Neural Feedback Scheduling of Real-Time Control Tasks

Many embedded real-time control systems suffer from resource constraints and dynamic workload variations. Although optimal feedback scheduling schemes are in principle capable of maximizing the overall control performance of multitasking control systems, most of them induce excessively large computational overheads associated with the mathematical optimization routines involved and hence are not directly applicable to practical systems. To optimize the overall control performance while minimizing the overhead of feedback scheduling, this paper proposes an efficient feedback scheduling scheme based on feedforward neural networks. Using the optimal solutions obtained offline by mathematical optimization methods, a back-propagation (BP) neural network is designed to adapt online the sampling periods of concurrent control tasks with respect to changes in computing resource availability. Numerical simulation results show that the proposed scheme can reduce the computational overhead significantly while delivering almost the same overall control performance as compared to optimal feedback scheduling.Comment: To appear in International Journal of Innovative Computing, Information and Contro

Optimal On-Line Scheduling of Multiple Control Tasks: A Case Study

We study the problem of dynamically scheduling a set of state-feedback control tasks controlling a set of linear plants. We consider an on-line non-preemptive scheduling policy that is optimal in the sense that it minimizes a quadratic performance criterion for the overall system. The optimal scheduling decision at each point in time is a function of the states of the controlled plants. To be able to solve the scheduling problem for realistic examples, we use the technique of relaxed dynamic programming to compute suboptimal solutions with error bounds. The approach is compared to earlier approaches in a case study involving simultaneous control of one ball-and-beam process and two DC-servo processes. We also show how the scheduling policy can be modified to allow for background tasks to execute when the need for control is small. 1

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

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

Dynamic Incentives for Optimal Control of Competitive Power Systems

This work presents a real-time dynamic pricing framework for future electricity markets. Deduced by first-principles analysis of physical, economic, and communication constraints within the power system, the proposed feedback control mechanism ensures both closed-loop system stability and economic efficiency at any given time. The resulting price signals are able to incentivize competitive market participants to eliminate spatio-temporal shortages in power supply quickly and purposively

DYNAMIC ORIGIN-DESTINATION DEMAND ESTIMATION AND PREDICTION FOR OFF-LINE AND ON-LINE DYNAMIC TRAFFIC ASSIGNMENT OPERATION

Time-dependent Origin-Destination (OD) demand information is a fundamental input for Dynamic Traffic Assignment (DTA) models to describe and predict time-varying traffic network flow patterns, as well as to generate anticipatory and coordinated control and information supply strategies for intelligent traffic network management. This dissertation addresses a series of critical and challenging issues in estimating and predicting dynamic OD demand for off-line and on-line DTA operation in a large-scale traffic network with various information sources. Based on an iterative bi-level estimation framework, this dissertation aims to enhance the quality of OD demand estimates by combining available historical static demand information and time-varying traffic measurements into a multi-objective optimization framework that minimizes the overall sum of squared deviations. The multi-day link traffic counts are also utilized to estimate the variation in traffic demand over multiple days. To circumvent the difficulties of obtaining sampling rates in a demand population, this research proposes a novel OD demand estimation formulation to effectively exploit OD demand distribution information provided by emerging Automatic Vehicle Identification (AVI) sensor data, and presents several robust formulations to accommodate possible deviations from idealized conditions in the demand estimation process. A structural real-time OD demand estimation and prediction model and a polynomial trend filter are developed to systematically model regular demand pattern information, structural deviations and random fluctuations, so as to provide reliable prediction and capture the structural changes in time-varying demand. Based on a Kalman filtering framework, an optimal adaptive updating procedure is further presented to use the real-time demand estimates to obtain a priori estimates of the mean and variance of regular demand patterns. To maintain a representation of the network states which consistent with that of the real-world traffic system in a real-time operational environment, this research proposes a dynamic OD demand optimal adjustment model and efficient sub-optimal feedback controllers to regulate the demand input for the real-time DTA simulator while reducing the adjustment magnitude

Development of adaptive control methodologies and algorithms for nonlinear dynamic systems based on u-control framework

Inspired by the U-model based control system design (or called U-control system design), this study is mainly divided into three parts. The first one is a U-model based control system for unstable non-minimum phase system. Pulling theorems are proposed to apply zeros pulling filters and poles pulling filters to pass the unstable non-minimum phase characteristics of the plant model/system. The zeros pulling filters and poles pulling filters derive from a customised desired minimum phase plant model. The remaining controller design can be any classic control systems or U-model based control system. The difference between classic control systems and U-model based control system for unstable non-minimum phase will be shown in the case studies.Secondly, the U-model framework is proposed to integrate the direct model reference adaptive control with MIT normalised rules for nonlinear dynamic systems. The U-model based direct model reference adaptive control is defined as an enhanced direct model reference adaptive control expanding the application range from linear system to nonlinear system. The estimated parameter of the nonlinear dynamic system will be placement as the estimated gain of a customised linear virtual plant model with MIT normalised rules. The customised linear virtual plant model is the same form as the reference model. Moreover, the U-model framework is design for the nonlinear dynamic system within the root inversion.Thirdly, similar to the structure of the U-model based direct model reference adaptive control with MIT normalised rules, the U-model based direct model reference adaptive control with Lyapunov algorithms proposes a linear virtual plant model as well, estimated and adapted the particular parameters as the estimated gain which of the nonlinear plant model by Lyapunov algorithms. The root inversion such as Newton-Ralphson algorithm provides the simply and concise method to obtain the inversion of the nonlinear system without the estimated gain. The proposed U-model based direct control system design approach is applied to develop the controller for a nonlinear system to implement the linear adaptive control. The computational experiments are presented to validate the effectiveness and efficiency of the proposed U-model based direct model reference adaptive control approach and stabilise with satisfied performance as applying for the linear plant model

On-line monitoring of water distribution networks

This thesis is concerned with the development of a computer-based, real-time monitoring scheme which is a prerequisite of any form of on-line control. A new concept, in the field of water distribution systems, of water system state estimation is introduced. Its function is to process redundant, noise-corrupted telemeasurements in order to supply a real-time data base with reliable estimates of the current state and structure of the network. The information provided by the estimator can then be used in a number of on-line programs. In view of the strong nonlinearity of the network equations, two methods of state estimation, which have enhanced numerical stability, are examined in this thesis. The first method uses an augmented matrix formulation of a classical least-squares problem, and the second is based on a least absolute value solution of an over determined set of equations. Two water systems, one of which is a realistic 34-node network, are used to evaluate the performance of the proposed methods .The problem of bad data processing and its extension to the validation of network topology and leakage detection is also examined. It is shown that the method based on least absolute values estimation provides a more immediate indication of erroneous measurements. In addition, this method demonstrates the useful feature of eliminating the effects of gross errors on the final state estimate. The important question of water system observability is then studied. Two original combinatorial methods are proposed to check topological observability. The first one is an indirect technique which searches for a maximum measurement-to-branch matching and then attempts to build a spanning tree of the network graph using only the branches with measurement assignment. The second method is a direct search for an observable spanning tree. A number of systems are used to test both techniques, including a 34-node water supply network and an IEEE 118-bus power system. The problem of minimisation of distributed leakages is solved efficiently using a state estimation technique. Comparison of the head profile achieved for the calculated optimal valve controls with the standard operating conditions for a 25-node network indicates a major reduction of the volume of leakages. In the final part of this thesis a software package, which simulates the real-time operation of a water distribution system, is described. The programs are designed in such a way that by replacing simulated measurements with live telemetry data they can be directly used for. water network monitoring and control