Yixin Diao

Stable fault-tolerant adaptive fuzzy/neural control for a turbine engine

Stimulated by the growing demand for improving the reliability and performance of systems, fault-tolerant control has been receiving significant attention since its goal is to detect the occurrence of faults and achieve satisfactory system performance in the presence of faults. To develop an intelligent fault-tolerant control system, we begin by constructing a design model of the system using a hierarchical learning structure in the form of Takagi-Sugeno fuzzy systems. Afterwards, the fault-tolerant control scheme is designed based on stable adaptive fuzzy/neural control, where its online learning capabilities are used to capture the unknown dynamics caused by faults. Finally, the effectiveness of the proposed methods has been studied by extensive analysis of system zero dynamics and asymptotic tracking abilities for both indirect and direct adaptive control cases, and by component level model simulation of the General Electric XTE46 turbine engine.

MIMO control of an Apache web server: modeling and controller design

This paper considers the efficacy of feedback control in improving the performance of computing systems. Computing systems typically have many competing performance goals which are affected by several external variables. A feedback control strategy is desirable because well established techniques exist to handle these performance trade-offs and external disturbances. In order to employ such a strategy, decisions need to be made about the inputs, outputs, sample time, model type, and performance measures. This paper describes this process, which is often nebulous for computing systems, in the context of an Apache web server. A linear multi-input multi-output model of the system is identified experimentally and used to design several feedback controllers. Experimental results are presented showing the problems associated with a pure pole placement design and effectiveness of LQ control based techniques. The paper concludes with a discussion of future work.

Intelligent fault-tolerant control using adaptive and learning methods

Abstract Stimulated by the growing demand for improving system performance and reliability, fault-tolerant system design has been receiving significant attention. This paper proposes a new fault-tolerant control methodology using adaptive estimation and control approaches based on the learning capabilities of neural networks or fuzzy systems. On-line approximation-based stable adaptive neural/fuzzy control is studied for a class of input–output feedback linearizable time-varying nonlinear systems. This class of systems is large enough so that it is not only of theoretical interest but also of practical applicability. Moreover, the fault-tolerance ability of the adaptive controller has been further improved by exploiting information estimated from a fault-diagnosis unit designed by interfacing multiple models with an expert supervisory scheme. Simulation examples for a fault-tolerant jet engine control problem are given to demonstrate the effectiveness of the proposed scheme.

Adaptive neural/fuzzy control for interpolated nonlinear systems

Adaptive control for nonlinear time-varying systems is of both theoretical and practical importance. We propose an adaptive control methodology for a class of nonlinear systems with a time-varying structure. This class of systems is composed of interpolations of nonlinear subsystems which are input-output feedback linearizable. Both indirect and direct adaptive control methods are developed, where the spatially localized models (in the form of Takagi-Sugeno fuzzy systems or radial basis function neural networks) are used as online approximators to learn the unknown dynamics of the system. Without assumptions on rate of change of system dynamics, the proposed adaptive control methods guarantee that all internal signals of the system are bounded and the tracking error is asymptotically stable. The performance of the adaptive controller is demonstrated using a jet engine control problem.

Incorporating cost of control into the design of a load balancing controller

Load balancing is widely used in computing systems as a way to optimize performance by reducing bottleneck utilizations, such as adjusting the size of buffer pools to balance resource demands in a database management system. Load balancing is generally approached as a constrained optimization problem in which only the benefits of load balancing are considered. However, the costs of control are important as well. Herein, we study the value of including in controller design the trade-off between the cost of transient imbalances in resource utilizations and the cost of changing resource allocations. An example of the latter are actions such as resizing buffer pools that can reduce throughputs. This is because requests for data in pools whose memory is reduced immediately have longer access times whereas requests for data in pools whose memory is increased must fill this memory with data from disk before accessed times are reduced. We frame our study of control costs in terms of the widely used linear quadratic regulator (LQR). We develop a cost model that allows us to specify the LQR Q and R matrices based on the impact on system performance of changing resource allocations and transient load imbalances. Our studies of a DB2 universal database server using benchmarks for online transaction processing and decision support workloads show that incorporating our cost model into the MIMO LQR controller results in a 14% improvement in performance beyond that achieved by dynamically allocating the size of buffers without properly considering the cost of control.

Immunity-based hybrid learning methods for approximator structure and parameter adjustment☆

Abstract From the point of view of information processing the immune system is a highly parallel and distributed intelligent system which has learning, memory, and associative retrieval capabilities. In this paper we present two immunity-based hybrid learning approaches for function approximation (or regression) problems that involve adjusting the structure and parameters of spatially localized models (e.g., radial basis function networks). The number and centers of the receptive fields for local models are specified by immunity-based structure adaptation algorithms, while the parameters of the local models, which enter in a linear fashion, are tuned separately using a least-squares method. The effectiveness of the procedure is demonstrated through a nonlinear function approximation problem and a nonlinear dynamical system modeling problem.

Stable adaptive control of feedback linearizable time-varying non-linear systems with application to fault-tolerant engine control

Stable indirect and direct adaptive controllers are presented for a class of input–output feedback linearizable time-varying non-linear systems. The radial basis function neural networks are used as on-line approximators to learn the time-varying characteristics of system parameters. Stability results are given in the paper, and the performance of the indirect and direct adaptive schemes is demonstrated through a fault-tolerant engine control problem where the faults are naturally time-varying.

Fault diagnosis for a turbine engine

We deal with a sophisticated component level model (CLM) simulation of a turbine engine (XTE46) that can simulate the effects of manufacturing and deterioration differences, in addition to a variety of failures. To develop a fault diagnosis system we begin by using the CLM to generate data that is used by the Levenberg-Marquardt method to train a Takagi-Sugeno fuzzy system to represent the engine. The multiple copies of this nonlinear model, each representing a different failure, are then used to generate error residuals by comparing them to the engine output. In fact, we manage the composition of the set of models with a supervisor that ensures the appropriate models are online, and that processes the error residuals to detect and identify faults. The robustness of the approach is analyzed and several simulations are conducted to illustrate the effectiveness of the method.

Intelligent fault tolerant control using adaptive schemes and multiple models

Fault tolerant control for nonlinear time-varying systems is of both theoretical and practical importance. In this paper we present an intelligent fault tolerant control methodology using an on-line approximation based adaptive controller to try to achieve system stability and robustness, and a fault diagnosis unit to provide fault information and then reconfigure the control law. Simulation examples for a fault tolerant engine control problem are given to demonstrate the effectiveness of the proposed scheme.