Nowadays, industrial systems deal with a wide range of constraints. Output saturation and lack of system model are two types of these constraints. In this paper, a Data-Driven Adaptive Predictive Control (DDAPC) is propounded for a family of unknown non-linear systems featuring output saturation. The design of the control signal only dependent on the input and output data of the system. In this regard, a new adaptive predictive control scheme is suggested using the new developed dynamic linearization model. The stability analysis of the proposed method is provided by proving the boundedness of the tracking error for both time varying and constant desired reference signal and considering the output saturation data, which is a common physical constraint in industrial systems. Furthermore, the proposed method is more robust against the model uncertainties and nonlinearities, in comparison with the common model-based adaptive methods, since its controller design procedures as well as the stability analysis are independent of plant model. To verify the efficiency and applicability of the suggested approach some applicational and numerical simulation examples are provided.
The universal paradigm shift towards green energy has accelerated the development of modern algorithms and technologies, among them converters such as Z-Source Inverters (ZSI) are playing an important role. ZSIs are single-stage inverters which are capable of performing both buck and boost operations through an impedance network that enables the shoot-through state. Despite all advantages, these inverters are associated with the non-minimum phase feature imposing heavy restrictions on their closed-loop response. Moreover, uncertainties such as parameter perturbation, unmodeled dynamics, and load disturbances may degrade their performance or even lead to instability, especially when model-based controllers are applied. To tackle these issues, a data-driven model-free adaptive controller is proposed in this paper which guarantees stability and the desired performance of the inverter in the presence of uncertainties. It performs the control action in two steps: First, a model of the system is updated using the current input and output signals of the system. Based on this updated model, the control action is re-tuned to achieve the desired performance. The convergence and stability of the proposed control system are proved in the Lyapunov sense. Experiments corroborate the effectiveness and superiority of the presented method over model-based controllers including PI, state feedback, and optimal robust linear quadratic integral controllers in terms of various metrics.
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