This paper addresses the guaranteed cost leaderless consensus of delayed fractional-order (FO) multi-agent systems (FOMASs) with nonlinearities and uncertainties. A guaranteed cost function for FOMAS is proposed to simultaneously consider consensus performance and energy consumption. By employing the linear matrix inequality approach and the FO Razumikhin theorem, a delay-dependent and order-dependent consensus protocol is formulated for FOMASs with input delay. The proposed protocol not only guarantees the robust stability of the closed-loop system error but also ensures that the performance degradation caused by the system uncertainty is lesser than that obtained with other approaches. Two numerical examples are provided in order to verify the effectiveness and accuracy of the proposed protocol.
Permanent magnet synchronous motors (PMSM), which are with the advantages of high torque-to-weight ratio and high efficiency, are widely applied in modern industrial systems. However, existing approaches may fail to accurately track the speed trajectory because of the load disturbances. This paper proposes an equivalent and combined control strategy to mitigate the slow time-varying load disturbances and decrease the overshoot for PMSM in full speed range. First, a state observer is proposed to reconstruct the current variables and speed state in the d-q axis. Hence, one can get the speed and position information without the sensors. Then, the disturbance and the load are estimated by the estimating law. Thus, it can reduce the effect of load and disturbances. Further, the PD control is introduced to weaken the overshoot. As a result, the speed trajectory can be more effectively hold both in high speed and low speed. Finally, numerical examples are presented to demonstrate the validity and effectiveness of the proposed estimation scheme and its robustness under different conditions.
Predictive maintenance integrates equipment condition monitoring, fault diagnosis, fault prediction, maintenance decision support and maintenance activities. Intelligent manufacturing upgrades need to match the synchronous improvement of predictive maintenance capabilities, and predictive maintenance is the basic guarantee for enterprises to achieve intelligent manufacturing. Take the equipment condition monitoring and diagnosis system applicate in process industry as an example, this paper proposes IOT and operation big data analysis to equipment fault monitoring, diagnosis and preventive maintenance. Under the three-layer system framework of perception layer, network layer and application layer, machine learning algorithm is applied to carry out data mining on the equipment operation big data, establish expert knowledge base, obtain diagnosis rules, and realize the intelligent and efficient management mode integrating online monitoring, remote monitoring, remote diagnosis, fault matching and identification. Based on IOT and operation large data analysis, equipment health intelligent diagnosis provides the basic guarantee for equipment intelligent operation and maintenance, which helping enterprise establish new equipment management, maintenance, inspection and repair system under the concept of predictive maintenance and proactive maintenance.
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