Extended grey wolf optimization–based adaptive fast nonsingular terminal sliding mode control of a robotic manipulator

This article proposes a novel hybrid metaheuristic technique based on nonsingular terminal sliding mode controller, time delay estimation method, an extended grey wolf optimization algorithm and adaptive super twisting control law. The fast convergence is assured by nonsingular terminal sliding mode controller owing to its inherent nonlinear property and no prior knowledge of the robot dynamics is required due to time delay estimation. The proposed extended grey wolf optimization algorithm determines an optimal approximation of the inertial matrix of the robot. Moreover, adaptive super twisting control based on the Lyapunov approach overcomes the disturbances and compensate the higher dynamics not achievable by the time delay estimation method. First, the fast nonsingular terminal sliding mode controller relying on time delay estimation is designed and is combined with super twisting control for chattering attenuation. The constant gain matrix of the time delay is determined by the proposed extended grey wolf optimization algorithm. Second, an adaptive law based on Lyapunov stability theorem is designed for improving tracking performance in the presence of uncertainties and disturbances. The novelty of the proposed method lies in the adaptive law where the prior knowledge of parametric uncertainties and disturbances is not needed. Moreover, the constant gain matrix of time delay estimation method is obtained using the proposed algorithm. The control method has been tested in simulation on a 3-degrees of freedom robotic manipulator in trajectory tracking mode in the presence of control disturbances and uncertainties. The results obtained confirmed the effectiveness, robustness and the superior precision of the proposed control method compared to the classical ones

Visual based lane following for non-holonomic mobile robot

Abstract: This paper aims to contribute in mobile robots endowment with autonomy when accomplishing robotic tasks. The problem treated concerns path following by non-holonomic mobile robot (Pioneer II DX) using visual data coming from CCD camera mounted on the latter. The current advancing path is approximated by a linear function in case of straight line path and by a third function in curve case. The obtained experimental results confirm the effectiveness of the proposed approach

Adaptive robust nonsingular terminal sliding mode design controller for quadrotor aerial manipulator

In this paper, a novel adaptive control approach for Unmanned Aerial Manipulators (UAMs) is proposed. The UAMs are a new configuration of the Unmanned Arial Vehicles (UAVs) which are characterized by several inhered nonlinearities, uncertainties and coupling. The studied UAM is a Quadrotor endowed with two degrees of freedom robotic arm. The main objectives of our contribution are to achieve both a tracking error convergence by avoiding any singularity problem and also the chattering amplitude attenuation in the presence of perturbations. Therefore, the proposed Adaptive Nonsingular Terminal Super-Twisting controller (ANTSTW) consists of the hybridization of a Nonsingular Terminal Sliding Mode Control and an Adaptive Super Twisting. The adaptive law, which adjust the Super-Twisting’s parameters, is obtained by using stability Lyapunov theorem. Simulation experiments in trajectory tracking mode were realized and compared with Nonsingular Terminal Super-twisting control to prove the superiority and the effectiveness of the proposed approach

Decentralized RBFNN Type-2 Fuzzy Sliding Mode Controller for Robot Manipulator Driven by Artificial Muscles

In the few last years, investigations in neural networks, fuzzy systems and their combinations become attractive research areas for modeling and controlling of uncertain systems. In this paper, we propose a new robust controller based on the integration of a Radial Base Function Neural Network (RBFNN) and an Interval Type-2 Fuzzy Logic (IT2FLC) for robot manipulator actuated by pneumatic artificial muscles (PAM). The proposed approach was synthesized for each joint using Sliding Mode Control (SMC) and named Radial Base Function Neural Network Type-2 Fuzzy Sliding Mode Control (RBFT2FSMC). Several objectives can be accomplished using this control scheme such as: avoiding difficult modeling, attenuating the chattering effect of the SMC, reducing the rules number of the fuzzy control, guaranteeing the stability and the robustness of the system, and finally handling the uncertainties of the system. The proposed control approach is synthesized and the stability of the robot using this controller was analyzed using Lyapunov theory. In order to demonstrate the efficiency of the RBFT2FSMC compared to other control technique, simulations experiments were performed using linear model with parameters uncertainties obtained after identification stage. Results show the superiority of the proposed approach compared to RBFNN Type-1 Fuzzy SMC. Finally, an experimental study of the proposed approach was presented using 2-DOF robot

Optimization of fuzzy logic quadrotor attitude controller - particle swarm, Cuckoo search and BAT algorithms

Bio-inspired optimisation algorithms have recently attracted much attention in the control community. Most of these algorithms mimic particular behaviours of some animal species in such a way that allows solving optimisation problems. The present paper aims at applying three metaheuristic methods for optimising fuzzy logic controllers used for quadrotor attitude stabilisation. The investigated methods are particle swarm optimisation (PSO), BAT algorithm and cuckoo search (CS). These methods are applied to find the best output distribution of singleton membership functions of the fuzzy controllers. The quadrotor control requires measured responses, therefore, three objective functions are considered: integral squared error, integral time-weighted absolute error and integral time-squared error. These metrics allow performance comparison of the controllers in terms of tracking errors and speed of convergence. The simulation results indicate that BAT algorithm demonstrated higher performance than both PSO and CS. Furthermore, BAT algorithm is capable of offering 50% less computation time than CS and 10% less time than PSO. In terms of fitness, BAT algorithm achieved an average of 5% better fitness than PSO and 15% better than CS. According to these results, the BAT-based fuzzy controller exhibits superior performance compared with other algorithms to stabilise the quadrotor

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)