Synchronization controller for a 3-RRR parallel manipulator

A 3-RRR parallel manipulator has been well-known as a closed-loop kinematic chain mechanism in which the end-effector generally a moving platform is connected to the base by several independent actuators. Performance of the robot is decided by performances of the component actuators which are independently driven by tracking controllers without acknowledging information from each other. The platform performance is degraded if any actuator could not be driven well. Therefore, this paper aims to develop an advanced synchronization (SYNC) controller for position tracking of a 3-RRR parallel robot using three DC motor-driven actuators. The proposed control scheme consists of three sliding mode controllers (SMC) to drive the actuators and a supervisory controller named PID-neural network controller (PIDNNC) to compensate the synchronization errors due to system nonlinearities, uncertainties and external disturbances. A Lyapunov stability condition is added to the PIDNNC training mechanism to ensure the robust tracking performance of the manipulator. Numerical simulations have been performed under different working conditions to demonstrate the effectiveness of the suggested control approach

A Real-Time Bilateral Teleoperation Control System over Imperfect Network

Functionality and performance of modern machines are directly affected by the implementation of real-time control systems. Especially in networked teleoperation applications, force feedback control and networked control are two of the most important factors, which determine the performance of the whole system. In force feedback control, generally it is necessary but difficult and expensive to attach sensors (force/torque/pressure sensors) to detect the environment information in order to drive properly the feedback force. In networked control, there always exist inevitable random time-varying delays and packet dropouts, which may degrade the system performance and, even worse, cause the system instability. Therefore in this chapter, a study on a real-time bilateral teleoperation control system (BTCS) over an imperfect network is discussed. First, current technologies for teleoperation as well as BTCSs are briefly reviewed. Second, an advanced concept for designing a bilateral teleoperation networked control (BTNCS) system is proposed, and the working principle is clearly explained. Third, an approach to develop a force-sensorless feedback control (FSFC) is proposed to simplify the sensor requirement in designing the BTNCS, while the correct sense of interaction between the slave and the environment can be ensured. Fourth, a robust-adaptive networked control (RANC)-based master controller is introduced to deal with control of the slave over the network containing both time delays and information loss. Case studies are carried out to evaluate the applicability of the suggested methodology

Sensorless force feedback joystick control for teleoperation of construction equipment

This paper aims to develop an innovative approach named sensorless force feedback joystick control for teleoperation of construction equipment. First, a force sensorless supervisory controller is designed with two advanced modules: a neural network-based environment classifier to estimate environment characteristics without requiring a force sensor and, a fuzzy-based force feedback tuner to generate properly a force reflection to the joystick. Second, two local robust adaptive controllers are simply built using neural network and Lyapunov stability condition to ensure desired task performances at both master and slave sites. A teleoperation system is setup to demonstrate the applicability of the proposed approach

Synchronization controller for a 3-RRR parallel manipulator

A 3-RRR parallel manipulator has been well-known as a closed-loop kinematic chain mechanism in which the end-effector generally a moving platform is connected to the base by several independent actuators. Performance of the robot is decided by performances of the component actuators which are independently driven by tracking controllers without acknowledging information from each other. The platform performance is degraded if any actuator could not be driven well. Therefore, this paper aims to develop an advanced synchronization (SYNC) controller for position tracking of a 3-RRR parallel robot using three DC motor-driven actuators. The proposed control scheme consists of three sliding mode controllers (SMC) to drive the actuators and a supervisory controller named PID-neural network controller (PIDNNC) to compensate the synchronization errors due to system nonlinearities, uncertainties and external disturbances. A Lyapunov stability condition is added to the PIDNNC training mechanism to ensure the robust tracking performance of the manipulator. Numerical simulations have been performed under different working conditions to demonstrate the effectiveness of the suggested control approach

Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression

Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy

Sensor Fault-Tolerant Control Design for Mini Motion Package Electro-Hydraulic Actuator

With the rapid development of computer science and information and communication technology (ICT), increasingly intelligent, and complex systems have been applied to industries as well as human life. Fault-tolerant control (FTC) has, therefore, become one of the most important topics attracting attention from both engineers and researchers to maintain system performances when faults occur. The ultimate goal of this study was to develop a sensor fault-tolerant control (SFTC) to enhance the robust position tracking control of a class of electro-hydraulic actuators called mini motion packages (MMPs), which are widely used for applications requiring large force-displacement ratios. First, a mathematical model of the MMP system is presented, which is then applied in the position control process of the MMP system. Here, a well-known proportional, integrated and derivative (PID) control algorithm is employed to ensure the positional response to the reference position. Second, an unknown input observer (UIO) is designed to estimate the state vector and sensor faults using a linear matrix inequality (LMI) optimization algorithm. Then an SFTC is used to deal with sensor faults of the MMP system. The SFTC is formed of the fault detection and the fault compensation with the goal of determining the location, time of occurrence, and magnitude of the faults in the fault signal compensation process. Finally, numerical simulations were run to demonstrate the superior performance of the proposed approach compared to traditional tracking control