Flexible Scheduling Methods and Tools for Real-Time Control Systems

This thesis deals with flexibility in the design of real-time control systems. By dynamic resource scheduling it is possible to achieve on-line adaptability and increased control performance under resource constraints. The approach requires simulation tools for control and real-time systems co-design. One approach to achieve flexibility in the run-time scheduling of control tasks is feedback scheduling, where resources are scheduled dynamically based on measurements of actual timing variations and control performance. An overview of feedback scheduling techniques for control systems is presented.A flexible strategy for implementation of model predictive control (MPC) is described. In MPC, the control signal in each sample is obtained by the solution of a constrained quadratic optimization problem. A termination criterion is derived that, unlike traditional MPC, takes the effects of computational delay into account in the optimization. A scheduling scheme is also described, where the MPC cost functions being minimized are used as dynamic task priorities for a set of MPC tasks. The MATLAB/Simulink-based simulator TrueTime is presented. TrueTime is a co-design tool that facilitates simulation of distributed real-time control systems, where the execution of controller tasks in a real-time kernel is simulated in parallel with network transmissions and the continuous-time plant dynamics. Using TrueTime it is possible to study the effects of CPU and network scheduling on control performance and to experiment with flexible scheduling techniques and compensation schemes. A general overview of the simulator is given and the event-based kernel implementation is described.TrueTime is used in two simulation case studies. The first emulates TCP on top of standard Ethernet to simulate networked control of a robot system. The second case study uses TrueTime to simulate a web server application. A feedback scheduling strategy for QoS control in the web server is described

TinyRealTime - An EDF Kernel for the Atmel ATmega8L AVR

This report describes the design and implementation of {TinyRealTime}, an event-based real-time kernel for the Atmel AVR ATmega8L 8-bit micro-controller. The kernel is event-based and supports fully preemptive earliest-deadline-first scheduling of tasks. Semaphores are provided to support task synchronization. The focus of the report is on the memory management, timing, and internal workings of the kernel. The flash memory footprint of the kernel is approximately 1200 bytes and it occupies 11 bytes of SRAM memory for the kernel data structure plus an additional 11 bytes for each task and one byte for each semaphore. An application example is described, where the real-time kernel is used to implement concurrent control of two ball and beam laboratory processes using six application tasks

Flexible Implementation of Model Predictive Control Using Sub-Optimal Solutions

The on-line computational demands of model predictive control (MPC) often prevents its application to processes where fast sampling is necessary. This report presents a strategy for reducing the computational delay resulting from the on-line optimization inherent in many MPC formulations. Recent results have shown that feasibility, rather than optimality, is a prerequisite for stabilizing MPC algorithms, implying that premature termination of the optimization procedure may be valid, without compromising stability. The main result included in the report is a termination criterion for the on-line optimization algorithm giving rise to a sub-optimal, yet stabilizing, MPC algorithm. The termination criterion, based on an associated delay-dependent cost index, quantifies the trade-off between successively improved control profiles resulting form the optimization algorithm and the potential performance degradation due to increasing computational delay. It is also shown how the cost index may be used in a dynamic scheduling application, where the processor time is shared between two MPC tasks executing on the same CPU

Optimal On-line Sampling Period Assignment for Real-Time Control Tasks Based on Plant State Information

The paper presents a feedback scheduling strategy for multiple control tasks that uses feedback from the plant states to distribute the computing resources optimally among the tasks. Linear-quadratic controllers are analyzed, and expressions relating the expected cost to the sampling period and the plant state are derived and used for on-line sample-rate adjustments. In the case of minimum-variance control of multiple integrator processes, an exact expression for the optimal sampling periods is obtained. For the general case, an on-line optimization procedure is developed. The approach is exemplified on a set of controllers for first-order systems. The issues of computational delay and the choice of the feedback scheduler period are also discussed

Maximizing the Use of Computational Resources in Multi-Camera Feedback Control

In vision-based feedback control systems, the time to obtain sensor information is usually non-negligible, and these systems thereby possess fundamentally different timing behavior compared to standard real-time control applications. For many image-based tracking algorithms, however, it is possible to trade-off the computational time versus the accuracy of the produced position/orientation estimates.This paper presents a method for optimizing the use of computational resources in a multi-camera based positioning system. A simplified equation for the covariance of the position estimation error is calculated, which depends on the set of cameras used and the number of edge detection points in each image. An efficient algorithm for selection of a suitable subset of the available cameras is presented, which attempts to minimize the estimation covariance given a desired, pre-specified maximum input-output latency of the feedback control loop.Simulations have been performed that capture the real-time properties of the vision-based tracking algorithm and the effects of the timing on the performance of the control system. The suggested strategy has been compared with heuristic algorithms, and it obtains large improvements in estimation accuracy and performance for objects both in free motion and under closed-loop position control

Simulation of Networked Control Systems Using TrueTime

This paper gives a brief introduction to the TrueTime simulator and then gives several examples on how TrueTime can be used to simulate networked control systems. Among the examples are time-triggered and event-based networked control and AODV routing in wireless ad-hoc networks

TrueTime: Real-time Control System Simulation with MATLAB/Simulink

Traditional control design using MATLAB/Simulink, often disregards the temporal effects arising fromthe actual implementation of the controllers. Nowadays, controllersare often implemented as tasks in a real-time kernel and communicatewith other nodes over a network. Consequently, the constraints of thetarget system, e.g., limited CPU speed and network bandwidth, must betaken into account at design time.For this purpose we have developed TrueTime, a toolbox forsimulation of distributed real-time control systems. TrueTimemakes it possible to simulate the timely behavior of real-time kernelsexecuting controller tasks. TrueTime also makes it possibleto simulate simple models of network protocols and their influence onnetworked control loops.TrueTime consists of a kernel block and a network block, bothvariable-step S-functions written in C++. TrueTimealso provides a collection of MATLAB functions used to, e.g., do A/Dand D/A conversion, send and receive network messages, set up timers,and change task attributes. The TrueTime blocks are connectedwith ordinary continuous Simulink blocks to form a real-time controlsystem.The TrueTime kernel block simulates a computer with anevent-driven real-time kernel, A/D and D/A converters, a networkinterface, and external interrupt channels. The kernel executesuser-defined tasks and interrupt handlers, representing, e.g., I/Otasks, control algorithms, and communication tasks. Execution isdefined by user-written code functions (C++ functions orm-files) or graphically using ordinary discrete Simulink blocks. Thesimulated execution time of the code may be modeled as constant,random or even data-dependent. Furthermore, the real-time schedulingpolicy of the kernel is arbitrary and decided by the user.The TrueTime network block is event driven and distributesmessages between computer nodes according to a chosen network model.Currently five of the most common medium access control protocols aresupported (CSMA/CD (Ethernet), CSMA/CA (CAN), token-ring, FDMA, andTDMA). It is also possible to specify network parameters such astransmission rate, pre- and post-processing delays, frame overhead,and loss probability.TrueTime is currently used as an experimental platform forresearch on flexible approaches to real-time implementation andscheduling of controller tasks. One example is feedback schedulingwhere feedback is used in the real-time system to dynamicallydistribute resources according to the current situation in the system

Resource-Constrained Embedded Control Systems: Possibilities and Research Issues

A survey that points out research issues and open problems in the area of integrated control and real-time scheduling. Issues that are discussed include temporal robustness, schedulability margin, optimal and direct feedback scheduling, quality-of-control, and tools

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 mobile agents and wireless sensor/actuator networks. The complexity of these applications make co-simulation a necessary tool during system development. This paper presents a simulation environment that facilitates simulation of computer nodes and communication networks interacting 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

Resource Management for Control Tasks Based on the Transient Dynamics of Closed-Loop Systems

This paper presents a resource management strategy for control tasks that maximizes control performance within the available resources by readjusting the task periods at run-time. A feedback scheduler is used to determine on-line the optimal task periods considering the response over a finite time horizon of the plants controlled by arbitrary linear control laws. We show how this problem can be expressed as an optimization problem, where the objective function relates the sampling periods to the transient responses of the controlled plants, and where restrictions are based on EDF schedulability constraints. For the general case, the solution of the optimization problem is computationally expensive, and thus, an approximate procedure to be executed on-line has been developed. We present simulation results that validate the presented approach