Adaptive fault tolerant control for time-varying delay system with actuator fault and mismatched disturbance

On the existence of Equivalent-Input-Disturbance and multiple integral augmentation via H-Infinity Synthesis for unmatched systems

In this paper, the existence of a solution for the transformation of the disturbances from the unmatched cases to the matched one is investigated. The usage of matched/unmatched disturbance notions and the underlying assumptions are clarified. Then, a simplified definition is introduced to obtain a set of performance metrics to be used in observer design. Using bilinear pole shifting and multiple integral augmentation to the plant, not only the stabilizability/detectability conditions but also infinity-norm bounds for unstable MIMO systems are derived. Then, the solvability of the augmented Hamiltonian matrices to get stabilizing solutions via standard H_∞-Synthesis is explained. Finally, the solutions, definitions, and assumptions are validated through numerical examples.

Fault reconstruction and resilient control for discrete-time stochastic systems

In this paper, a novel resilient control technique is proposed for discrete-time stochastic Brownian systems with simultaneous unknown inputs and unexpected faults. Prior to previous work, the stochastic Brownian system under consideration is quite general, where stochastic perturbations exist in states, control inputs, uncertainties, and faults. Moreover, the unknown input uncertainties concerned cannot be fully decoupled. Innovative observer by employing augmented system approach, decomposition observer, and optimization algorithms is proposed to achieve simultaneous estimates of both states and faults. Furthermore, fault reconstruction-based signal compensation is formulated to alleviate the effects from actuator faults and sensor faults. An observer-based controller is eventually constructed to enhance the stability and robustness of the closed-loop dynamic system. The integrated resilient control technique can ensure the system has reliable output even under faults. Both linear systems and Lipschitz nonlinear systems are investigated and the design procedures are addressed, respectively. Finally, the proposed resilient control techniques are validated via an electromechanical servo-system, and an aircraft system.

Model reference adaptive tracking control for hydraulic servo systems with nonlinear neural-networks

It is well known that hydraulic systems typically suffer from heavy disturbances including parametric uncertainties and unknown disturbances. In order to attain high performance tracking control, this paper proposes a composite design of nonlinear neural-networks (NN) and continuous robust integral of the sign of the error (RISE) feedback controller. The control development incorporates a NN feedforward component to have a compensation for unknown state-dependent disturbances and to further improve the accuracy of feedforward compensation, meanwhile input parameter is updated online. To achieve asymptotic stability, a novel RISE term with NN-based feedforward component is developed for the first time to enable the incorporation of model reference adaptive control structure where acceleration signal is not employed. The proposed controller guarantees controlled hydraulic system a semi-global asymptotic stability. For the experimental results, the prescribed transient performance is tested under rectangular trajectory and the steady state performance is tested under sinusoidal trajectory.