Karl-Erik Årzén

Piecewise quadratic stability of fuzzy systems

Presents an approach to stability analysis of fuzzy systems. The analysis is based on Lyapunov functions that are continuous and piecewise quadratic. The approach exploits the gain-scheduling nature of fuzzy systems and results in stability conditions that can be verified via convex optimization over linear matrix inequalities. Examples demonstrate the many improvements over analysis based on a single quadratic Lyapunov function. Special attention is given to the computational aspects of the approach and several methods to improve the computational efficiency are described.

A Simple Event-Based PID Controller

A simple event-based PID controller is presented. It is shown that it is possible to obtain large reductions in CPU utilization with only minor control performance degradation. Simulations on a double-tank process are presented.

Real Time Scheduling Theory: A Historical Perspective

In this 25th year anniversary paper for the IEEE Real Time Systems Symposium, we review the key results in real-time scheduling theory and the historical events that led to the establishment of the current real-time computing infrastructure. We conclude this paper by looking at the challenges ahead of us.

How does control timing affect performance? Analysis and simulation of timing using Jitterbug and TrueTime

To achieve good performance in systems with limited computer resources, the constraints of the implementation platform must be taken into account at design time. To facilitate this, software tools are needed to analyze and simulate how timing affects control performance. This article describes two such tools: Jitterbug and TrueTime.

Feedback–Feedforward Scheduling of Control Tasks

A scheduling architecture for real-time control tasks is proposed. The scheduler uses feedback from execution-time measurements and feedforward from workload changes to adjust the sampling periods of the control tasks so that the combined performance of the controllers is optimized. The performance of each controller is described by a cost function. Based on the solution to the optimal resource allocation problem, explicit solutions are derived for linear and quadratic approximations of the cost functions. It is shown that a linear rescaling of the nominal sampling frequencies is optimal for both of these approximations. An extensive inverted pendulum example is presented, where the performance obtained with open-loop, feedback, combined feedback and feedforward scheduling, and earliest-deadline first scheduling are compared. The performance under earliest-deadline first scheduling is explained by studying the behavior of periodic tasks under overload conditions. It is shown that the average values of the sampling periods equal the nominal periods, rescaled by the processor utilization.

Modeling and Optimization with Optimica and JModelica.org—Languages and Tools for Solving Large-Scale Dynamic Optimization Problems

The Modelica language, targeted at modeling of complex physical systems, has gained increased attention during the last decade. Modelica is about to establish itself as a de facto standard in the modeling community with strong support both within academia and industry. While there are several tools, both commercial and free, supporting simulation of Modelica models few efforts have been made in the area of dynamic optimization of Modelica models. In this paper, an extension to the Modelica language, entitled Optimica, is reported. Optimica enables compact and intuitive formulations of optimization problems, static and dynamic, based on Modelica models. The paper also reports a novel Modelica-based open source project, JModelica.org, specifically targeted at dynamic optimization. JModelica.org supports the Optimica extension and offers an open platform based on established technologies, including Python, C, Java and XML. Examples are provided to demonstrate the capabilities of Optimica and JModelica.org.

TrueTime: Simulation of Control Loops Under Shared Computer Resources

Abstract The paper presents TrueTime, a Matlab/Simulink-based simulator for real-time control systems. TrueTime makes it possible to simulate the temporal behavior of multi-tasking real-time kernels containing controller tasks and to study the effects of CPU and network scheduling on control performance. The simulated real-time kernel is event-driven and can handle external interrupts as well as fine-grained details such as context switches. Arbitrary scheduling policies may be defined, and the control tasks may be implemented using C functions, M functions, or Simulink block diagrams. A number of examples that illustrate the use of TrueTime are presented.

An introduction to control and scheduling co-design

The paper presents the emerging field of integrated control and CPU-time scheduling, where more general scheduling models and methods that better suit the needs of control systems are developed. This creates possibilities for dynamic and flexible integrated control and scheduling frameworks, where the control design methodology takes the availability of computing resources into account during design and allows online trade-offs between control performance and computing resource utilization.

Simulation of Wireless Networked Control Systems

Embedded systems are becoming increasingly networked and are deployed in application areas that require close interaction with their physical environment. Examples include distributed mobileagents and wireless sensor/ac tuator networks. The complexity of these applic ations make co- simulation a necessary tool during system development. This paper presents a simulation environment that facilitates simu- lation of computer nodes and communic ation networks inter- acting with the continuous-time dynamics of the real world. Features of the simulator include interrupt handling, task scheduling, wired and wireless communication, local clocks, dynamic voltage scaling, and battery-driven operation. Two simulation case studies are presented: a simple communication scenario and a mobile robot soccer game.

An architecture for expert system based feedback control

Abstract In expert control a knowledge-based system is used for representing general control knowledge and heuristics concerning tuning and adaptation. The knowledge-based system is used for on-line supervision of a set of numerical algorithms for control, identification, and monitoring. The basic ideas behind expert control are described. An architecture for expert control is described where two concurrent processes are used for the knowledge-based system and the numerical algorithms. A modular, blackboard-based approach is used. This allows the decomposition of the problem into subtasks which are implemented as separate knowledge sources that can be rule-based with different inference strategies or written in terms of procedures. The framework can be compared with a real-time operating system and has similar real-time primitives. The system has been implemented successfully on a VAX 11/780. An example is given where the framework is used for controller tuning.