To solve the problem of offset failure of flexible single-link manipulator actuator due to actuator aging and wear,an adaptive boundary fault-tolerant control combining hysteresis quantizer and event trigger mechanism is proposed.Firstly,a hysteresis quantizer with unknown parameters is used to quantize the control input signal.Secondly,a static event trigger mechanism is constructed to reduce the consumption of input signal resources.Furthermore,the adaptive boundary fault-tolerant control law and parameter updation law are designed based on the improved Lyapunov direct method.Finally,the simulation results show that the proposed control algorithm is feasible.With communication constraints and disturbances,the proposed control method can achieve better posture tracking effect compared with traditional adaptive methods,while achieving system stability with lower computational load.
In recent years, the exponential growth in global mobile traffic, driven by advanced applications, services, and devices, has led to the deployment of numerous small cell base stations to meet future network needs. Ensuring stable connectivity is crucial for seamless mobile user experiences. This paper introduces an adaptive fuzzy logic-based handover parameter adjustment approach aimed at enhancing UE mobility robustness by reducing handover failure (HOF) and ping-pong handover (PPHO) occurrences, all while maintaining high throughput. Simulation results demonstrate the method's effectiveness in adjusting handover parameters based on system performance indicators across different speed ranges. Compared to traditional fuzzy logic, clustering-based fuzzy logic, and A3 event-based optimisation methods, our proposed approach consistently outperforms in terms of reducing HOPP and HOF while sustaining high throughput levels.
The main concern in this letter is the almost sure active-synchronisation (act-synchronisation) of stochastic network systems. The scaling parameters are adopted to quantify the level of act-synchronisation among agents, and heterogeneous gains are used to assure the physical realisation of the overall plant, due to the inconsistency in signs among scaling parameters. For the devised proposal, it is demonstrated that there is indeed no reason at all to impose a common sign for each pair of scaling parameters and heterogeneous gain. To avoid global information, a local-information-based linear transformation is carried out. Subsequently, the requirement guaranteeing almost sure act-synchronisation is discussed by exploring the exponential stability condition in the mean square sense, associated with the involved error system. It is suggested that the statistical characteristics of the noise relative to expectation and covariance are crucial for the problem of interest, apart fromthe scaling parameter, heterogeneous gain, and the interacting graph.
In the last decade, the service of intelligent transportation systems has benefited from the rapid development of advanced computing and communication technologies. However, it is difficult to satisfy the increasing user demand and strict service requirements with the current local or cloud computing paradigm only. In this paper, we propose an operator-based edge service network composed of multi-layer roadside units (RSUs) for various scenarios. An optimised task offloading scheduling strategy considering the service demand, computing delay and energy consumption, is designed for both stochastic and concurrent tasks. Furthermore, a genetic algorithm (GA) is proposed to solve the bandwidth allocation problem after the computation execution. Finally, the simulation results verify the service efficiency and operation effectiveness of the proposed strategy, in terms of the task execution delay, operation cost and attainment rate.
Recently, there has been a high demand for unmanned aerial vehicles (UAVs) for oil and gas pipeline inspection. Therefore, a multisensory quadcopter is developed using an edge controller to meet this demand. In addition, a pair of thermosensitive gas sensors and nondispersive infrared (NDIR) gas sensors are embedded in the quadcopter for detecting pipeline leakages. Python scripts are used to analyse the quadcopter's dynamics during flight in order to evaluate its ability to deploy instruments. The result of this work is a system that can fly semi-autonomously to survey pipeline infrastructure in real-time. Performance analysis has been carried out on the system and its subcomponents. Results reveal that the developed system functions as predicted. Moreover, we envisage that this system will help improve safety, situational awareness, and decision-making in the oil and gas industries.
Non-intrusive household load identification is an important way to achieve intelligent power consumption management. Random forests (RF), an ensemble learning method, has wide applicability and high robustness, which has been employed in the field of load identification. An enhanced random forests (ERF) algorithm is proposed in this work to solve the problem that the traditional random forest algorithm ignores the difference of ability of decision tree classification and the unfairness of voting. First, the Bayesian information criterion (BIC) is used to detect and identify switching events. Second, the best feature set is selected according to the time-frequency characteristics of the load information. Finally, the enhanced random forest algorithm is used to establish the load identification model. Experimental results show that ERF can achieve higher recognition accuracy of load categories than RF, and the accuracy can reach more than 90%.
