Input-output nominalization of linear systems with slow-varying uncertainties
Purpose - The purpose of this paper is to present a method to compensate slow varying disturbances and plant parameter drifts using a simple yet robust algorithm called input-output nominalization. Design/methodology/approach - In case of known uncertainties, an analytical expression of pre-computed feed-forward compensation command is derived. In presence of unknown disturbances and parameter drifts, the control algorithm uses a proportional-integrative estimator-based nominalizer. It creates a nominalizing signal, reflecting the deviation of the system from its nominal form using plant input and output. The signal is subtracted from the nominal controller output to cancel the uncertainty and disturbances effects. Findings - As a result, the uncertain plant and the nominalizer quickly converge to the nominal plant. Therefore, a simple controller tuned according to the nominal plant can be used despite the disturbances and parameter drifts and a nominal response is always obtained. Simulation and experimental results are given to describe the control algorithm performance and inherent limitations. Research limitations/implications - The proposed method is suitable for linear systems with low frequency uncertainties and disturbances only. Practical implications - The technique allows compensating errors in plant parameter identifications as well as parameter drifts during plant operations. Constant and slow varying disturbances are also rejected, allowing obtaining a prescribed nominal response. Originality/value - The proposed approach is different from the common robust control methods to the uncertain linear systems control. Instead of designing a robust controller, efforts are concentrated on the plant input-output nominalization in a fashion similar to input-output linearization. The method allows compensating slow varying disturbances and plant parameter drifts using a simple algorithm leading to a simple controller tuned according to the nominal plant parameters.
algorithmic languages control systems systems theory electric motors