Communicating Processes with Data for Supervisory Coordination

We employ supervisory controllers to safely coordinate high-level discrete(-event) behavior of distributed components of complex systems. Supervisory controllers observe discrete-event system behavior, make a decision on allowed activities, and communicate the control signals to the involved parties. Models of the supervisory controllers can be automatically synthesized based on formal models of the system components and a formalization of the safe coordination (control) requirements. Based on the obtained models, code generation can be used to implement the supervisory controllers in software, on a PLC, or an embedded (micro)processor. In this article, we develop a process theory with data that supports a model-based systems engineering framework for supervisory coordination. We employ communication to distinguish between the different flows of information, i.e., observation and supervision, whereas we employ data to specify the coordination requirements more compactly, and to increase the expressivity of the framework. To illustrate the framework, we remodel an industrial case study involving coordination of maintenance procedures of a printing process of a high-tech Oce printer.Comment: In Proceedings FOCLASA 2012, arXiv:1208.432

First steps toward formal controller synthesis for bipedal robots with experimental implementation

Bipedal robots are prime examples of complex cyber–physical systems (CPSs). They exhibit many of the features that make the design and verification of CPS so difficult: hybrid dynamics, large continuous dynamics in each mode (e.g., 10 or more state variables), and nontrivial specifications involving nonlinear constraints on the state variables. In this paper, we propose a two-step approach to formally synthesize controllers for bipedal robots so as to enforce specifications by design and thereby generate physically realizable stable walking. In the first step, we design outputs and classical controllers driving these outputs to zero. The resulting controlled system evolves on a lower dimensional manifold and is described by the hybrid zero dynamics governing the remaining degrees of freedom. In the second step, we construct an abstraction of the hybrid zero dynamics that is used to synthesize a controller enforcing the desired specifications to be satisfied on the full order model. Our two step approach is a systematic way to mitigate the curse of dimensionality that hampers the applicability of formal synthesis techniques to complex CPS. Our results are illustrated with simulations showing how the synthesized controller enforces all the desired specifications and offers improved performance with respect to a classical controller. The practical relevance of the results is illustrated experimentally on the bipedal robot AMBER 3

A Process Algebra for Supervisory Coordination

A supervisory controller controls and coordinates the behavior of different components of a complex machine by observing their discrete behaviour. Supervisory control theory studies automated synthesis of controller models, known as supervisors, based on formal models of the machine components and a formalization of the requirements. Subsequently, code generation can be used to implement this supervisor in software, on a PLC, or embedded microprocessor. In this article, we take a closer look at the control loop that couples the supervisory controller and the machine. We model both event-based and state-based observations using process algebra and bisimulation-based semantics. The main application area of supervisory control that we consider is coordination, referred to as supervisory coordination, and we give an academic and an industrial example, discussing the process-theoretic concepts employed.Comment: In Proceedings PACO 2011, arXiv:1108.145

Survey on time-delay approach to networked control

This paper provides a survey on time-delay approach to networked control systems (NCSs). The survey begins from a brief summary on fundamental network-induced issues in NCSs and the main approaches to the modelling of NCSs. In particular, a comprehensive introduction to time-delay approach to sampled-data and networked control is provided. Then, recent results on time-delay approach to event-triggered control are recalled. The survey highlights time-delay approach developed to modelling, analysis and synthesis of NCSs, under communication constraints, with a particular focus on Round-Robin, Try-once-discard and stochastic protocols. The time-delay approach allows communication delays to be larger than the sampling intervals in the presence of scheduling protocols. Moreover, some results on networked control of distributed parameter systems are surveyed. Finally, conclusions and some future research directions are briefly addressed

Data-driven control for feedback linearizable single-input systems

More than a decade ago Fliess and co-workers [1], [2], [3] proposed model-free control as a possible answer to the inherent difficulties in controlling non-linear systems. Their key insight was that by using a sufficiently high sampling rate we can use a simple linear model for control purposes thereby trivializing controller design. In this paper, we provide a variation of model-free control for which it is possible to formally prove the existence of a sufficiently high sampling rate ensuring that controllers solving output regulation and tracking problems for the approximate linear model also solve the same problems for the true and unknown nonlinear model. This is verified experimentally on the bipedal robot AMBER-3M

Data-driven control for feedback linearizable single-input systems

More than a decade ago Fliess and co-workers [1], [2], [3] proposed model-free control as a possible answer to the inherent difficulties in controlling non-linear systems. Their key insight was that by using a sufficiently high sampling rate we can use a simple linear model for control purposes thereby trivializing controller design. In this paper, we provide a variation of model-free control for which it is possible to formally prove the existence of a sufficiently high sampling rate ensuring that controllers solving output regulation and tracking problems for the approximate linear model also solve the same problems for the true and unknown nonlinear model. This is verified experimentally on the bipedal robot AMBER-3M

Adaptive cruise control: Experimental validation of advanced controllers on scale-model cars

Recent advances in automotive technology, such as, sensing and onboard computation, have resulted in the development of adaptive cruise control (ACC) algorithms that improve both comfort and safety. With a view towards developing advanced controllers for ACC, this paper presents an experimental platform for validation and demonstration of an online optimization based controller. Going beyond traditional PID based controllers for ACC that lack proof of safety, we construct a control framework that gives formal guarantees of correctness. In particular, safety constraints-maintaining a valid following distance from a lead car-are represented by control barrier functions (CBFs), and control objectives- achieving a desired speed-are encoded through control Lyapunov functions (CLFs). These different objectives can be unified through a quadtraic program (QP), with constraints dictated by CBFs and CLFs, that balances safety and the control objectives in an optimal fashion. This methodology is demonstrated on scale-model cars, for which the CBF-CLF based controller is implemented online, with the end result being the experimental validation of an advanced adaptive cruise controller

Adaptive cruise control: Experimental validation of advanced controllers on scale-model cars

Abstract — Recent advances in automotive technology, such as, sensing and onboard computation, have resulted in the development of adaptive cruise control (ACC) algorithms that improve both comfort and safety. With a view towards devel-oping advanced controllers for ACC, this paper presents an experimental platform for validation and demonstration of an online optimization based controller. Going beyond traditional PID based controllers for ACC that lack proof of safety, we construct a control framework that gives formal guarantees of correctness. In particular, safety constraints—maintaining a valid following distance from a lead car—are represented by control barrier functions (CBFs), and control objectives— achieving a desired speed—are encoded through control Lya-punov functions (CLFs). These different objectives can be unified through a quadtraic program (QP), with constraints dictated by CBFs and CLFs, that balances safety and the control objectives in an optimal fashion. This methodology is demonstrated on scale-model cars, for which the CBF-CLF based controller is implemented online, with the end result being the experimental validation of an advanced adaptive cruise controller