Conference registrants can sign up for workshops directly through the registration site.  For additional information about Workshops, please contact Workshops Chair, Eric Frew ([email protected]).

Model Predictive Control Workshop
Organizer: James B. Rawlings
Model predictive control (MPC) has become the most popular advanced control method in use today. Its main attractive features are (i) optimization of a model forecast over the available actuators (ii) estimation of the state of the system and disturbances from the process measurements, (iii) accounting for the process and actuator constraints, and (iv) accounting for full multivariable interactions. After its introduction in the process industries in the 1970s, MPC has today become a pervasive control technology in many industries, and is now being increasingly deployed for optimization of high-level functions such as minimizing energy consumption and maximizing product quality.

This workshop is intended to introduce graduate students and practitioners to the theory and design of MPC systems.
The two days of lectures will cover the following topics:

1. Model predictive control: regulation problem, dynamic programming, lin- ear quadratic regulator, constraints, infinite horizon, LQR, constrained regulation.
2. State estimation: least-squares estimator, Kalman filter, observability and convergence.
3. Putting regulation and estimation together, industrial practice, distur- bance models, and offset.
4. Nonlinear MPC. introduction, stability, Lyapunov function theory, distur- bances and robust stability, nominal stability, suboptimal MPC, inherent robustness of optimal and suboptimal MPC, some examples.
5. Nonlinear moving horizon state estimation. full state estimation, moving horizon estimation with zero prior weighting, nonzero prior weighting, constrained estimation.
5. Economic MPC. problem formulation and properties, periodic constraints. open research issues.
6. Other topics: Suboptimal MPC, MPC with discrete actuators.

[Click here for more information].
 

Smart Grid Control
Organizers: Jakob Stoustrup, Anuradha Annaswamy, Zhihua Qu, Amro Farid, Umesh Vaidya
Motivated by the growing energy needs amidst compelling sustainability and environmental concerns, a new architecture for energy management, labeled Smart Grid, is emerging where increasingly energy generation, transmission and distribution are expected to be controlled by cyber-enabled and cyber- secure components. Concepts of systems and control occupy a central role in this area in a wide range of topics ranging from pricing and markets to issues of estimation and integration at the transmission level to issues at the distribution, consumers, and demand management. This workshop focuses on three major topics in this area, and aims to provide a tutorial exposition of these topics in the morning, followed by outlines of the main challenges and problems that lie ahead in the same three topics in the afternoon. Given the strong participation and interest from academics, government, and industry, the workshop will also provide perspectives from these three sectors.

[Click here for more information].

Loop Shaping in the 21st Century
Organizers: William Messner, Takenori Atsumi
Loop shaping remains a popular method for controller design for SISO systems because it is powerful and intuitive, and several tools developed over the last fifteen years or so make it even more useful. These tools include new compensator structures, new techniques for phase stabilization, and new frequency response visualization methods. Among other advantages, these new compensator structures more readily provide the capability of designing phase stabilized systems. The visualization tools provide the capability for designing low order controllers for robust performance from frequency response data alone.

Using a series of 45 minutes Òlearn by doingÓ modules, this workshop will teach the features of these tools and how to use them effectively. The first 15-20 minutes of each module will be instruction, and the remainder will be the participants using their laptops to perform exercises using Matlabª. We will distribute Matlabª code for the various compensator structures and visualization techniques for participants to use at the workshop that they may take with them, and we will provide copies of appropriate references. The head-positioning control system of hard disk drives will be the primary motivating example problem.

This workshop will be particularly valuable to the large portion of the industrial controls community that uses loop shaping. Educators will find this valuable because the material on compensator structures is suitable for advanced undergraduates. The outline of the workshop is below.

Advanced Compensator Structures (Messner).
The first four modules of the workshop will present advanced compensator structures that employ complex poles and complex zeros. These structures provide greater flexibility than compensators using only real poles and zeros. With respect to loop shaping, these structures allow decoupling of the phase contribution of a compensator from the shape of the magnitude at a particular frequency. They are also quite useful for modeling

1. Lead and lag compensators with complex poles and zero
2. Phase adjustable resonance and notch filters
3. Complex proportional-integral-lead (CPIL) compensators
4. Complex poles and zeros for specific angle contributions at points in the complex plane

Advanced Techniques (Messner and Atsumi).
The second four modules will present advanced techniques. The first two modules will cover two aspects of phase stabilization. The second two modules will cover visualization tools that are enhance Bode plots that depict closed-loop specifications on the Bode magnitude chart and Bode phase chart of the open-loop frequency response. The so-called Robust Bode plots enable the design low order controllers for robust performance from frequency response data alone. Neither the plant model, nor for the performance weighting function, nor for the uncertainty weighting function requires a realizable transfer function.

Phase stabilization.

5. Resonance stabilization
6. Design for narrow band high gain

Controller Design with the Robust Bode (RBode) Plot

7. Unstructured uncertainty
8. Structured uncertainty

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Modeling, Estimation and Control Across Scales in Neuroscience
Organizers: Sridevi Sarma, ShiNung Ching, Jason Ritt
In this workshop, an array of speakers will present the state of current research in neurocontrol, and outline the key challenges and future directions for applications in research and clinical settings. An organizing principle for the workshop is the question of scale; a central goal is to bridge the gaps in methods typically employed from single neurons to macroscopic brain regions. The workshop will introduce models of single neurons and neural ensembles commonly used in computational neuroscience. We will cover biophysical-based models of neurons, mean field models of populations of neurons, and hybrid models of neural networks. We will discuss algorithms to estimate parameters of these models and describe their use in various applications, including characterization of neurons in diseased networks (e.g. ParkinsonÕs disease, epilepsy), and the effect of deep brain stimulation control. We will conclude with a moderated discussion on outstanding control problems in neuroscience.

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Identification of Linear, Parameter Varying, and Nonlinear Systems: Theory, Computation, and Applications
Organizers: Wallace E. Larimore (Adaptics, Inc), Pepijn B. Cox (Eindhoven University of Technology), Roland Toth (Eindhoven University of Technology)

Speakers: Wallace E. Larimore (Adaptics, Inc.), Pepijn B. Cox (Eindhoven University of Technology).

In this workshop, first powerful subspace identification methods (SIM) are described for the well understood case of data-driven modeling of linear time-invariant (LTI) systems. Recent extensions are then developed to linear parameter-varying (LPV), quasi-LPV, and general nonlinear (NL) systems with polynomial nonlinearities. The presentation, following the extended tutorial paper (Larimore, ACC2013), includes detailed conceptual development of the theory and computational methods with references to the research literature for those interested. Numerous applications are discussed including aircraft wing flutter (LPV), chemical process control (LTI), automotive engine modeling (quasi-LPV, NL), and the Lorenz attractor (NL). An emphasis is placed on conceptual understanding of the subspace identification method to allow effective application to system modeling, control, and fault diagnosis.

Over the past decade, major advances have been made in LTI system identification with data gathered in the open loop (Larimore, ACC1999) and closed-loop settings (Larimore, DYCOPS2004; Chiuso, TAC2010). However, efficiently identifying state-space models of LPV and NL systems remains an open question, as the required computational complexity for subspace methods grows exponentially with the number of system inputs, outputs, and states while prediction error methods correspond to nonlinear parameter optimization problems prone to local minima and also often leading to infeasible computational requirements.

The workshop presents a statistical approach using the fundamental canonical variate analysis (CVA) method for subspace identification of LTI systems, with detailed extensions to LPV and NL systems. The LTI case includes basic concepts of reduced rank modeling of ill-conditioned data to obtain the most appropriate statistical model structure and order using optimal maximum likelihood methods. The fundamental statistical approach gives expressions of the multistep-ahead likelihood function for subspace identification of LTI systems. This leads to direct estimation of parameters using singular value decomposition type methods that avoid iterative nonlinear parameter optimization. This results in statistically optimal maximum likelihood parameter estimates and likelihood ratio tests of hypotheses. The parameter estimates have optimal Cramer-Rao lower bound accuracy, and the likelihood ratio hypothesis tests on model structure, model change, and process faults produce optimal decisions. The LTI CVA method is compared to different system identification methods - including other subspace, prediction error, and maximum likelihood approaches - and show considerably less computational time and higher parameter accuracy.

The LTI subspace methods are extended to LPV systems, which are represented in the state-space by matrices that are no longer constant, but are functions of the system operating point or other external “exogenous” variables called the scheduling signals. Often the underlying dependence of these matrices are captured in terms of linear plus constant functions of the scheduling. For example, this allows the identification of constant underlying structural stiffness parameters while wing flutter dynamics vary with scheduling functions of speed and altitude. This is further extended to quasi-LPV systems where the scheduling functions may be functions of dynamic variables, i.e. of the inputs and/or outputs of the system directly (Larimore, Cox and Tóth, ACC2015). This includes the NL case of bilinear and general polynomial systems that are universal approximators. The developed subspace identification method for parameter-varying systems avoids the exponential growth in computational characteristics exhibited by other SIMs.

The workshop is continued with introducing alternative LPV subspace identification approaches, including a novel basis reduced realization scheme on identified FIR models (Cox, Tóth and Petreczky, LPVS2015), predictor based subspace approaches, and other alternatives. Maximum likelihood refinement of the subspace estimates is also presented in terms of an expectation-maximization method and the gradient-based optimization of the prediction error. Computational efficiency and estimation performance of the presented approaches is analyzed and compared.

[Additional information on the workshop can be found here].
 

Model Predictive Control Under Uncertainty: Theory, Computations and Applications
Organizers: Sasa V. Rakovic, William S. Levine, Behcet Acikmese, Ilya V. Kolmanovsky
Model Predictive Control (MPC) has established itself as a dominant advanced control technology across many industries due to its exceptional ability to explicitly account for control objectives, directly handle static and dynamic constraints and systematically optimize performance. MPC provides, via an iterative open loop optimal control implemented by repetitive online optimization, a feedback control that meets design specifications and maximally utilizes system capabilities. Not surprisingly, MPC has also become a highly vibrant and interdisciplinary branch of mathematical control theory that fuses and synergistically treats controlÐtheoretic issues (such as stability, performance, robustness, etc.) for constrained systems with the optimization theory and numerical computations. Important areas in MPC that have recently seen significant theoretical and implementational progress include robust and stochastic MPC as well as efficient computations for MPC via convex and reliable real-time optimization.

Overview
The workshop introduces its audience to the theory, design and applications of model predictive control under uncertainty. The workshop provides conceptual and practical principles governing rigorous and computationally effective methods for design of MPC under set-membership and probabilistic uncertainty. The theoretical fundamentals are carefully introduced and studied within the frameworks of robust and stochastic MPC. The technical foundations are complemented with a study of related design and practical aspects as well as with an overview of effective computations based on convex and reliable real-time optimization. Thus, the workshop provides a concise and unified exposure to MPC under uncertainty.

Themes
Conventional MPC (William S. Levine)
This part introduces MPC, provides its formulation, discusses its algorithmic implementation and summarizes its fundamental system theoretic properties. It also discusses effects of uncertainty in MPC in terms of types and models as well as interplay of the uncertainty with predictions, constraints and cost. This part also comments on inherent robustness of MPC and provides a base for design methods that take the uncertainty into account more directly, i.e. robust and stochastic MPC.

Robust MPC (Sasa V. Rakovic)
This part focuses on exact robust MPC and, motivated by its computational intractability, it highlights importance of careful use of parametrized control policies in order to computationally simplify the exact robust MPC whilst preserving as many of its strong structural properties as possible. A particular emphasis is given to tube MPC framework that addresses effectively the fundamental challenge of reaching a meaningful compromise between the quality of guaranteed structural properties and the associated computational complexity. This part also builds upon generic introduction to tube MPC by providing an overview (in terms of theory and design aspects for) of basic and advanced tube MPC design methods, namely rigid, homothetic and parameterized tube MPC synthesis.

Stochastic MPC (Ilya V. Kolmanovsky)
In this part of the workshop, variants of MPC frequently referred to as stochastic MPC (SMPC) are considered. The presentation of SMPC will begin with a more in-depth discussion of modeling/representation of stochastic uncertainties and probabilistic handling of constraints. Then, existing approaches to SMPC for linear systems are discussed. The presentation continues with extension to the case of SMPC for general nonlinear systems. Mechanisms for guaranteeing recursive feasibility and stochastic stability properties in SMPC problems are also described. Finally, special problems such as of dual (i.e., combined identification/estimation and tracking) control and drift counteraction/optimal stopping are introduced, and their treatment within MPC, are covered as motivated by practical applications.

Convexification for MPC Under Uncertainty with Reliable Online Computations (Behcet Acikmese)
This part presents methods of convexification and real-time convex optimization for robust and stochastic MPC problems. It begins with an introduction of recent analytical results enabling the formulation of a class of MPC problems within convex optimization framework, as well as presenting linear matrix inequality based methods for handling deterministic and probabilistic disturbances in MPC with particular emphasis on the model uncertainties described via incremental quadratic inequalities. The presentation is concluded with a summary of recent advances in real-time optimization and convex optimization. A particular emphasis is placed on the development of custom Interior Point Method algorithms and methods for customization and autocoding that lead to real-time implementable software.

Overview of Applications and Closing Open Discussion
Applications of MPC under uncertainty have been reported in many domains, including finance, building control, electric power grid, chemical process industry, and automotive and aerospace systems. The applications to automotive and aerospace systems are of special interest as these systems operate in uncertain environment, have fast dynamics and very limited onboard computing power. Consequently, these application are in the focus of the workshop.

A closing open discussion aims at assessing the current state of affairs in, and identifying relevant future research directions in terms of theory and applications.

Primary Objectives
The major goals of the workshop are to provide a comprehensive tutorial of both fundamental and advanced aspects of MPC under uncertainty and present a unified treatment of its conceptual and practical aspects. More specifically, the workshop delivers a compact understanding of MPC as well as comprehensive theoretical foundation underpinning MPC under both the setÐmembership and stochastic uncertainty (a.k.a. robust and stochastic MPC). The workshop also covers the numerical implementation of conventional, robust and stochastic MPC design methods via advanced and reliable realÐtime optimization techniques. Thus, the workshop synergistically fuses theoretical, computational and applicationsÐdriven aspects of MPC under uncertainty and, consequently, furnishes a unique blend of advanced control synthesis and analysis methods.

An equally important aim of the workshop is to highlight the importance of MPC under uncertainty and disseminate the knowledge of advanced robust and stochastic MPC as powerful design methods with high potential to successfully tackle realÐlife problems across a wide range of traditional and emerging industrial applications.

Attendee Benefits
The workshop is designed carefully and flexibly in order to be accessible to a broad range of researchers and engineers within both academia and industry. It is entirely presented by the four organizers in a coherent and effective manner. The workshop provides, for junior researchers and students, a comprehensive exposure to advanced theory and design of MPC for constrained systems subject to uncertainty. On the other hand, the workshop delivers a systematic framework for senior researchers and engineers working on real-life industrial problems where constraints and uncertainty play a key role. A closing open panel discussion assesses the current state of affairs in, and identifies relevant future research directions for this field in terms of theory and applications. This one full day event aims to stimulate the creation of a specialized network of researchers focused on further advances in this highly important research field.

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Collaborative Sensing, Learning, and Control in Human-Machine Systems
Organizers: Nisar Ahmed, Soumik Sarkar, Girish Chowdhary, Luca F. Bertuccelli
Recent research at the juncture of decision and learning theory are leading to fundamentally new ways of understanding and tackling challenges in sensing, estimation and control problems. This is especially true for human-machine systems, where autonomous machines can efficiently exploit human insights, knowledge and information-gathering capabilities to improve robustness and performance in complex, time- and safety-critical situations. To enable seamless human-machine collaboration, the community is striving to solve fundamental questions such as asking human the right question(s) at the right time in the right form and vice-versa; understanding each other’s (human and machine) perspective; fusing soft, imperfect information with hard, sensory data; and adapting to complex and possibly unforeseen environment and situations.

In this context, this workshop aims to bring together experts in the fields of control theory, machine learning, artificial intelligence, and human-machine systems to discuss the fundamentals, state of the art and open questions for the various related topic areas including but are not limited to: value of information and optimization/learning strategies for collaboration; Bayesian and non-Bayesian techniques for modeling and adaptation; modeling and explorative/exploitative learning of human capabilities; human-machine communication; team planning and control; interaction in multi human/multi-machine systems; applications to robotics, decision support systems, cyber-physical systems, and socially important domains such as transportation, energy, medicine, manufacturing, and beyond.

The workshop web page can be found at http://web.me.iastate.edu/soumiks/workshops/acchms2016/index.html

[Click here for more information].

Methods of Easily Verifiable Control Design
Organizers: Mahdi Shahbakhti, J. Karl Hedrick, Kenneth R. Butts
The goal of this workshop is to provide an overview of Verification and Validation (V&V) for controllers, and to introduce advanced techniques of designing controllers robust to implementation imprecision. The workshop materials are partially based upon the work supported by the United States National Science Foundation (NSF).

V&V includes essential stages in the design cycle of controllers. V&V for complex dynamic systems is costly and time consuming. Reducing cost and time of V&V is a major challenge for all complex control systems. A large number of errors detected during independent V&V are introduced during the initial stages of controller development. V&V would cost 10 times less if those errors could be identified and fixed during the early stages of controller software design. Design and implementation of controllers involves the interaction and coordination of three disciplines: control engineering, software engineering, and electronic hardware engineering. A critical gap occurs when uncertainty in controller software/hardware implementation is not considered as part of the controller design.

This workshop presents new theoretical and experimental approaches for design of easily verifiable controllers that minimize V&V iterations for complex industrial control systems. The workshop materials range from introductory to state-of-the art research using our previous work and recent results from NSF sponsored project on the topic of easily verifiable controller design. Our new results show that the tracking performance of controllers under implementation imprecision can be improved by 40-90% using our proposed control design techniques. In this workshop, we will (i) provide an overview of V&V and major sources of controller implementation imprecisions, (ii) present techniques to model/simulate uncertainties arising from controller implementation, (iii) introduce new approaches to make a controller design robust to implementation uncertainties that originate from sampling, quantization, and processor computation limit, (iv) describe techniques to assess controllers robustness in software-in-the-loop, processor-in-the-loop, and hardware-in-the-loop test setups, and (v) have in-workshop real-time demonstration of the introduced techniques on an experimental test bench designed for control of engine throttle body (i.e., DC brush motor).

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Field-programmable Gate Array Implementation for High-speed, High-bandwidth Feedforward Control
Organizers: Juan Ren, Qingze Zou, Kam K. Leang
Presenters: Juan Ren, Qingze Zou, Kam K. Leang and William Nagel
Advanced feedforward control techniques have been successfully demonstrated in applications such as high- speed nanopositioning and scanning probe microscopy. In areas such as manufacturing and robotic manipulation, advanced feedforward control is essential to achieve superior performance. Practical implementation of these advanced control techniques in high-speed, high-bandwidth motion control, however, tends to be challenged by hardware limitations, as the conventional microprocessor-based data acquisition systems fail to provide a fast enough round-loop sampling frequency to execute these algorithms. Field-programmable gate array (FPGA) possesses great potential to address this challenge as FPGA does not only provide the hard- ware flexibility in realizing various controller architectures, but also enable ultra-high round-loop sampling rate to implement advanced control techniques for high-speed, high-bandwidth systems. However, applications of FPGAs by the control community are still rather limited due to factors including unawareness of the advantages of FPGAs or hesitation caused by the technical challenges involved in FPGA-based controller implementations. Therefore, we would like to, through this workshop, bridge the recent advances in advanced control techniques and their implementations in practical applications using FPGA. We will provide a step-by-step tutorial on FPGA for control applications, and stimulate and encourage explorations of advanced feedforward control and FPGA implementations to tackle challenges in emerging areas including biomedical and probe-based microscopy applications. In particular, we will first provide a brief introduction on recent advances in feedforward control techniques, then give a basic tutorial on FPGA and its use in controller de- sign and implementation; second, we will illustrate and demonstrate the applications of FPGA in high-speed motion control using broadband nanomechanical quantification and ultra-high-speed AFM imaging as two examples; and finally, we will conclude the workshop through discussions of the potential and challenges of using FPGAs in emerging applications.

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