The significant role for a contemporary control algorithm in the position control of a permanent magnet linear motor (PMLM) system is highlighted by the rigorous standards for accuracy in many modern industrial and robotics applications. A robust predefined time convergent sliding mode controller (PreDSMC) is designed for the high precision position tracking of a permanent magnet linear motor (PMLM) system with external disturbance, and its convergence time is independent of the system’s initial value and model parameters. We verified theoretically that the performance function conditions are satisfied, the motor speed is steady and constrained, and the motor position tracking error converges to zero within the prescribed time. First, we designed a sliding mode (SM) surface with predetermined time convergence, which mathematically demonstrates that the tracking error converges to zero within the predefined time and shows that the position tracking accuracy is higher. Secondly, we developed a PreDSMC law that is independent of initial state and based on the predefined time convergence Lyapunov stability criterion. Finally, to prove the accuracy and higher precision of the proposed PreDSMC, comparative numerical simulations are performed for PMLM with compound disturbances. Simulation findings show that the suggested robust predefined control method considerably reduces the impacts of friction and external disturbances; consequently, it may increase the control performance when compared to the typical proportional integral derivative (PID) controller, the nonsingular fast terminal SMC, and the linear SMC.

Densifying low-power/small base stations (SBSs) in support of overlaid macro BSs (MBS) is getting significant consideration as a viable solution for meeting the rapidly increasing capacity demands of emerging wireless networks. Given the disparity in transmit power of BS tiers, load balancing strategies have achieved paramount importance as they ensure balance of user access load amongst BS tiers and assist in availing maximum potential of multi-tier networks. Moreover, it is also essential to utilize efficient interference management schemes to account for the excessively increasing interference in the deployed networks. There is a plethora of literature available on the analysis of dense multi-tier networks that undertake various load balancing and interference management mechanisms but they are strictly limited to single slope (SS) path loss model (PLM). Furthermore, it is also well-known in literature that SS-PLM leads to inaccuracies in estimating the performance of dense networks owing to the simplistic approach of estimating the path loss. To overcome the limitations of SS-PLM, in our work, we have employed DSPLM to study and analyze the performance of a two-tier dense cellular network that permits flexible user association using SBS biasing in conjunction with a simple frequency reuse mechanism. A comparison of SS-PLM with DS-PLM is performed for tier association probability and coverage probability, which shows that there is significant difference in the estimated overall network performance of our proposed scheme, as compared to SS-PLM. The results clearly signify the benefits of utilizing both load balancing and interference management mechanisms under DS-PLM, and give critical design insights for finding optimal parametric values in cellular planning.

This paper presents a fault-tolerant control (FTC) strategy for a six-degree-of-freedom (DoF) anthropomorphic manipulator operating under actuator faults and complex friction dynamics. The proposed framework integrates a backstepping control methodology with LuGre friction modeling and a feedforward neural network (FFNN) for friction estimation. Actuator faults are considered in the form of multiplicative efficiency losses and additive disturbances. An adaptive control law is developed to estimate and compensate for both friction and actuator faults in real time. The stability of the closed-loop system is guaranteed through Lyapunov theory. The simulation results validate the effectiveness and robustness of the proposed approach in ensuring precise trajectory tracking despite faults and friction uncertainties.