Internal model control (IMC) is a well-known model-based control structure that has additional dead-time compensation (DTC), while PID is the popularly-implemented control structure due to its simple structure, ease of implementation, and satisfactory performance at a wide range of operating conditions. Therefore, IMC-based PID tuning methods are introduced to include the benefits of IMC in PID. Approximations involved in the existing IMC-based PID tuning methods for specific types of transfer function models result in performance degradation. The IMC structure can be rearranged to the form of a standard PID type controller without approximation, which retains good servo performance for time delay processes. On the other hand, the presence of a time delay element in the controller structure makes the loop highly sensitive to dead-time variations. Thus, the sensitivity of the IMC scheme realized in the PID structure is studied based on the conventional Nyquist stability criterion. It is observed that the high sensitivity is due to the occurrence of multiple interaction points of the Nyquist curve of the loop transfer function and unit circle. Besides, the number of the interaction points is relative to the ratio of loop dead-time to closed loop time constant. Further, it is shown that the occurrence of multiple interaction points can be avoided by the right choice of closed loop time constant. The performance and robustness of the proposed design approach is confirmed via simulation analysis.

This article draws its motivation from classical model-based controller design methods, such as Direct Synthesis (DS) and Internal Model Control (IMC) methods, and develops a model-based PI-like control scheme for stable systems. The proposed control scheme has been realized in a reset configuration. Furthermore, the control scheme has a tuning parameter, and its control action resembles a PI-like controller. The proposed scheme shows improved performance as compared to that of IMC, Skogestad-IMC-PI, and Skogestad-IMC-PID control schemes. In addition, an automated approach to designing this scheme is proposed. The efficacy of the proposed control scheme is demonstrated via experimental evaluation on a computer-interfaced pilot-scale two-tank system.

Element bearings are widespread significant parts of rotary machinery in industries and their condition monitoring through fault detection and isolation methods are of superior interests to engineers. Condition monitoring could assist in attaining effective system maintenance and the automation processes. Nonetheless, despite the availability of diverse bearing fault detection methods, there is paucity of literature concerning the theoretical modelling of the bearing faults using mathematical representations. The principal goal of this communication is to build up an approach to predict ball bearing fault, including ball fault, outer race fault and inner race fault. The proposed method uses the vibration data collected from the rotating ball bearing and predicts the type of fault. The proposed method is based on wavelet decomposition method, PCA, ANFIS and SVM. The vibration data is decomposed into 3rd level wavelet components using wavelet packet decomposition; each wavelet component has been divided into several energy segments, these 3rd level wavelet components are reduced to lower level components using Principal Component Analysis (PCA). ANFIS algorithm is trained using the reduced feature set as input and the type of fault as output. The trained ANFIS algorithm is validated using another set of data. Support Vector Machine (SVM) has been used instead of ANFIS algorithm, the SVM based method gives more accurate prediction compared to that of ANFIS. The proposed method has been validated using the real-time data collected from the ball bearing setup. The validation results show that the proposed method exactly predicts the type of ball bearing fault. The real-time data validation results confirm the accuracy of the fault prediction method.

The multivariable control scheme is a widely used advanced process control methodology to control key process variables in chemical engineering processes. However, successful implementation of multivariable control requires a simplified control structure, a lower number of tuning parameters, and an appropriate tuning method. In this work, a model based multivariable control scheme is realized in reset configuration for the control of stable multi‐input and multi‐output systems. This control scheme utilizes the process transfer function matrix and the inverse of the steady state gain matrix for its implementation. The control scheme has a single tuning parameter and also inherently takes into account the interaction that exists between process inputs and outputs. This scheme is easy to implement and apply in multivariable systems with multiple delays, high‐dimensional systems, non‐square systems, and systems with RHP zeros. Simulation examples from process system engineering such as industrial scale polymerization reactor system, quadruple‐tank system, temperature control for the four room process, and shell control problem are presented to show the efficacy of the proposed multivariable control scheme.

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