In most of the industrial process plants, PI/PID controllers have been widely used because of its simple design, easy tuning, and operational advantages. However, the performance of these controllers degrades for the processes with long dead-time and variation in set-point. Up next, a PPI controller is designed based on the Smith predictor to handle dead-time processes by compensation technique, but it failed to achieve adequate performance in the presence of external noise, large disturbances, and higher-order systems. Furthermore, the model-based controllers structure is complex in nature and requires the exact model of the process with more tunable parameters. Therefore, in this research, a fractional-order predictive PI controller has been proposed for dead-time processes with added filtering abilities. The controller uses the dead-time compensation characteristics of the Smith predictor and the fractional-order controller's robustness nature. For the high peak overshoot, external noise, and disturbance problems, a new set-point and noise filtering technique is proposed, and later it is compared with different conventional methods. In servo and regulatory operations, the proposed controller and filtering techniques produced optimal performance. Multiple real-time industrial process models are simulated with long dead-time to evaluate the proposed technique's flexibility, set-point tracking, disturbance rejection, signal smoothing, and dead-time compensation capabilities.

Communication in industrial wireless networks necessitates reliability and precision. Besides, the existence of interference or traffic in the network must not affect the estimated network properties. Therefore, data packets have to be sent within a certain time frame and over a reliable connection. However, the working scenarios and the characteristics of the network itself make it vulnerable to node or link faults, which impact the transmission reliability and overall performance. This article aims to introduce a developed multipath routing model, which leads to cost-effective planning, low latency and high reliability of industrial wireless mesh networks, such as the WirelessHART networks. The multipath routing model has three primary paths, and each path has a backup node. The backup node stores the data transmitted by the parent node to grant communication continuity when primary nodes fail. The multipath routing model is developed based on optimal network planning and deployment algorithm. Simulations were conducted on a WirelessHART simulator using Network Simulator (NS2). The performance of the developed model is compared with the state-of-the-art. The obtained results reveal a significant reduction in the average network latency, low power consumption, better improvement in expected network lifetime, and enhanced packet delivery ratio which improve network reliability.

The placement of wireless mesh routers is a significant matter in enhancing the performance of a wireless mesh network. Therefore, it is essential that the mesh routers are optimally placed to guarantee strong connectivity and coverage for ensuring the best network accessibility. The importance of this issue has motivated the researchers to pursue more investigation on optimizing the networks’ performance. Various optimization algorithms have been developed in the literature to identify the trade-offs between network connectivity and client coverage. This work is aimed at implementing a novel application of Harris Hawk’s Optimization (HHO) algorithm to optimally place mesh routers in a wireless mesh network so as to improve connectivity and coverage. The HHO seeks optimal routers placement that leads to maximizing the connection between routers and increasing the number of covered clients. In this study, three network configurations are used with sizes of 40, 60, and 100 mesh routers. The simulation results are statistically analyzed and compared to other population-based algorithms such as Sine Cosine Algorithm (SCA), Gray Wolf’s Optimization (GWO), and Particle Swarm Optimization (PSO). Moreover, the performance of HHO is compared with the state-of-the-art to validate the effectiveness of the proposed approach. The simulation findings and statistical analysis confirm that the HHO outperforms the other algorithms in terms of network coverage and connectivity. The outcomes demonstrate that HHO can be considered an efficacious means to enhance the performance of wireless mesh networks (WMNs).

The growing tendency of wind energy technology usage has induced researchers to seek more studies on the improvement of wind energy harvesting systems with highly reliable power injected into the electric grid. The amount of extracted wind energy by a wind turbine depends on the accuracy of the maximum power point tracking (MPPT) algorithm associated with the control scheme of the wind energy conversion system (WECS). This paper aims to present a novel development of MPPT algorithm based on parabolic prediction techniques (PPT) for variable speed wind turbines (VSWT) so as to achieve maximum wind power extraction and efficiency enhancement. The developed PPT-MPPT algorithm uses parabolic convex functions to estimate the parabola wind turbine output curve and locate the maximum power point on that curve. Simultaneously the algorithm establishes a systematic procedure for adjusting the concavity and the optimal part of the estimated parabola curve to make sure that the proposed technique is in continuous convergence. The feasibility of the developed PPT-MPPT algorithm has been validated under rapid and random changing wind speed profiles via the MATLAB/Simulink environment. The performance of the proposed PPT-MMPT algorithm is evaluated by comparing the simulation finding to those obtained using the benchmark perturb and observe(P&O) algorithms. The simulation outcomes affirm that the proposed PPT-MPPT algorithm outperformed the other algorithms in terms of tracking efficiency and extracted power which can be regarded as an efficacious manner to improve the VSWT system.

A novel hybrid Harris Hawk-Arithmetic Optimization Algorithm (HHAOA) for optimizing the Industrial Wireless Mesh Networks (WMNs) and real-time pressure process control was proposed in this research article. The proposed algorithm uses inspiration from Harris Hawk Optimization and the Arithmetic Optimization Algorithm to improve position relocation problems, premature convergence, and the poor accuracy the existing techniques face. The HHAOA algorithm was evaluated on various benchmark functions and compared with other optimization algorithms, namely Arithmetic Optimization Algorithm, Moth Flame Optimization, Sine Cosine Algorithm, Grey Wolf Optimization, and Harris Hawk Optimization. The proposed algorithm was also applied to a real-world industrial wireless mesh network simulation and experimentation on the real-time pressure process control system. All the results demonstrate that the HHAOA algorithm outperforms different algorithms regarding mean, standard deviation, convergence speed, accuracy, and robustness and improves client router connectivity and network congestion with a 31.7% reduction in Wireless Mesh Network routers. In the real-time pressure process, the HHAOA optimized Fractional-order Predictive PI (FOPPI) Controller produced a robust and smoother control signal leading to minimal peak overshoot and an average of a 53.244% faster settling. Based on the results, the algorithm enhanced the efficiency and reliability of industrial wireless networks and real-time pressure process control systems, which are critical for industrial automation and control applications.

The optimal performance of a wireless mesh network (WMN) can be greatly improved by strategically placing wireless mesh routers. As a result, it is crucial to optimally locate the WMN routers for better coverage and connectivity. Besides the optimal placement, the network congestion due to overlaying routers has to be taken into consideration. These issues have become a motivation for researchers to identify a variety of approaches to optimize WMN performance. Multiple metaheuristic algorithms have been employed for identifying the trade-offs between coverage and connectivity in WMN. Consequently, a novel hybrid Harris Hawks optimization with the sine cosine algorithm (HHOSCA) is presented in this work to tackle the aforementioned WMN optimization problems. The proposed HHOSCA seeks optimal router placement that leads to significantly increased network coverage and achieves full connectivity between the mesh routers. In addition, the proposed HHOSCA produces a cost-effective WMN by reducing the congestion in the network to the minimum number of routers whilst ensuring maximum coverage and connectivity. The superiority of the proposed HHOSCA in comparison to the other algorithm was validated by using 33 benchmark functions. It was compared against four well-known algorithms including Sine Cosine Algorithm (SCA), Harris Hawks optimization (HHO), GrayWolf Optimization (GWO), and Particle Swarm Optimization (PSO). These algorithms are statistically analyzed and compared to the simulated results of the proposed method. In addition, the performance of HHOSCA is compared to the state-of-the-art to highlight the efficacy of the proposed algorithm. The statistical analyses and simulation findings confirm that the HHOSCA outperforms the other algorithms in terms of network connectivity, coverage, network reduction, and convergence. The experimental results reveal that the proposed HHOSCA method achieves favourable optimization results compared with other relevant methods.