Robust Adaptive Fault-Tolerant Control of Stochastic Systems with Modeling Uncertainties and Actuator Failures

This paper deals with the problem of fault-tolerant control (FTC) of uncertain stochastic systems subject to modeling uncertainties and actuator failures. A robust adaptive fault-tolerant controller design method based on stochastic Lyapunov theory is developed to accommodate the negative impact on system performance arising from uncertain system parameters and external disturbances as well as actuation faults. There is no need for on-line fault detection and diagnosis (FDD) unit in the proposed FTC scheme, which not only simplifies the design process but also makes the implementation inexpensive. Numerical examples are provided to validate and illustrate the benefits of the proposed control method

Actuator failure identification and compensation through sliding modes

The actuator failure compensation problem is addressed in this brief. It is considered an uncertain linear plant, which is supposed to undergo unknown failures causing the plant input components to be stuck at some uncertain but bounded time functions. A sliding-mode-based control policy is presented, guaranteeing the detection of the fault and the identification of the failed component by means of a suitable test input. Once the failed component has been identified, the control law is reconfigured, redistributing the control activity among the controllers still working. The proposed controller has been tested by simulation on a benchmark problem

Genetic design of multivariable control systems

In the real world there are three types of multivariable control systems. The first one is when the number of inputs is equal to the number of the outputs, this type of multivariable control system is defined as a squared multivariable control system and the main type of controller designed is a decoupling controller which minimizes interactions and gives good set-point tracking. The second type of multivariable control system is where the number of inputs is greater than the number of the outputs, for this type of system the main controller designed is a fail-safe controller. This controller remains stable if a sub-set of actuator fail. The third type of multivariable control system is the number of outputs is greater than the number of inputs, for this type of system the main controller designed is an override control system. This controller only controls a sub-set of outputs based on a lowest wins control strategy. All the three types of multivariable control systems are included in this thesis. In this thesis the design of multivariable decoupling control, multivariable fail-safe control and multivariable override control as considered. The invention of evolutionary computing techniques has changed the design philosophy for control system design. Rather than using conventional techniques such as Nyquest plots or root-loci control systems can be designed using evolutionally algorithm. Such algorithms evolve solutions using cost functions and optimization. There are a variety of system performance indicators such as integral squared error operator has been used as cost functions to design controllers using such algorithms. The design of both fail-safe and override multivariable controllers is a difficult problem and there are very few analytical design methods for such controllers. Therefore, the main objective of this thesis is to use the genetic algorithms to involve both fail-safe and override controller multivariable controllers, such that they perform well in the time-domain