Assessment and diagnosis of feedforward/feedback control system
A sequence of statistical hypothesis procedures for assessing a feedforward/feedback control performance, diagnosing and removing its fault causes is proposed. Based on the controlled output, the current variance is contributed by the feedback only effect and the combination of feedback with feedforward effect, respectively. These effect variations are systematically conducted by a sequence of the statistic hypothesis testing and the isolation strategy to compare the current control performance and the achievable benchmark operating one. A diagnosis tree with hierarchical structure is constructed. The capability of the proposal is illustrated through a simulation case with multiple faults.
Assessment of achievable PI control performance for linear processes with dead time
A numerical scheme is outlined for the estimation of achievable PI control performance measured by output variance in linear processes with dead time when stochastic load disturbances are affecting the process. For this numerical scheme, an approximate stochastic realization is employed for disturbance model identification, and this realized model is then used for the achievable performance prediction. The approximate stochastic disturbance model realization is performed based on the feedback invariant closed-loop impulse response coefficients so that the impulse response coefficients of the realized disturbance model are approximately equal to a given feedback invariant impulse response sequence. A computer simulation is carried out for the estimation of achievable PI control performance, and the result is compared to the true PI control performance bound.
ADAPTIVE DEAD-TIME ESTIMATION
Abstract A method for simultaneous estimation of process parameters and process time delay is presented. It requires a minor extension of the original recursive least squares method. It allows for the parameter-adaptive control of dead-time processes without a priori information about the exact time delay. It also allows for tracking of time-varying process dead-time. The main importance is that the robustness of some types of self-tuning and adaptive control algorithms is substantially improved. The method is thus very promising from a practical point of view.