Showing papers in "Control Engineering Practice in 2018"

TL;DR: In this article, the authors proposed a parallel PCA-KPCA (P-PCA-kPCA) model and monitoring scheme that incorporates randomized algorithm (RA) and genetic algorithm (GA) for fault detection for a process with linearly correlated and nonlinearly related variables.

TL;DR: Simulation and experiment results show that the proposed control strategy can robustly track the reference path and at the same time maintains the yaw stability of vehicle at or near the physical limits of tyre friction.

TL;DR: The cascaded integration of Incremental Nonlinear Dynamic Inversion (INDI) for attitude control and INDI for position control of micro air vehicles and the effect of using a linearization to calculate thrust vector increments is investigated.

TL;DR: This work applies the Equivalent Consumption Minimisation Strategy (ECMS) to a ship powered by a hybrid propulsion plant with hybrid power supply that can be recharged with renewable shore power, inspired by automotive research in Energy Management Strategies.

TL;DR: The paper illustrates the performance of the autonomous guidance system in a virtual reality environment (SYNTHIA) and in a real scenario achieving the proposed goal: to move autonomously from a starting point to a final point in a comfortable way.

TL;DR: The main novelty of the proposed Collaborative-Coverage Path Planning (C-CPP) stems from the establishment of a theoretical framework capable of providing a path for accomplishing a full coverage of the infrastructure, without any further simplifications.

TL;DR: The proposed solution consists of the design of a suitable manifold on which voltage regulation is achieved even in presence of unknown load demand and modeling uncertainties, which has been theoretically analyzed and validated through experiments on a real DC microgrid.

TL;DR: A robust adaptive integral terminal sliding mode control strategy is proposed to deal with unknown but bounded dynamic uncertainties of a nonlinear system and no prior knowledge of the exact dynamic model and upper bounds of uncertainties is required.

TL;DR: The results show that the proposed controller guarantees the performance of the conventional SOSMC under external disturbance and parametric uncertainty with less chattering.

TL;DR: The proposed DP control law achieves the global asymptotic regulation of positioning errors, while guaranteeing the global uniform ultimate boundedness of all signals in the DP closed-loop control system.

TL;DR: A robust/adaptive perturbation observer based fractional-order sliding-mode controller for a photovoltaic inverter connected to the power grid, in which a maximum power point tracking technique is achieved to harvest the available maximum solar energy from the PV arrays in the presence of various atmospheric conditions.

TL;DR: The active disturbance rejection control (ADRC) is applied to the steering controller design in this paper and the stability analysis based on the concept of Lyapunov exponents shows that the ADRC control system is exponentially stable around the equilibrium point.

TL;DR: A novel approach for energy-optimal adaptive cruise control (ACC) combining model predictive control (MPC) and dynamic programming (DP) is presented and leads to a significant reduction of the energy consumption compared to the preceding vehicle on the same route.

TL;DR: The proposed decentralized strategy is applied to a guidepath network by means of a simulation software and guarantees the avoidance of deadlocks and collisions.

TL;DR: A belief rule-based evidence updating method for industrial alarm system design, concentrating on handling uncertainties of process variable, which has better comprehensive performance than some typical probability-based methods, binary classifiers, and the original evidence updating methods.

TL;DR: Combining insights from of the extended state observer and robot dynamics, an efficient collision detection method based on only proprioceptive sensors (encoders and torque sensors) is introduced and provides magnitude and direction information of force signals covering a general class of actuator faults.

TL;DR: Adaptive models—moving windows (MW), time difference (TD), and locally weighted regression (LWR) under the framework of Bayesian network (BN) are proposed and shows great superiorities over other traditional methods, especially in dealing with missing data and the ability of learning causality.

TL;DR: A dynamic process monitoring method based on canonical variable analysis (CVA) and long short-term memory (LSTM) that can effectively detect process abnormalities and perform multi-step-ahead prediction of the system's behavior after the appearance of a fault is proposed.

TL;DR: A new framework for root-cause fault diagnosis applicable for multivariate nonlinear processes is proposed, which can reduce the necessary number of calculation for causality analysis among time-series and a new and fast symbolic dynamic-based normalized transfer entropy (SDNTE) technique is proposed to enable real-time application of transfer entropy.

TL;DR: Modeling and control of a multi-medium unmanned vehicle capable of seamless operation in air or underwater is introduced and some preliminary experimental results are introduced, showing the vehicle performing in air, underwater and through the transition.

TL;DR: This study approaches the design of an energy-maximising optimal controller for a single WEC device by employing the moment-based phasor transform, describing a pioneering application of this novel moment-matching mathematical scheme to an optimal control problem.

TL;DR: In this paper, the state-of-the-art in lithium-ion battery degradation models, including accuracy, computational complexity, and amenability to control algorithm development, are evaluated.

TL;DR: In silico and in vivo results without feedforward insulin boluses delivered at meal times indicate that safe blood glucose control can be achieved by the proposed controller.

TL;DR: A hybrid diagnosis system design which combines model-based residuals with incremental anomaly classifiers is proposed which is able to identify unknown faults and also classify multiple-faults using only single-fault training data.

TL;DR: This is the first time a visual servoing algorithm for automated manipulation has been developed and verified, through subsea trials, on a commercial work-class ROV with industry standard hydraulic manipulators.

TL;DR: The results show that the performance of the state-of-the-art industrial autotuners can be significantly improved.

TL;DR: The experimental results illustrate that the proposed feedforward–feedback control scheme can be used in micro-positioning motion control applications to enhance the tracking performance of the piezoelectric cantilever actuator under different operating conditions.

TL;DR: Experimental results are presented to validate the effectiveness of the proposed observers in real conditions and to analyze the performance of the estimators, particularly with respect to the possibility of estimating the disturbances and unknown dynamics acting on the system.

TL;DR: This paper deals with the energy consumption management problem in buildings by modeling and controlling the main electric appliances and designing a simulator that models the load energy consumptions and helps to recognize how they contribute to peak demand.

TL;DR: Three types of deep belief network (DBN)-based models to estimate NO X emission in coal-fired power plants by a new data acquisition method are developed and the results indicate that the DBN-based models have a greater prediction accuracy and greater robustness compared to the three other NO X prediction models.