Rania. R. Darwish

This paper proposes a hybrid adaptive Single Neuron (SN) controller integrated with an optimal feedback compensator and enhanced by fuzzy logic. Conventional controllers often lack robustness against parameter uncertainties and external disturbances; therefore, this study introduces a compensator designed to balance the trade-off between fast adaptation and system stability. A Genetic Algorithm (GA) is utilized to optimize the parameters of both the controller and the compensator, ensuring effective online tuning. The proposed structure is designed for dynamic systems subject to parameter uncertainties, load variations, and nonlinearities. The performance of the proposed approach is validated through two case studies: Load Frequency Control (LFC) of a single-area power system and speed control of a Brushless DC (BLDC) motor. Simulation results for the LFC system indicate a 73.33% reduction in undershoot and a 40.35% reduction in settling time compared to a Conventional SN (CSN). Furthermore, experimental validation on the BLDC motor demonstrates significant superiority over Self-Tuning Fuzzy PID (STFPID), CSN, and GA-based PID controllers, reducing settling time by up to 87.4% and rise time by up to 87%. The results confirm that the proposed Fuzzy Variable-Gain SN-OFC offers superior oscillation damping and rapid adaptation in uncertain dynamic environments.

Achieving a balance between rapid adaptation and robustness is a critical yet challenging objective in the design of industrial control systems. Model Reference Adaptive Control (MRAC) is a standard approach for managing system uncertainties; however, it suffers from a fundamental trade-off between adaptation speed and robustness. The high adaptation gains required for fast tracking often lead to parameter bursting or instability in the presence of noise. To resolve this issue, this paper proposes a new Dual-Loop Optimal Feedback Control (OFC) architecture applied to a Brushless DC (BLDC) motor drive. Unlike conventional methods that rely solely on tuning the adaptive mechanism, the proposed architecture introduces a parallel compensation loop designed to decouple disturbance rejection from reference tracking. This structure utilizes a Genetic Algorithm (GA) as an offline optimization engine to identify the Optimal Compensator gains that balance transient recovery with steady-state stability. Experimental validation demonstrates that the proposed Dual-Loop OFC architecture significantly outperforms traditional approaches. Specifically, it achieves an 88.99% reduction in overshoot and a 13.8% reduction in settling time compared to Conventional MRAC (CMRAC). Furthermore, it exhibits an 86.7% faster rise time compared to Self-Tuning Fuzzy PID (STFPID). These results confirm that the proposed Dual-Loop structure effectively mitigates the classic adaptability–robustness trade-off, offering a stable and high-performance solution for industrial actuators under varying operating conditions.

The conventional methods of observer poles placement in sensor fault detection usually adopt the trial-and-error methods. These methods cannot achieve global optimal performance because of their fixed poles placement and it leads to an observer with constant parameters, which could be reducing the system performance. Therefore, this paper proposes a fuzzy-based observer tuning method to optimize and adapt the selection of poles locations to determine the optimal gains of the observer, and it is experimentally applied to a composite sensor fault detection. Fuzzy logic is a promising method that could overcome the trial-and-error method challenges by introducing better adaptation and system robustness. The proposed observer structure includes adaptive tuning corresponding to an unknown input. Utilizing self-tuning for the observer correction stage, the gain is going to be updated online using the proposed fuzzy adaptive poles placement (FAPP) system. This paper validated the system simulation by implementing fault detection algorithms by using a real-time embedded observer-based system. The experimental results demonstrate the effectiveness of the proposed fuzzy-based observer schemes at detecting sensor faults in the Brushless DC (BLDC) motors, with significantly better performance than conventional counterparts’ methodologies. The experiments indicate that the average estimation error is 0.146, which less by 43.8% than was obtained for high levels of noise and disturbances compared with the traditional Luenberger observer approach.

In this paper, an adaptive high-accurate observer-based fault detection approach for industrial applications is proposed. The proposed fault detection algorithm employs a fuzzy logic-based approach with the objective of finding the appropriate observer gains that could cope with the different working conditions. The flexibility and adaptability represent the main objectives of the proposed observer. This work is interested in proposing an observer with fuzzy variable gains for a general nonlinear system. Furthermore, a linear model has been built to facilitate the accomplishment of the fault detection of the industrial servo system by using the proposed observer. In order to evaluate the proposed approach, eleven realistic sensor fault scenarios are created under varying conditions: fault parameters (e.g., multiple fault profiles, location, and magnitudes), unknown inputs (e.g., disturbers and sensor noises) for performance testing. Also, a scoring algorithm has been implemented, to evaluate the classification ability of the algorithm and the early fault detection ability. The experimental results demonstrate the effectiveness of the proposed observer approach in detecting sensor faults in the industrial servo systems, with 88.8% classification accuracy. Furthermore, the obtained results confirm the proposed algorithm superiority when compared to classical Luenberger observer with constant gains.

This paper introduces a decision-driven architecture for the information-centric Internet-of-Things (IoT) scenarios. The hallmark of this work is the conjugate of the awareness about the congestion situations along with the quality of the decisions’ supporting information. Controlling the incoming decisions’ upsurge is accomplished on a fashion that respects their degree of emergency. Wherefore, the Active Weighted Random Early Detection (AWRED) algorithm is integrated into the autonomic admission module to underpin the congestion control concept. Afterwards, an adaptive information-centric scheduling module is proposed in order to devise an optimized information-aware execution schedule for the admitted real-time decisions. The proposed scheduling algorithm is augmented with a Global best Harmony search (GHS) based amending approach. This integrated scheduling concept empowers the proposed algorithm from tuning the schedules of the admitted real-time decisions such that the completeness opportunities of decisions are elevated without sacrificing the rest architecture objectives. Simulations results demonstrate the robustness of the proposed architecture performance against various congestion degrees, an essential feature in a highly dynamic environment as the IoT. Furthermore, it has been proved that the proposed architecture significantly surpasses state-of-art approaches in terms of both quality of information (QoI) and quality of service (QoS) obligations especially during congested situations.

Intrinsically, cloud computing is the paradigm that is been envisioned to dynamically furnish endless computing services to pervasive worldwide clients. However, the massive energy consumption resulted from the underpinning data centers would drastically hinder the full potential from the dreamed paradigm. Accordingly, this work opts towards devising green proactive cloud resources orchestration architecture. Specifically, this work focuses on cloud-based web applications which considered one of the most paramount cloud service categories. The proposed framework hence geared for leveraging cloud system resources utilization, ensuring explicit guarantees on negotiated web services’ obligations, meanwhile achieving power consumption minimization objectives. The proposed framework consolidates heuristic methodologies along with control theory approaches in a green resources orchestration hierarchical framework. Firstly, an energy-aware global controller is presented. The proposed global controller employs an online graph coloring-based approach for mapping the submitted virtual machines (VMs) to the appropriate cloud resources. The proposed global controller seeks energy-efficient configurations for the submitted VMs in a constrained environment without obligating the negotiated services SLAs. Secondly, a proactive fuzzy-logic-based local controller is proposed. The proposed local controller aimed at in confronting workloads’ sustainable fluctuations via proactive amendment for the placement and provisioning schedules. Additionally, the proposed local controller oriented towards maintaining active power management policy, especially during transient peak of usage, thereby mitigating overall costs, and extending resources capacity and performance capabilities. Conducted simulations and comparisons highly demonstrate the effectiveness of the proposed architecture at outperforming counterpart approaches specifically in terms of scalability, total energy consumption, and maintaining the web applications SLAs objectives in spite of workload fluctuations.