Search

This article studies the adaptive fuzzy fault tolerant control (FTC) problem for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with unknown control directions in the presence of time-varying asymmetric output constraints. Our contribution includes a step forward beyond usual FTC results to exhibit that the system output of nonsquare and square MIMO systems is uniformly bounded against actuator faults by a novel FTC methodology without the fault detection unit, as well as stay in the preselected constraints. To obtain new results, an equivalent unconstrained system is established by employing an error transformation technique. Furthermore, a learning-based switching function scheme is proposed to automatically activate different groups of actuators without human intervention for attenuating the influence of faulty actuators. By this means, no explicit fault detection and isolation units are needed to result in reducing the risk of false alarm or missed detections and expediting the responsiveness of the controller. Moreover, the obstacle caused by unknown control directions is circumvented by a novel technique combining the matrix decomposition technique and Nussbaum-type function. It is proved that the desired tracking performance with prescribed output bounds and the boundedness of all the signals in the closed-loop system can be guaranteed via an improved average dwell time approach. Finally, simulation results demonstrate the merits of the proposed controller. [ABSTRACT FROM AUTHOR]

In complex industrial processes, it is difficult to measure the key quality variables online. It takes a long time to obtain quality variables through offline testing, which makes it difficult to get timely information to guide the production process. Therefore, this article proposes a novel dynamic time feature expanding and extracting framework for the sinter quality prediction. First, the original data are differentiated, compensated for time delay, expanded, and serialized by using time characteristics, and the input time series is reconstructed. Second, the integrated time features extractor is used to obtain the process information. Then, the recurrent neural network regression is applied to obtain the prediction of key quality variables. Finally, the effectiveness of the proposed method is verified by a numerical example, the actual data of sintering process and various comparative experiments, and the prediction effect of FeO content in sintering process is improved. [ABSTRACT FROM AUTHOR]

Batch manufacturing has attracted extensive attention as a crucial way in process industry. It has typical wide-range nonstationary characteristics subject to time variant conditions, not only within each batch but also for different batches. The presence of closed-loop control complicates the task of batch process monitoring which shows that closed-loop control can significantly alter the process characteristics with both static deviations and varying temporal correlations. It is of a considerable challenge for batch process analysis and monitoring under closed-loop control which may work quite unlike that under open-loop control. In this article, a fine-scale process modeling and monitoring method are developed with dynamics analytics for wide-range nonstationary batch manufacturing with duration uncertainty. Both static and dynamic monitoring statistics are designed with clear physical interpretation which can work together to achieve fine-scale batch process status identification and monitoring. On one hand, it can identify frequent normal switching of operation status at different time by simultaneously checking both static and dynamic deviations regulated by closed-loop control. On the other hand, it can detect small disturbances in early stage where the impact may be overshadowed by the regulation of closed-loop controllers, making faults difficult to detect in time by conventional methods. Experiments have shown the validity of the proposed fine-scale monitoring method for wide-range batch process. [ABSTRACT FROM AUTHOR]

The papermaking industry supplies the carrier for literature and packaging via chemi-mechanical pulping, which is the most commonly used approach for converting wood chips into cellulose fibers. In 2016, Chinese papermaking enterprises consumed 41.05 million tons of standard coal. Thus, it is important to reduce the electrical energy consumed in mechanical pulp production by optimizing the operating conditions of primary stage refiners. This article reports a two-stage optimization algorithm to achieve this objective. First, mixed data sampling regression combined with a new weighting scheme derived from mixed flow conditions is used to encode the characteristics of high consistency refiner (HCR) in the manufacturing system. Then, the genetic algorithm with self-adaptive population searches for the optimal operating parameters that can minimize the power cost of the papermaking process. The proposed mixed framework and its separate components are validated through numerical functions and practical datasets, all of which have competitive performances compared to other algorithms. After being applied to a papermaking plant for two months, the intelligent optimization algorithm is found to have significant economic achievements in reducing the HCR energy consumption by 1.67 kWh/adt on an average scale. [ABSTRACT FROM AUTHOR]

This paper presents novel cooperative tracking control for a class of input-constrained multiagent systems with a dynamic leader. Each follower agent is described by a high-order nonlinear dynamics in strict feedback form with input constraints. Our main contribution lies in presenting a system transformation method that can convert the input-constrained state feedback cooperative tracking control of agents into an unconstrained output feedback control of agents with dynamics in Brunovsky normal form. As a result, the original problem is simplified to be a simple stabilization of the transformed system for the agents. Thus, the use of the backstepping scheme is obviated, and the synthesis and computation are extremely simplified. It is strictly proved that all follower agents can synchronize to the leader with bounded synchronization errors, and all other signals in the closed-loop system are semi-global uniformly ultimately bounded. Finally, numerical analysis is carried out to validate the theoretical results and demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive control strategy is presented for the tracking control of a class of multi-input-multioutput uncertain nonlinear systems with external disturbances to place user-defined time-varying constraints on the system state. Our contribution includes a step forward beyond the usual stabilization result to show that the states of the plant converge asymptotically, as well as remain within user-defined time-varying bounds. To achieve the new results, an error transformation technique is first established to generate an equivalent nonlinear system from the original one, whose asymptotic stability guarantees both the satisfaction of the time-varying restrictions and the asymptotic tracking performance of the original system. The uncertainties of the transformed system are overcome by an online neural network (NN) approximator, while the external disturbances and NN reconstruction error are compensated by the robust integral of the sign of the error signal. Via standard Lyapunov method, asymptotic tracking performance is theoretically guaranteed, and all the closed-loop signals are bounded. The requirement for a prior knowledge of bounds of uncertain terms is relaxed. Finally, simulation results demonstrate the merits of the proposed controller. [ABSTRACT FROM AUTHOR]

An adaptive consensus algorithm is proposed for a class of nonlinear multiagent systems with completely unknown agent dynamics. Due to uncertainties in the agent’s dynamics, previous consensus approaches usually yield uniformly ultimately bounded consensus error. Our main contribution includes a novel robust consensus algorithm which can guarantee that the consensus error converges to zero asymptotically. In order to address the unknown dynamics, a two-layer neural network (NN) is utilized to learn the unknown dynamics in an online manner, and a robust continuous term is introduced to alleviate effects of the NN residual reconstruction error and external disturbances. The continuousness of the control signal is guaranteed to remove the actuator bandwidth requirement and avoid the caused chattering phenomenon. The proposed consensus algorithm is distributed in the sense that each agent only exchanges information with its neighbor agents. The asymptotic consensus result is achieved via Lyapunov synthesis. Furthermore, the proposed algorithm can also be extended to the case where the agents are required to form a prescribed formation. Finally, simulation studies on a nonlinear multiagent system are provided to demonstrate the performance of the scheme. [ABSTRACT FROM PUBLISHER]

Graphics processing unit (GPU) has significantly increased the computing capacity of state-of-the-art high performance computing systems. This paper presents a novel GPU acceleration approach for transient stability-constrained optimal power flow (TSCOPF), which is one of the most computational challenging tasks in large-scale power system applications. Enabled by the revealed two-level decomposition parallelism in reduced-space interior point method, GPUs serve as plug-and-play coprocessors for time-consuming linear algebra operations in TSCOPF solving. Enhanced by multi-GPU processing and mixed-precision iterative refinement technique, the efficiency of solving TSCOPF is greatly improved without redesigning and reimplementing the existing algorithm framework. Numerical studies based on a series of test cases with up to 12 951 buses indicate the effectiveness of the proposed GPU-based approach for large-scale TSCOPF problems. [ABSTRACT FROM PUBLISHER]

Wireless charging is a promising way to solve the energy constraint problem in sensor networks. While extensive efforts have been made to improve the performance of charging and communication in wireless rechargeable sensor networks (WRSNs), little has been done to address the operation scheduling problem. To fill this void, we propose a joint energy replenishment and scheduling mechanism so as to maximize the network lifetime while making strict sensing guarantees in the WRSN. We first formulate the problem in a general 2-D space and prove its NP-completeness. We then devise an f-approximate scheduling mechanism by transforming the classical minimum set cover problem and develop an optimal energy-replenish strategy based on the energy consumption of nodes returned by the scheduling mechanism. Large-scale simulation results validate our design and show a $39.2\%$ improvement of network lifetime over a baseline method. [ABSTRACT FROM PUBLISHER]

This brief presents a novel power control strategy for variable-speed wind turbines equipped with doubly fed induction generators (DFIGs). The control objective is to optimize the extracted power from wind while regulating the stator reactive power to meet grid requirements. First, in order to optimize the extracted power, an adaptive control technique is designed to drive the electromagnetic torque to follow its reference generated by the maximum power point tracking algorithm. Subsequently, aiming at satisfying reactive power requirements on the grid side, an adaptive reactive power controller is proposed to manipulate the stator reactive power to follow a given desired reactive power determined by the grid. Compared with most existing studies, we are capable of quantifying and further guaranteeing the system performance on both transient and steady-state stages. All signals in the closed-loop system are proved to be bounded via standard Lyapunov synthesis. Finally, the effectiveness of the proposed scheme is validated on a 1.5-MW DFIG-based wind turbine using the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) simulator developed by the National Renewable Energy Laboratory. [ABSTRACT FROM AUTHOR]