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Actuator faults are inevitable in small reverse osmosis desalination plants. It may cause energy losses and reduce the quality of the freshwater, which may endanger human life. This paper focuses on the integrated fault detection and fault-tolerant control approach. The primary motivation of this paper is to propose a novel integrated fault detection and fault-tolerant control approach. The actuator fault is estimated using the concept of parity space approach. Then the system model is updated in the fault-tolerant control block using the information of the estimated fault parameter. Moreover, the proposed approach uses the receding-horizon predictive control-bounded data uncertainties controller, which is the robust and stable variant of generalized predictive control. The remaining uncertainty caused by the model and observer is compensated by this controller. The structure of a small reverse osmosis desalination plant is deployed. In this plant, the permeate flow rate and conductivity are controlled by a retentate valve and a bypass valve, which add a small amount of inlet to the outlet. The performances of three predictive model controllers are evaluated, and a comparison is made between their computational costs, stability, and robustness. The plant is considered to be linear time-invariant and subject to model uncertainties, measurement noise, and actuator fault in the retentate valve as efficiency dropping. The results reveal the robustness of the proposed approach concerning noise and matched uncertainties as well as its accommodation to actuator fault up to 90%.

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Surge and constant pressure are some of the most critical issues in compressor control. In this paper, the problem of the surge and constant pressure in the presence of environmental disturbances is solved. Proposed design for control system based on proportional-integral controllers, adaptive neuro-fuzzy inference system (FIS), and particle swarm optimized neural fuzzy and for modeling neural network strategy fuzzy nonlinear automatic regression with external input is used. Based on this, for modeling, practical and real data are extracted from the K-250 compressor of Isfahan Steel Company. In the adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO)