Artificial intelligent based friction modelling and compensation in motion control system

The interest in the study of friction in control engineering has been driven by the need for 10 precise motion control in most of industrial applications such as machine tools, robot 11 systems, semiconductor manufacturing systems and Mechatronics systems. Friction has 12 been experimentally shown to be a major factor in performance degradation in various 13 control tasks. Among the prominent effects of friction in motion control are: steady state 14 error to a reference command, slow response, periodic process of sticking and sliding (stick-15 slip) motion, as well as periodic oscillations about a reference point known as hunting when 16 an integral control is employed in the control scheme. Table 1 shows the effects and type of 17 friction as highlighted by Armstrong et. al.(1994). It is observed that, each of task is 18 dominated by at least one friction effect ranging from stiction, or/and kinetic to negative 19 friction (Stribeck). Hence, the need for accurate compensation of friction has become 20 important in high precision motion control. Several techniques to alleviate the effects of 21 friction have been reported in the literature (Dupont and Armstrong, 1993; Wahyudi, 2003; 22 Tjahjowidodo, 2004; Canudas, et. al., 1986). 23 One of the successful methods is the well-known model-based friction compensation 24 (Armstrong et al., 1994; Canudas de Wit et al., 1995 and Wen-Fang, 2007). In this method, 25 the effect of the friction is cancelled by applying additional control signal which generates a 26 torque/force. The generated torque/force has the same value (or approximately the same) 27 with the friction torque/force but in opposite direction

To develop an efficient variable speed compressor motor system

This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment

Parallel Distributed Compensation-PID Controller Design for Maximum Power Point Tracking of Dynamic Loaded Photovoltaic System

Control issues come from the output voltage of PV installations and systems operating in a range of irradiance and temperature. By using a DC converter, such systems are able to maintain a constant output voltage despite fluctuations in the generated voltage and load. The design of a maximum power point tracking (MPPT) on DC converter controller is presented in this article for a system. Fractional Order-Proportional Integral Derivative (FO-PID) and Parallel Distributed Compensation-Proportional Integral Derivative (PDC-PID) controllers have been implemented to the system converter as a proposed control approach. Particle Swarm Optimization (PSO) is used as optimization technique for determining the optimal parameters of (FO-PID) and (PDC-PID) controllers for tracking the output voltage from trained Adaptive Neuro Fuzzy Inference System (ANFIS) that is corresponding to maximum power generated from (PV) module. The PV system with the dynamic load is modeled and simulated by using the MATLAB/Simulink environment. The system performance is displayed in the form of a family of curves under different operating conditions

A New Self-Tuning Nonlinear PID Motion Control for One-Axis Servomechanism with Uncertainty Consideration

This paper introduces a new study for one-axis servomechanism with consideration the parameter variation and system uncertainty. Also, a new approach for high-performance self-tuning nonlinear PID control was developed to track a preselected profile with high accuracy. Moreover, a comparison study between the proposed control technique and the well-known controllers (PID and Nonlinear PID). The optimal control parameters were determined based on the COVID-19 optimization technique. The parameters of the servomechanism system changed randomly at a preselected range through the online simulation. The change of these parameters acts as the nonlinearity resources (friction, backlash, environmental effects) and system uncertainty. A comparative study between the linear and nonlinear models had been accomplished and investigated. The results show that the proposed controller can track several operating points with high accuracy, low rise time, and small overshoot

Measurements, Models, Systems and Design

531 s.

Fuzzy-PID in BLDC Motor Speed Control Using MATLAB/Simulink

Brushless DC motors (BLDC) are one of the most widely used types of DC motors, both in the industrial and automotive fields. BLDC motor was chosen because it has many advantages over other types of electric motors. However, in its application in the market, most of the control systems used in BLDC motors still use conventional controls. This conventional method is easy and simple to apply but has many weaknesses, one example is that if the system state changes, then the parameters of the PID must also be changed so that static and dynamic performance will decrease, causing slow response and frequent oscillations. In this study, the design and simulation of a speed control system for BLDC motors using the Fuzzy-PID method were carried out. The research method is performed through simulation with Matlab / Simulink. The simulation is carried out by providing a speed setpoint input of 650 rpm and used 2 methods, namely Fuzzy-PID Logic and Pi conventional method which was carried out for 1 second. The test results show that the Fuzzy-PID control can provide better and more stable performance than the conventional PI control. The use of Fuzzy-PID control can reduce speed fluctuation and torque stability so that the BLDC motor can operate more efficiently and reliably

Design and Development of a Twisted String Exoskeleton Robot for the Upper Limb

High-intensity and task-specific upper-limb treatment of active, highly repetitive movements are the effective approaches for patients with motor disorders. However, with the severe shortage of medical service in the United States and the fact that post-stroke survivors can continue to incur significant financial costs, patients often choose not to return to the hospital or clinic for complete recovery. Therefore, robot-assisted therapy can be considered as an alternative rehabilitation approach because the similar or better results as the patients who receive intensive conventional therapy offered by professional physicians.;The primary objective of this study was to design and fabricate an effective mobile assistive robotic system that can provide stroke patients shoulder and elbow assistance. To reduce the size of actuators and to minimize the weight that needs to be carried by users, two sets of dual twisted-string actuators, each with 7 strands (1 neutral and 6 effective) were used to extend/contract the adopted strings to drive the rotational movements of shoulder and elbow joints through a Bowden cable mechanism. Furthermore, movements of non-disabled people were captured as templates of training trajectories to provide effective rehabilitation.;The specific aims of this study included the development of a two-degree-of-freedom prototype for the elbow and shoulder joints, an adaptive robust control algorithm with cross-coupling dynamics that can compensate for both nonlinear factors of the system and asynchronization between individual actuators as well as an approach for extracting the reference trajectories for the assistive robotic from non-disabled people based on Microsoft Kinect sensor and Dynamic time warping algorithm. Finally, the data acquisition and control system of the robot was implemented by Intel Galileo and XILINX FPGA embedded system