Current-mode control is a commonly utilized control strategy for the step-up power converters because these converters' control-to-output transfer function contains right-half plane zeros. The main concern associated with the recent dual-loop current-mode controller (CMC) is that the integrator operates on the error term itself. Thus, integrand can assume extremely large values when error is large such as during transient response, and the controller output may saturate, especially when sufficiently large controller gains are used. If lower gain values of gains are used, the speed of response in the presence of small parameter variations could be much lower. Thus, there is a compromise between the transient response when error signal is large and speed of the response for small error signals. To address these concerns, an improved normalized-error based current-mode controller (NECC) is employed for voltage regulation in a sixth-order boost configuration. This controller's main characteristic is that the integrator now operates on a bounded integrand which is a normalized-error. This avoids the integrator saturation and also increase the room for tuning the controller gains. The state-space averaged model of the topology is given and a detailed stability analysis is shown. The main contribution of the paper is that a detailed comparative study of the traditional CMC and an improved NECC based on some simulation and experimental waveforms is provided. Both simulation and experimental outcomes clearly prove the superiority of the proposed NECC in terms of an improved speed and less overshoot of the output voltage response.

Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manufacturing environments, enabling scalable and flexible access to remote data centers over the internet. In these environments, Virtual Machines (VMs) are employed to manage workloads, with their optimal placement on Physical Machines (PMs) being crucial for maximizing resource utilization. However, achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives, particularly in scenarios involving inter-VM communication dependencies, which are common in smart manufacturing applications. This manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, enhanced with improved mutation and crossover operators, to efficiently place VMs. This approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource utilization. The proposed algorithm is benchmarked against other multi-objective algorithms, such as Multi-Objective Evolutionary Algorithm with Decomposition (MOEA/D), demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing