The Shewhart np CONTROL chart is often used to monitor the quantity of nonconforming, but it is slow in detecting small deviations. This paper proposes an efficient approach to monitor the quantity of nonconforming. The novelty of the paper is utilization of an initial belief to construct an analytic VARIABLE limit np CONTROL chart. The proposed method uses all gathered data, sequentially. This approach is significantly faster than some existent effective approaches in detecting small deviations. These charts are mainly used for evaluation of the initial setup in the process. The simulated results for the average run  length profiles demonstrate the superiority of the new approach against the standard np chart, binomial CUSUM, binomial EWMA and moving average approach.

This work is trying to introduce a fractional order floated pole CONTROLler as a fast and robust approach. We designed a robust VARIABLE structure CONTROL that yields a continuous and constrained CONTROL signal, also a fast response in the presence of model uncertainties and external disturbances. In the proposed CONTROLler, we employed the pole placement algorithm, then by designing proper polynomials gave it robust property, then due to a simple optimization routine, we make it fast and faster within the stability region. Finally, to evaluate the proposed method, numerical examples in different situations of the presence of noise, disturbance, and model uncertainties, also comparative results are presented. This paper proposed an accurate, fast, and robust CONTROLler. This can improve the performance of the perturbed functional systems used in the industrial fields. It is proposed to spread the benefit of fractional calculus in the CONTROL of complex systems in practical situations.

Under a constant loading condition, use of a CONTROLler with constant coefficients can be acceptable for servo pneumatic systems. However, in VARIABLE loads with widespread changes, more advanced CONTROL methods should be considered to achieve desirable performance. In this paper, an adaptive CONTROLler is designed and implemented to a variably loaded servo pneumatic system with PWM driven switching valve. In the experimental setup, a pneumatic circuit is used which consists of one PWM driven fast switching valve instead of an expensive servo or proportional valve. In the designed adaptive CONTROLler, real time identification of system parameters is performed using input-output data and CONTROLler parameters are adjusted instantaneously. “Self-tuning regulators” algorithm in which the desired closed loop poles and zeroes are predefined, is applied to design the proposed CONTROLler. The designed CONTROLler is applied to the pneumatic actuator via an interface board and its results are compared to results of a PD and a multi model CONTROLler. Unlike the proposed method which varies the CONTROL parameters continuously according to load variations, in multi-model method the CONTROL law is selected among a number of fixed CONTROLlers. Experimental results demonstrate the high performance of the adaptive CONTROLler under VARIABLE loads.

In this paper the robust CONTROL of multi VARIABLE singular perturbation systems is studied. In order to robust the CONTROL of the system, only the additive modeling error is considered. Based on the above bounds of modeling error and the Nyquist criteria and in order to improve the performance of the system a relation is found to determine the robust CONTROLler. The singular perturbation system is decomposed into fast and slow subsystems and the robust CONTROLler is designed for reduced order systems. It is explained how to find the new bound to stabilize the system and decrease the sensitivity.

This paper addresses the design of CONTROL charts for both VARIABLE (x chart) and attribute (u and c charts) quality characteristics, when there is uncertainty about the process parameters or samples data. Derived CONTROL charts are more flexible than the strict crisp case, due to the ability of encompassing the effects of vagueness in form of the degree of expert's presumption. We extend the use of proposed fuzzy CONTROL charts in case of linguistic data using a developed defuzzifier index, which is based on the metric distance between fuzzy sets.

The CCC-r chart is developed based on cumulative count of a conforming (CCC) CONTROL chart that considers the cumulative number of items inspected until observing r nonconforming ones. Typically, the samples obtained from the process are analyzed through 100% inspection to exploit the CCC-r chart. However, considering the inspection cost and time would limit its implementation. In this paper, we investigate the performance of CCC-r chart with VARIABLE sampling interval (CCC-rVSI chart). The efficiency of CCC-rVSI chart is compared with fixed sampling interval (FSI) scheme of CCC-r chart (CCC-rFSI chart) and CCCVSI chart. The comparison results show that CCC-rVSI chart is more efficient than the CCCVSI chart in reducing the average time to signal (ATS) and also CCC-rVSI chart performs better than CCC-rFSI chart. In addition, some sensitivity analyses are performed to illustrate the effect of the input parameters on the performance of CCC-rVSI chart.