Adaptive fractional-order non-singular terminal sliding mode control based on fuzzy wavelet neural networks for omnidirectional mobile robot manipulator

Fractional order fast terminal sliding mode control scheme for tracking control of robot manipulators

In this study, a fractional order fast terminal sliding mode control strategy is developed to address the trajectory tracking problem that arises when robot manipulators are subjected to uncertainties and external disturbances. A novel fractional order fast terminal sliding surface is proposed to achieve rapid finite time convergence and the explicit expression for the settling time is also formulated. To manage uncertainties, chattering phenomenon, singularities, large control gains, etc., a new fractional order fast terminal sliding mode control scheme is developed based on the proposed sliding surface. The radial basis function neural network is used in the proposed control strategy to approximate the nonlinearities and modeling errors of the robot dynamics in real time. The reconstruction error of neural network and upper bound on disturbances are handled by the adaptive compensator. The Lyapunov technique is used to examine the stability of the proposed control strategy. The proposed control technique improves the efficiency of the controller and allows for the asymptotic error convergence to occur in a finite amount of time. To compare the effectiveness of the proposed scheme to various existing control approaches, numerical simulation studies are also conducted.

An adaptive robust backstepping improved control scheme for mobile manipulators robot

In this study, an approach with the position control scheme is proposed for the mobile manipulator robot, by applying the adaptive strategies with the backstepping control technique. In the proposed backstepping controller, the fixed problem of main control parameters has been improved by the adaptive self-updating process. In addition, this control method requires no prior knowledge of the control system, such as the unknown dynamics and disturbances. And, to relax uncertainties, an adaptive robust controller is also designed as a compensator for the main backstepping controller. Moreover, all adaptive updating algorithms are designed based on the Lyapunov theorem so that the proposed control system performances are guaranteed. The advantage of the improved control system will be evaluated via simulation results for the 3 - DOF MMR system.

A comparative study of data-driven models for runoff, sediment, and nitrate forecasting

Effective prediction of qualitative and quantitative indicators for runoff is quite essential in water resources planning and management. However, although several data-driven and model-driven forecasting approaches have been employed in the literature for streamflow forecasting, to our knowledge, the literature lacks a comprehensive comparison of well-known data-driven and model-driven forecasting techniques for runoff evaluation in terms of quality and quantity. This study filled this knowledge gap by comparing the accuracy of runoff, sediment, and nitrate forecasting using four robust data-driven techniques: artificial neural network (ANN), long short-term memory (LSTM), wavelet artificial neural network (WANN), and wavelet long short-term memory (WLSTM) models. These comparisons were performed in two main tiers: (1) Comparing the machine learning algorithms' results with the model-driven approach; In order to simulate the runoff, sediment, and nitrate loads, the Soil and Water Assessment Tool (SWAT) model was employed, and (2) Comparing the machine learning algorithms with each other; The wavelet function was utilized in the ANN and LSTM algorithms. These comparisons were assessed based on the substantial statistical indices of coefficient of determination (R-Squared), Nash-Sutcliff efficiency coefficient (NSE), mean absolute error (MAE), and root mean square error (RMSE). Finally, to prove the applicability and efficiency of the proposed novel framework, it was successfully applied to Eagle Creek Watershed (ECW), Indiana, U.S. Results demonstrated that the data-driven algorithms significantly outperformed the model-driven models for both the calibration/training and validation/testing phases. Furthermore, it was found that the coupled ANN and LSTM models with wavelet function led to more accurate results than those without this function.

Neural network-based adaptive second-order sliding mode control for uncertain manipulator systems with input saturation

In order to solve the trajectory tracking problem for robotic manipulators with dynamic uncertainty, external disturbance and input saturation, a novel second-order sliding mode control scheme based on neural network is proposed in this paper. First of all, a model-based second-order non-singular fast terminal sliding mode controller (SONFTSMC) is designed to overcome the chattering problem under the consideration of uncertain parameters. Then attention is focused on the scenario that all those nonlinear uncertainties are unknown, and a new fuzzy wavelet neural network (FWNN) is designed to estimate those unknown uncertainties via lumping them into one compounded uncertainty. In addition, all parameters in FWNN are adjusted autonomously by using an adaptive method. The proposed second-order non-singular fast terminal sliding mode (SONFTSM) control method not only improves the convergence speed and tracking accuracy of the robotic manipulator, but also enhances its robustness. Finally, the advantages of SONFTSM control strategy over existing sliding mode control methods are verified with comparative simulations.

Neural Tracking Control of a Four-Wheeled Mobile Robot with Mecanum Wheels

This study designed an algorithm for the intelligent control of the motion of a mobile robot with mecanum wheels. After reviewing the model kinematics and dynamics of the robot, we conducted a synthesis of the neural control algorithm to determine network weight adaptation, according to Lyapunov stability theory. Using a MATLAB/Simulink computing environment, we developed a numerical simulation for the implementation of the robot’s motion path with parametric disturbances acting on the control object. To determine the quality of the implementation of the desired motion path, a numerical test of the robot’s motion, controlled with the use of a PD controller, was conducted. The proposed control algorithm was verified on a laboratory stand equipped with a dSpace DS1103 controller board and a Husarion Panther four-wheeled mobile robot with mecanum wheels. The conducted research confirmed the improved implementation of the desired motion path by a robot controlled with the use of an intelligent control system.

Adaptive Fuzzy Fractional Order Global Sliding Mode Tracking Control Algorithm for Particleboard Glue System

In this paper, a novel flow tracking control scheme for particleboard glue system with complex disturbance and unmeasurable system state is investigated. The method is based on hyperbolic tangent extended state observer and adaptive fuzzy fractional order global sliding mode control with exponential reaching law. The novel compound control scheme has the following advantages: Firstly, the extended state observer with hyperbolic tangent function can improve the estimation ability for the system state and complex disturbance without detailed knowledge of the controlled plant and disturbance model. Secondly, the global sliding mode control method based on fractional calculus can improve the response speed and robustness of the system, and provide a more flexible controller structure than the traditional sliding mode controller. Thirdly, the adaptive fuzzy controller is introduced to approximate the sliding mode switching term, so as to reduce the chattering phenomenon of the system. In addition, the convergence of the proposed observer and asymptotic stability of the control system are verified based on strict Lyapunov analysis. Finally, the numerical simulation results show the effectiveness of the proposed compound control scheme for particleboard glue system.

Recent Developments and Challenges of 3D-Printed Construction: A Review of Research Fronts

In the last few years, scattered experiences of the application of additive manufacturing in the construction of buildings using 3D printing with robots or automated equipment have emerged around the world. These use a variety of procedures and suggest relevant advantages for the construction industry. In order to identify the different processes and features in development in this field and to guide future research and applications, this article presents a review of the literature on the main aspects involved in the use of 3D printing in the construction sector. The review includes state-of-the-art material mixtures, printing technologies, and potential uses, as well as a novel analysis of building strategies, management systems, and benefits stated about this new approach for construction. It reveals progressive experimentation regarding diverse features, with challenges related to the consolidation of procedures and this technology’s readiness to participate in the building market.