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SUMMARY The flexible motion of the inchworm makes the locomotion mechanism as the prominent one than other limbless animals. Recently, the application of engineering greatly assists the inchworm locomotion to be applicable in the robotic mechanism. Due to the outstanding robustness, sliding mode control (SMC) has been validated as a robust control strategy for diverse types of systems. Even though the SMC techniques have made numerous achievements in several fields, some systems cannot be comfortably accepted as the general SMC approaches. Accordingly, this paper develops the Grey Wolf-Second order sliding mode control (GW-SoSMC) to control the manipulator of the inchworm robot. The GW-SoSMC reduces the chattering phenomenon of SMC and improves the controlling ability of SoSMC by weightage function. Subsequently, it compares the performance of the proposed method with several conventional techniques like Grey Wolf-SMC (GW-SMC), FireFly-SoSMC (FF-SoSMC), Artificial Bee Colony-SoSMC (ABC-SoSMC), Group Searching-SoSMC (GS-SoSMC), and Genetic Algorithm-SoSMC (GA-SoSMC). It portrays the valuable comparative analysis by measuring the accomplished joint angles, error, and response of the controller. Thus the proposed method discovers the supervisory controller for the inchworm robot that is immensely better than conventional controllers mentioned earlier.

This paper presents an adaptive neuro-fuzzy sliding mode control (ANFSMC) scheme for diving motion control of an autonomous underwater vehicle (AUV) in the presence of parameter perturbations and wave disturbances. In the derivation of diving motion equations of an AUV, the pitch angle of the vehicle is often assumed to be small in the vertical plane. This is a quite strong restricting condition in underwater operations and may cause serious modeling inaccuracies in AUV's dynamics. The problem of nonlinear uncertain diving behavior with restricting assumption on the pitch angle directly is resolved by a neural network (NN) based equivalent control. The online NN estimator is designed to approximate a part of the equivalent control term containing nonlinear unknown dynamics and external disturbances. Subsequently, corrective control based on an adaptive fuzzy proportional-integral control is applied to eliminate the chattering phenomenon by smoothing the switching signal and also compensate structured uncertainties. The weights of NN are updated such that the corrective control signal of the ANFSMC converges towards zero. The adaptive laws are developed to compute coefficients of PID sliding manifold and adjust the gain of fuzzy switching control. The simulation results are presented to shows the efficacy of the control performance. INDEX TERMS Autonomous underwater vehicle, adaptive neuro-fuzzy sliding mode control, diving motion, neural network, parameter perturbations and chattering phenomenon.

A novel dynamic fuzzy sliding mode control (DF-SMC) algorithm is developed for heading angle control of autonomous underwater vehicles (AUV's) in horizontal plane. So far, the dynamics of AUV's are highly nonlinear, time varying and hydrodynamic coefficients of vehicle are difficult to be accurately estimated a prior, because of the variations of these coefficients with different operating conditions. These kinds of difficulties cause modeling inaccuracies of AUV's dynamics. Therefore, DFSMC is proposed for regulating heading angle in horizontal plane in presence of parametric uncertainty and disturbances. In this approach, two fuzzy approximators are employed in such a way that, to vary the supports of input-output fuzzy membership functions in the inference engine module. These fuzzy approximators are mainly utilized for updating width of boundary layer and hitting gain. Simulation results shows that, the reaching time and tracking error in the approaching phase can be significantly reduced with chattering problem can also be eliminated. The effectiveness of proposed control strategy and its advantages are indicated in comparison with conventional sliding mode control and fuzzy sliding mode control.

Safe and incessant power flow is considered as a serious problem which has to be corrected in the transmission line. Actually, rescheduling based Congestion Management is deliberated as the capable solutions for the feature. Still, the technique faces problems based on the cost of rescheduling. A huge number of research studies have been identified hitherto to resolve the problems in Congestion Management. In addition, optimization methods play a very important role to solve this problem. In this case, this work develops a novel rescheduling based Congestion Management. technique that integrates a novel method; Grey Wolf Optimization (GWO) and Grasshopper Optimization Algorithm (GOA) (GWO-GOA) which optimizes generating power of augmented generators using the bus system. The proposed GWO-GOA method is the hybridization of two methods such as GWO and GOA, which aspires to manage the congestion through minimized rescheduling cost. Furthermore, the proposed technique evaluates its performance with the other existing techniques based on rescheduling stratagem regarding the analysis of cost it shows the effectiveness of proposed methods over other existing algorithms.