots

Abstract: In this paper, adaptive formation control is addressed for a network of autonomous mobile robots in which there are only two leaders knowing the prescribed reference velocity while the others just play the role of followers. Assuming that each follower has only two neighbors to form a cascade interconnection, an adaptive formation control law is designed that allows each follower to achieve a specific triangular formation with its two neighbors without the need to know the velocity of its neighbors. With this scalable design approach, any expected geometric pattern of a group of n robots with two leaders can be realized by assigning an appropriate neighbor relationship and specifying a desired formation for each follower to reach. Both rigorous analysis and simulations are provided to demonstrate the effectiveness of the adaptive formation controller.

Distributed model predictive control for building temperature regulation

Abstract: This paper presents a predictive control structure for thermal regulation in buildings. The proposed method considers a dynamic cost function trying to exploit the intermittently operating mode of almost all types of buildings. One of the key idea is to use the knowledge about the occupation profile. For that purpose, the predictive control strategy is first presented for a single zone building then extended to a multizone building example. Two opposite control strategies commonly exists. The decentralized control structure, which does not offer good performances especially when the thermal coupling among adjacent rooms is not negligible, and on the other hand, the centralized control for which the computational demand grows exponentially with the size of the system, being very expensive for large scale buildings. Our solution is based on a distributed approach which takes the advantages of the both methods mentioned above. A distributed MPC algorithm with one information exchange per time step is proposed with good control performances and low computational requirements.

Adaptive control of piecewise linear systems: The output tracking case

Abstract: This paper studies the adaptive state feedback for state tracking control problem for piecewise linear systems, which are approximations of nonlinear controlled systems at multiple operating points. Piecewise linear reference model systems are studied and used for generating state trajectories. Adaptive schemes are developed using Lyapunov design methods, and their stability and tracking performance are analyzed and evaluated by simulation examples. Asymptotic tracking performance of such an adaptive control system with a sufficiently rich reference input is shown by simulation results, indicating that certain persistent excitation conditions can be sufficient for ensuring the desired asymptotic tracking in the presence of repetitive system switchings.

Stability analysis for class of switched nonlinear systems

Abstract: Stability analysis for a class of switched nonlinear systems is addressed in this paper. Two linear matrix inequality (LMI) based sufficient conditions for asymptotic stability are proposed for switched nonlinear systems. These conditions are analogous counterparts for switched linear systems which are shown to be easily verifiable and suitable for design problems. The results are illustrated by numerical examples.

Transfer function parameter identification by modified relay feedback

Abstract: This paper proposes a transfer function parameter identification method, applicable to SISO systems of any order. Parameter identification is posed as a (non-convex) squared output error minimization problem, numerically solved utilizing Newton-Raphson iteration with back tracking line search. Focus lies on computing the cost function gradient and Hessian with respect to the parameter vector and on finding a feasible start point. The method is demonstrated for FOTD model identification. A modified relay non-linearity is utilized in order to obtain most input signal energy at a predefined phase, without a priori system information. The identification method is evaluated on a batch of common process industry processes. Finally, conclusions and suggestions on future work are provided.

A novel model predictive control formulation for hybrid systems with application to adaptive behavioral interventions

Abstract: This paper presents a novel model predictive control (MPC) formulation for linear hybrid systems. The algorithm relies on a multiple-degree-of-freedom formulation that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on move suppression weights as traditionally used in MPC schemes. The formulation is motivated by the need to achieve robust performance in using the algorithm in emerging applications, for instance, as a decision policy for adaptive, time-varying interventions used in behavioral health. The proposed algorithm is demonstrated on a hypothetical adaptive intervention problem inspired by the Fast Track program, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results in the presence of simultaneous disturbances and significant plant-model mismatch are presented. These demonstrate that a hybrid MPC-based approach for this class of interventions can be tuned for desired performance under demanding conditions that resemble participant variability that is experienced in practice when applying an adaptive intervention to a population.

Encoder and decoder design for signal estimation

Abstract: In this paper, we study the joint design of optimal linear encoders and decoders for filtering and transmission of a signal over an additive Gaussian noise channel subject to a real-time constraint. The objective is to minimize the variance of the estimation error at the receiving end. The design problem is nonconvex, but it is shown that a global optimum can be found by solving a related two-stage problem. The first stage consists of a mixed H 2 and H 1 norm minimization problem, where the H 2 norm corresponds to the error variance in a corresponding Wiener-Kolmogorov filtering problem and the H 1 norm is induced by the channel noise. The second stage consists of a spectral factorization. The results are illustrated by a numerical example.

Virtual Reference Feedback Tuning (VRFT) of velocity controller in self-balancing industrial manual manipulators

Abstract: Self-balancing manual manipulators are devices that countervail the weight of a load that must be manually handled and moved by a human operator. An electric motor delivers the force needed to control the velocity of a spool with a metallic rope to which the load is hanged. This work concerns the tuning of the DC electric motor velocity controller. Specifically, Virtual Reference Feedback Tuning has been applied, which provides controller parameter tuning in a completely automatic way based on a single I/O batch measurement on the plant. The tuned controller shows good tracking performances and good robustness to variation in the applied load.

Steady-state distributions for human decisions in two-alternative choice tasks

Abstract: In human-in-the-loop systems, humans are often faced with making repeated choices among finite alternatives in response to observations of the evolving system performance. In order to design humans into such systems, it is important to develop a systematic description of human decision making in this context. We examine a commonly used, drift-diffusion, decision-making model that has been fit to human neural and behavioral data in sequential, two-alternative, forced-choice tasks. We show how this model and type of task together can be regarded as a Markov process, and we derive the steady-state probability distribution for choice sequences. Using the analytic expression for this distribution, we prove matching behavior for tasks that exhibit a matching point and we compute the sensitivity of steady-state choices to a model parameter that measures the decision maker's “exploratory” tendency.

Cluster synchronization algorithms

Abstract: This paper presents two approaches to achieving cluster synchronization in dynamical multi-agent systems. In contrast to the widely studied synchronization behavior, where all the coupled agents converge to the same value asymptotically, in the cluster synchronization problem studied in this paper, we require that all the interconnected agents to evolve into several clusters and each agent only to synchronize within its cluster. The first approach is to add a constant forcing to the dynamics of each agent that are determined by positive diffusive couplings; and the other is to introduce both positive and negative couplings between the agents. Some sufficient and/or necessary conditions are constructed to guarantee n-cluster synchronization behavior. Simulation results are presented to illustrate the effectiveness of the theoretical analysis.

Frequency domain model reduction method for parameter-dependent systems

Abstract: In this paper a modification of a recently proposed model simplification method for linear time invariant parameterized models is presented. It is able to obtain models with explicit parameter dependence. The method is based on convex optimization with semidefinite constraints. Computation of the reduced model requires only the frequency samples of the full one, hence it is possible to apply the methods to large-scale models. Numerical examples showing the drawbacks and the advantages of the method are also presented.

Recursive prediction error identification and scaling of non-linear systems with midpoint numerical integration

Abstract: A new recursive prediction error algorithm (RPEM) based on a non-linear ordinary differential equation (ODE) model of black-box state space form is presented. The selected model is discretised by a midpoint integration algorithm and compared to an Euler forward algorithm. When the algorithm is applied, scaling of the sampling time is used to improve performance further. This affects the state vector, the parameter vector and the Hessian. This impact is analysed and described in three Theorems. Numerical examples are provided to verify the theoretical results obtained.

Modeling-error robustness of a viral-load preconditioning strategy for HIV treatment switching

Abstract: In previous work, we have developed optimal-control based approaches that seek to minimize the risk of subsequent virological failure by “pre-conditioning” the viral load during therapy switches. In this paper, we use Monte-Carlo methods to evaluate the sensitivity of an open-loop implementation of these approaches to modeling errors. To account for hidden parameter dependencies, we use parameter distributions obtained from the convergence of Bayesian parameter estimation techniques applied to sets of clinical data obtained during serial therapy interruptions as the distribution from which the Monte-Carlo method samples.

A general method for defining and structuring buffer management problems

Abstract: In an industrial plant, availability is an important factor since increased availability often gives an increase of final production, which in many cases means an increased profit for the company. The purpose of using buffer tanks is to increase the availability either by separating production units from each other or by minimizing flow variations. However, the methods for achieving this goal is not trivial, and depend on the specific characteristics of the problem. This paper contributes to structuring the general buffer management problem for continuous chemical plants and suggests methods for solving some specific problems, presented as a case study at Perstorp AB, Sweden.

Dynamic mapping of diesel engine through system identification

Abstract: From a control design point of view, modern diesel engines are dynamic, nonlinear, MIMO systems. This paper presents a method to find low-complexity black-box dynamic models suitable for model predictive control (MPC) of NO x and soot emissions based on on-line emissions measurements. A four-input-five-output representation of the engine is considered, with fuel injection timing, fuel injection duration, exhaust gas recirculation (EGR) and variable geometry turbo (VGT) valve positions as inputs, and indicated mean effective pressure, combustion phasing, peak pressure derivative, NO x emissions, and soot emissions as outputs. Experimental data were collected on a six-cylinder heavy-duty engine at 30 operating points. The identification procedure starts by identifying local linear models at each operating point. To reduce the number of dynamic models necessary to describe the engine dynamics, Wiener models are introduced and a clustering algorithm is proposed. A resulting set of two to five dynamic models is shown to be able to predict all outputs at all operating points with good accuracy.

Closed-loop minimal sampling method for determining viral-load minima during switching

Abstract: In previous work, we have developed optimal-control based approaches that seek to minimize the risk of subsequent virological failure by “pre-conditioning” the viral load during therapy switches. These techniques result in the transient susceptibility of the total viral load, and rely on finding the minimum of a dip in viral load and switching before viral load rebound. Model uncertainty necessitates a closed-loop approach to minimum-finding. Blood measurements are costly in terms of money, inconvenience and risk. In this paper, we introduce an iterative parameter estimation approach to find the viral load minimum, and measure the degree of optimality of minimum-seeking under conditions of measurement noise. We evaluate the cost-savings of this approach in terms of number of samples saved from a constant measurement rate.

Packet-based robust MPC for Wireless Networked Control using co-design

Abstract: This paper presents a packet-based robust Model Predictive Control (MPC) approach in a co-design framework for Wireless Networked Control Systems (WNCS). Lyapunov stability is guaranteed if the optimization problem is feasible. An Inverse Gaussian model which represents the statistical distribution of the round-trip delay (RTD) in a IEEE802.11 WNCS was applied to support a simulation study. Monte-Carlo simulation results on a cart-mounted inverted pendulum are presented to confirm the performance advantages of packet-based robust MPC scheme.

Flood control of rivers with Model Predictive Control - proof of concept based on the river Demer in Belgium -

Abstract: This paper shows that Model Predictive Control (MPC) can more effectively be used for flood prevention in comparison with a three-position controller. Because MPC takes rain predictions into account, it uses the buffer capacity of the available flood basins in a more optimal way. Simulation results for historical data for the Demer, a river in Belgium with a history of large floodings, show a significant reduction of the number and the impact of floodings when MPC is used. Because the river models behave strongly nonlinear, it is necessary to use a nonlinear model predictive controller. Furthermore, as rainfall predictions tend to be less predictable over longer periods, it is necessary to extend MPC to Multiple MPC in order to cover this uncertainty.

Analysis of buffer delay in web-server control

Abstract: This paper addresses a problem related to control of web servers. Due to architectual issues, relevant measurements can only be taken over a limited part of the server system, which leaves the controller unaware of what happens in buffers proceeding the actual web-server (for instance, in the TCP/IP layers). In earlier presented work we modeled such a system and proved that the unmeasured buffering turned a measurement of an input disturbance into a state-dependent variable. In this paper we investigate how these new properties effect a traditional control design consisting of feedback in combination with a feed-forward mechanism. The stability analysis suggests that under certain circumstances, the system enters limit cycles due to the feedback mechanism introduced by the designed feed-forward controller. We verify the analysis by both simulations and experiments.

State estimation for large-scale partitioned systems: a moving horizon approach

Abstract: In this paper we propose novel state-estimation methods for large-scale discrete-time constrained linear systems that are partitioned, i.e. made by coupled subsystems with non-overlapping states. We focus on moving horizon estimation (MHE) schemes due to their capability of exploiting physical constraints on states and noise in the estimation process. We propose three different partition-based MHE (PMHE) algorithms where each subsystem solves reduced-order MHE problems to estimate its own state. Different estimators have different computational complexity, accuracy and transmission requirements among subsystems. Numerical simulations demonstrate the viability of our approach.

Selective input adaptation in parametric optimal control problems involving terminal constraints

Abstract: This paper is concerned with input adaptation in dynamic processes in order to guarantee feasible and optimal operation despite the presence of uncertainty. For optimal control problems having terminal constraints, two sets of directions can be distinguished in the input function space: the so-called sensitivity-seeking directions, along which a small input variation does not affect the terminal constraints, and the complementary constraint-seeking directions, along which a variation does affect the terminal constraints. Two selective input adaptation scenarios are thus possible, namely, adaptation along each set of input directions. This paper proves the important result that the cost variation due to the adaptation along the sensitivity-seeking directions is typically smaller than that due to the adaptation along the constraint-seeking directions.

Adaptive Output Feedback Control of Nonlinear Systems with Nonlinear Parameterization: A Dwell-time-switching Based Multiple Model Adaptive Control Approach

Abstract: A dwell-time-switching based MMAC scheme is proposed for the adaptive output feedback control problem of nonlinear systems with nonlinear parameterization. As in [1], the novel idea of combining the monitoring of the adequacy of candidate models (in terms of their estimation performances) in most MMAC schemes with the monitoring of the performance of the active candidate controller is employed and emphasis has been put on the design of candidate controllers, multiple estimators and monitoring signals so that they possess desirable properties. With the candidate controllers, multiple estimators and monitoring signals being carefully designed, a finite time switching result has been obtained, a characterization on the maximum number of switchings is provided, and sufficient conditions are derived to guarantee closed-loop stability. As an application of the dwell-time-switching based MMAC scheme, a constructive design based on back-stepping is provided for the adaptive output feedback control problem of a special class of nonlinearly parameterized systems, which can satisfy all those sufficient conditions to ensure closed-loop stability.

A 12-electrode piezoelectric tube scanner for fast atomic force microscopy

Abstract: This paper presents a 12-electrode piezoelectric tube scanner for fast atomic force microscopy (AFM). The scanner is used simultaneously as a sensor and an actuator. The built-in sensing mechanism of the scanner allows for displacement measurement and the unique arrangement of the electrodes allows the tube to be driven in an anti-symmetrical manner, resulting in a collocated system suitable for positive-position feedback (PPF). A PPF controller is designed to damp the scanner's resonance. The device is installed into an AFM to obtain open- and closed-loop images of a grating at 10Hz, 15.6Hz and 31Hz scan rates. The closed-loop images are noticeably superior to the open-loop images, showcasing the effectiveness of the proposed scanner.

Observer-based quantum state estimation by continuous weak measurement

Abstract: We propose to apply the Back and Forth Nudging (BFN) method used for geo-physical data assimilations [1] to estimate the initial state of a quantum system. Weconsider a cloud of atoms interacting with a magnetic field while a single observable isbeing continuously measured over time using homodyne detection. The BFN methodrelies on designing an observer forward and backwards in time. The state of the BFNobserver is continuously updated by the measured data and tends to converge to thesystem state. The proposed estimator seems to be globally asymptotically conver-gent when the system is observable. A detailed convergence proof and simulationsare given in the 2-level case. 1 Introduction Estimating the state of a quantum system is a fundamental problem of great interest inquantum control. Amongst a variety of applications, it is essential to verify the efficiencyof a quantum state preparation protocol [3, 6]. For this reason, it is interesting to avoidthe usual quantum state tomography scheme which involves doing the experiment manytimes and performing a strong projective measurement of a new observable at each prepa-ration. Indeed, since many realizations of the preparation protocol are necessary to obtainone state estimation, the fidelity of the preparation protocol is averaged out over all these

LQG control of an optical squeezer

Abstract: In this paper, we consider the application of linear quadratic Gaussian (LQG) control to the problem of optimizing the level of squeezing in one of the quadratures of an optical field. Squeezed states of light can be generated when two optical fields (at fundamental and second-harmonic frequencies) interact in an optical cavity in the presence of a second-order nonlinear crystal. Our system is an optical squeezer which is modelled as a nonlinear quantum system. Suitable models for the quantum and classical noises present in the system are used and laser phase noise which arises due to mechanical vibration of the mirror(s) in the beam path is modeled as (approximately) integrated white noise. An LQG controller is synthesized and the closed loop system is simulated to validate our design.