Bharat Verma

In this letter, we propose a novel communication system for multiple Non-Orthogonal Multiple Access (NOMA)-based end-users, leveraging a mixed THz-RF wireless communication approach. The THz link serves as the backhaul communication link and is characterized by an α − μ fading channel, path loss, absorption loss, and pointing errors. The sub-6 GHz RF carrier has been utilized for end-user connectivity. We incorporate hardware impairments (HI), channel state information (CSI) and, successive interference cancellation (SIC) errors at the receiver to capture realistic operating conditions. We have presented a mathematical framework for outage probability and average bit error rate and the asymptotic results to evaluate the proposed system’s effectiveness. The results are analyzed for various system parameters to provide further insights into system performance. Further, Monte Carlo simulations are performed to validate the analytical results.

This article presents an indirect PID tuning rule for first-order unstable time-delayed processes. The proposed method combines the principles of Internal Model Control (IMC) and the Indirect design approach, utilizing an imaginary filter to facilitate pole-zero conversion. A first-order IMC filter is employed to design the IMC controller, while the introduction of new parameters ψ allows for robustness control. The proposed approach is validated through simulation studies on two distinct first-order unstable time-delayed processes. Results demonstrate that the proposed method achieves higher phase margins, indicating improved controller performance.

The response-based design approach is a straightforward method for proportional–integral–derivative (PID) controller tuning. In this method, an open-loop process response is used to tune the PID controller for fixed robustness. In this article, new response-based PID tuning rules are proposed with adjustable robustness and response. Instead of three PID variables, the proposed PID tuning method allows the user to adjust the process response with a single variable [ ]. The range of constant [ ] is provided for ensuring the stability of the process. Three MATLAB/SIMULINK examples and one temperature controller experiment are used to validate the proposed controller performance. The simulation result is also compared with three well-known response-based PID tuning rules Z-N, Cohen-Coon, Astrom et al., and with a model-based PID tuning proposed by Wang et al. Moreover, the proposed tuning provides lesser IAE, ITAE, ISE, and ITSE values than the existing methods.

Input-output pairing effectively minimizes the loop interaction in MIMO processes and avoids integral instability. In this article, a modified Relative Normalized Gain Array is proposed for the Input-Output Pairing and design of the First-order and Second-order Equivalent Transfer function. The proposed modified Relative Gain Array (mRNGA) matrix is derived by Normalizing the Relative Gain Array matrix with a normalization factor, which is calculated by minimizing the Integral Square Error of the given MIMO process. After that, the first order and second order Equivalent Transfer function is design by the derived mRNGA matrix, which offers an improved estimation of the Effective open-loop transfer function of the given MIMO process. The proposed method is corroborated with the help of the Input-Output pairing examples up to the 5 × 5 MIMO processes and three ETF design example.

The Proportional-Integral-Derivative (PID) controller is a widely used controller in automation industries. Several advanced PID tuning/design methods, such as response-based design, Internal Model Control and controller optimization by stochastic algorithms, have been proposed in the literature. However, regardless of the advantages and accuracy of developed tuning methods, due to the process modeling error and parametric uncertainties, the experimental response always differs from the theoretical response, which required online fine-tuning of the PID parameter. Manual adjustment of three PID parameters disturb the robustness of the controller from its desired value; also, it does not guarantee the stability of the process. Therefore, this work focuses on online PID controller tuning with the guaranteed robustness of the controller. A new single variable tuning method is developed for the online robustness and performance adjustment. Moreover, the proposed rules only depend upon the previously optimized PID parameters. The proposed method is an additional feature to all existing PID tuning methods, including optimal PID controller with stochastic optimization algorithms. The proposed method is validated with the help of optimal PID controller design by existing optimal tuning rules, optimized PID with Particle Swarm Optimization and experimental validation on Canonical Tank System.

The internal model controller‐proportional–integral–derivative (IMC‐PID) is one of the simplest model based proportional–integral–derivative (PID) design methods available in the literature. In IMC‐PID design, the three variable PID design problem is converted into the single variable design problem with the help of the internal model of the process and an internal model controller (IMC) filter. The IMC filter is used to control the robustness of the process and transient performance. The IMC filter, however, introduces additional phase lag in the process, due to which the stability of the process is compromised to maintain the transient response. In this study, a new indirect design approach of IMC‐PID controller is proposed for first‐order filter with dead time (FOPDT) and second‐order filter with dead time (SOPDT) processes, in which the IMC‐PID controller is designed for the pole‐zero shifted process instead of original process and the process robustness is tuned with the help of the proposed shifting constant . The proposed design does not involve any IMC filter, which helps to achieve a better IAE and higher Phase Margin at desired Maximum Sensitivity. The proposed method is validated with the help of real‐time experiment on water level control of Canonical Tank System and MATLAB simulation examples of the FOPDT and SOPDT processes.