Nowadays, induction machines (IMs) are widely used in industrial and transportation applications (electric or hybrid ground vehicle or aerospace actuators) thanks to their significant advantages in comparison to other technologies. Indeed, there is a large demand for IMs because of their reliability, robustness, and cost-effectiveness. The objective of this paper is to improve the reliability and performance of the three-phase induction machine in case of mechanical sensor failure. Moreover, this paper will discuss the development and proposal of a fault-tolerant controller (FTC), based on the combination of a vector controller, two virtual sensors (an extended Kalman filter, or EKF, and a sliding mode observer, or SMO) and a neural voting algorithm. In this approach, the vector controller is based on a new structure of a back-stepping sliding mode controller, which incorporates a double integral sliding surface to improve the performance of the induction machine in faulty operation mode. More specifically, this controller improves the machine performance in terms of having a fast response, fewer steady-state errors, and a robust performance in the existence of uncertainty. In addition, two voting algorithms are suggested in this approach. The first is based on neural networks, which are insensitive to parameter variations and do not need to set a threshold. The second one is based on fuzzy logic. Finally, validation is carried out by simulations in healthy and faulty operation modes to prove the feasibility of the proposed FTC.

This paper suggests a new dynamic sliding mode controller incorporating a disturbance observer for controlling integrating processes with time delay. Here, a new reaching law has been introduced to expedite the system response without evoking chattering. The convergence of this law is also studied. Additionally, the whole system stability is proved using the Lyapunov stability theory. This paper also proposes a new disturbance observer to improve the disturbance rejection and to help suppress chattering further. The suggested observer has only one parameter to be designed. Therefore, this observer can easily be implemented. Several simulation examples and a real-time application are considered to prove the superiority of the suggested controller over the reported controllers in the literature.

The proportional–integral and proportional–derivative controller is characterized by its capability to effectively control integrating processes compared with proportional–integral/proportional–integral–derivative controllers. Recently, several graphical methods have been proposed for tuning the PI-PD controller parameters by computing the centroid point of the stability boundary locus. However, these approaches are time-consuming because they entail plotting the stability boundary locus for finding the centroid point. Another disadvantage of those design methods is that the design procedure has to be redone as the transfer function changes. In this article, a generalized stability boundary locus is constructed in terms of the controller and assumed plant transfer function model parameters to enable the designer to avoid replotting the stability boundary locus as the process transfer function changes. More importantly, two analytical approaches, which simplify the design of the proportional–integral and proportional–derivative controller very much, are proposed to compute the centroid point of the generalized stability boundary locus. Analytical expressions for computing the performance measures of the designed closed-loop system have also been provided so that one can predict the performance of the designed closed-loop system. It has been shown by simulation examples that the analytical centroid of convex stability region method provides quicker responses with faster disturbance rejections compared to other reported design methods. Also, the simulation results have displayed that analytical weighted geometrical center gives more robust closed-loop responses in terms of gain margin, phase margins, and maximum sensitivity than analytical centroid of convex stability region. Finally, the feasibility of the proposed methods is tested using a real-time application based on an aerodynamical system. Real-time results have demonstrated that the analytical centroid of convex stability region and analytical weighted geometrical center methods give quicker responses with small overshoots compared to other reported methods.

PI-PD controllers have superior performance compared to traditional PID controllers, especially for controlling unstable and integrating industrial processes with time delays. However, computing the four tuning parameters of this type of controller is not an easy task. Recently, there has been significant interest in determining the tuning rules for PI-PD controllers that utilize the stability region. Currently, most tuning rules for the PI-PD controller are presented graphically, which can be time-consuming and act as a barrier to their industrial application. There is a lack of analytical tuning guidelines in the literature to address this shortfall. However, the existing analytical tuning guidelines do not consider a rigorous design approach. This work proposes new robust analytical tuning criteria based on predefined gain and phase margin bounds, as well as the centroid of the stability region. The proposed method has been tested using various simulation studies related to a DC–DC buck converter, a DC motor, and a heat exchanger. The results indicate that the proposed tuning rules exhibit strong performance against parameter uncertainty with minimal overshoots. Furthermore, the suggested technique for simultaneous control of yaw and pitch angles has been tested in a real-time application using the twin rotor multi-input multi-output system (TRMS). Real-time results indicate that, compared to other methods under investigation, the suggested approach provides nearly minimal overshoots.