This article presents a novel prescribed-time fuzzy adaptive consensus control scheme for nonlinear photovoltaic (PV) energy systems with dead-zone and actuator faults. The PV panels are controlled to track the maximum power point so that the considered systems can efficiently operate under different complex conditions. To quickly regulate the voltage to a desired reference, a novel prescribed-time performance function is presented to guarantee systems states convergence within a specified time. Besides, dead zones and faults are serious nonlinearity constraints in practical grid-connected PV systems. Thus, a finite-time tracking controller and the adaptive laws are designed to compensate for the effect of unknown nonlinear constraints. By applying fuzzy logic systems, the difficulty of dealing with unknown nonlinear dynamics is overcome. The stability of the closed-loop system is rigorously proven using Lyapunov stability theory, demonstrating the uniformly ultimately boundedness of all signals. Finally, extensive simulation experiments validate the effectiveness and superiority of the proposed approach.

This manuscript presents an adaptive funnel controller applying to the active magnetic bearing for position constraint control with prescriptive tracking performance. To better depict the actual active magnetic bearing dynamics, a nonlinear active magnetic bearing model with switched parameters instead of the existing fixed parameters is constructed considering the inherent properties of uncertainties and nonstationarities of active magnetic bearings. Then, by designing the funnel control scheme and adaptive laws based on Lyapunov stability theory and backstepping technique, the rotor displacement is constrained to not exceed the prescribed funnel boundary in view of safety concerns. Moreover, the funnel boundary integrated into the proposed controller is in line with the rotor displacement characteristics of active magnetic bearing systems under different speeds during real operation process. The boundness of the position tracking error is confirmed via Lyapunov synthesis. Eventually, the effectiveness of the proposed controller is verified by simulated and experimental examples.Note to Practitioners—The motivation of this manuscript is to investigate a control strategy with potential application for the displacement control of active magnetic bearings. In most of the existing displacement control schemes for active magnetic bearings, the constraint and limitation of displacement is not taken into account. However, considering the high-speed rotation and active controllability of active magnetic bearings, it is necessary to preset the performance indicators of rotor displacement including convergence speed, overshoot, and constraint limit, which can not only avoid serious safety accidents, but also give full play to the active control advantages of active magnetic bearings in different actual situations. Therefore, this manuscript suggests an adaptive funnel control strategy for prescriptive performance. Moreover, this manuscript solves the modeling and control problems for active magnetic bearings subject to nonlinearity, uncertainty, and external disturbances, which are difficult to be tackled in practical applications. The stability and convergence of the proposed controller are analyzed mathematically, and the practical experimental results reveal that the proposed controller has the potential and possibility to be applied in practice.

An extend-policy iterative algorithm is proposed for solving the ecological evolving-lung cancer cells growth inhibition optimal drug delivery scheme. With the analysis of the cell proliferation-apoptosis process of lung cancer cells with primitive immune system and external drug interventions, such as chemotherapeutic drugs and immunological agents, a model of ecological containment of lung cancer cells mimicking injection labeling is constructed. The HJB equation for biological tissue damage has also been established by considering the concentration of lung cancer cells in the blood and the amount of drug administered. The final simulation experiment proved the effectiveness of the drug delivery scheme.

This article investigates the event-driven fault-tolerance (ETFT) consensus problem for general linear multiagent systems (MASs) with partial loss of effectiveness (PLOE) and bias faults of actuators in leader-follower networks. Each agent's controller is only updated relatively infrequently at its event moments. A desirable feature of this article is that the proposed event-driven algorithm is fully distributed also independent of global information and additive fault boundaries. Based on this, a consensus error prediction model is used to avoid the limitation that each agent needs to monitor its neighbors' state under event-driven conditions continuously. We further excluded the Zeno behavior by proving that any adjacent event interval for each agent is greater than zero. The simulations verify our results.

This work seeks to advance the state of the art in HPC I/O performance analysis and interpretation. In particular, we demonstrate effective techniques to: (1) model output performance in the presence of I/O interference from production loads; (2) build features from write patterns and key parameters of the system architecture and configurations; (3) employ suitable machine learning algorithms to improve model accuracy. We train models with five popular regression algorithms and conduct experiments on two distinct production HPC platforms. We find that the lasso and random forest models predict output performance with high accuracy on both of the target systems. We also explore use of the models to guide adaptation in I/O middleware systems, and show potential for improvements of at least 15% from model-guided adaptation on 70% of samples, and improvements up to 10 x on some samples for both of the target systems.

Fair representations are a powerful tool for establishing criteria like statistical parity, proxy non-discrimination, and equality of opportunity in learned models. Existing techniques for learning these representations are typically model-agnostic, as they preprocess the original data such that the output satisfies some fairness criterion, and can be used with arbitrary learning methods. In contrast, we demonstrate the promise of learning a model-aware fair representation, focusing on kernel-based models. We leverage the classical Sufficient Dimension Reduction (SDR) framework to construct representations as subspaces of the reproducing kernel Hilbert space (RKHS), whose member functions are guaranteed to satisfy fairness. Our method supports several fairness criteria, continuous and discrete data, and multiple protected attributes. We further show how to calibrate the accuracy tradeoff by characterizing it in terms of the principal angles between subspaces of the RKHS. Finally, we apply our approach to obtain the first Fair Gaussian Process (FGP) prior for fair Bayesian learning, and show that it is competitive with, and in some cases outperforms, state-of-the-art methods on real data.

We present an efficient algorithm for learning mixed membership models when the number of variables p is much larger than the number of hidden components k. This algorithm reduces the computational complexity of state-of-the-art tensor methods, which require decomposing an $O(p^3)$ tensor, to factorizing $O(p/k)$ sub-tensors each of size $O(k^3)$. In addition, we address the issue of negative entries in the empirical method of moments based estimators. We provide sufficient conditions under which our approach has provable guarantees. Our approach obtains competitive empirical results on both simulated and real data.

Multi-tenant database systems have a component called the Resource Manager, or RM that is responsible for allocating resources to tenants. RMs today do not provide direct support for performance objectives such as: "Average job response time of tenant A must be less than two minutes", or "No more than 5% of tenant B's jobs can miss the deadline of 1 hour." Thus, DBAs have to tinker with the RM's low-level configuration settings to meet such objectives. We propose a framework called Tempo that brings simplicity , self-tuning , and robustness to existing RMs. Tempo provides a simple interface for DBAs to specify performance objectives declaratively, and optimizes the RM configuration settings to meet these objectives. Tempo has a solid theoretical foundation which gives key robustness guarantees. We report experiments done on Tempo using production traces of data-processing workloads from companies such as Facebook and Cloudera. These experiments demonstrate significant improvements in meeting desired performance objectives over RM configuration settings specified by human experts.