Exponential control law for a multi-degree of freedom mobile robot

2004 ◽  
Author(s):  
G. Ramirez ◽  
S. Zeghloul
Keyword(s):  
Mobile Robot ◽  
Degree Of Freedom ◽  
Control Law

Integration of Multibody System Dynamics With Sliding Mode Control Using FPGA Technique for Trajectory Tracking Problems

2018 ◽  
Author(s):  
Ayman A. Nada ◽  
Abdullateef H. Bashiri
Keyword(s):  
Sliding Mode Control ◽  
Mobile Robot ◽  
Control Systems ◽  
Trajectory Tracking ◽  
Sliding Mode ◽  
Integrated Control ◽  
Equations Of Motion ◽  
Multiple Input Multiple Output ◽  
Control Law ◽  
Mode Control

Trajectory tracking robotic systems require complex control procedures that occupy less space and need less energy. For these reasons, the development of computerized and integrated control systems is crucial. Recently, developing reconfigurable Field Programmable Gate Arrays (FPGAs) give a prominence of the complete robotic control systems. Furthermore, it has been found in the literature that the model-based control methods are most efficient and cost-effective. This model must interpret how multiple moving parts interact with each other and with their environment. On the other hand, MultiBody Dynamic (MBD) approach is considered to solve these difficulties to attain the models accurately. However, the obtained equations of motion do not match the well-developed forms of control theory. In this paper, the MBD model of a mobile robot is established; and the equations of motion are reshaped into their control canonical form. Additionally, the Sliding Mode Control (SMC) theory is used to design the control law. The constraints’ manifold, which is available in the equations of the MBD system, are imposed systematically as the switching surface. SMC is applied because of its ability to address multiple-input/multiple-output nonlinear systems without resorting any approximations. Eventually, the experimental verification of the proposed algorithm is carried out using DaNI mobile robot in which, a Reconfigurable Input/Output (RIO) board is used to reorient the control design, so that can fit the required trajectory. The control law is implemented using LabVIEW software and NI-sbRIO-9631 with acceptable performance. It is obvious that the integration of MBD/SMC/FPGA can be used successfully to develop embedded systems for the applications of trajectory tracking robotics.

scholarly journals Robust ℋ∞-Fuzzy Logic Control for Enhanced Tracking Performance of a Wheeled Mobile Robot in the Presence of Uncertain Nonlinear Perturbations

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3673 ◽  
Author(s):  
Nur Ahmad
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Wheeled Mobile Robot ◽  
Tracking Performance ◽  
Model Parameters ◽  
Control Law ◽  
System Modelling ◽  
Nonlinear Perturbations ◽  
Model Errors ◽  
Dc Motors

Motion control involving DC motors requires a closed-loop system with a suitable compensator if tracking performance with high precision is desired. In the case where structural model errors of the motors are more dominating than the effects from noise disturbances, accurate system modelling will be a considerable aid in synthesizing the compensator. The focus of this paper is on enhancing the tracking performance of a wheeled mobile robot (WMR), which is driven by two DC motors that are subject to model parametric uncertainties and uncertain deadzones. For the system at hand, the uncertain nonlinear perturbations are greatly induced by the time-varying power supply, followed by behaviour of motion and speed. In this work, the system is firstly modelled, where correlations between the model parameters and different input datasets as well as voltage supply are obtained via polynomial regressions. A robust H ∞ -fuzzy logic approach is then proposed to treat the issues due to the aforementioned perturbations. Via the proposed strategy, the H ∞ controller and the fuzzy logic (FL) compensator work in tandem to ensure the control law is robust against the model uncertainties. The proposed technique was validated via several real-time experiments, which showed that the speed and path tracking performance can be considerably enhanced when compared with the results via the H ∞ controller alone, and the H ∞ with the FL compensator, but without the presence of the robust control law.

Development of a Tracking System on the Mobile Robot

2015 ◽  
Vol 764-765 ◽  
pp. 680-684
Author(s):  
Kuo Lan Su ◽  
Jr Hung Guo ◽  
Kuo Hsien Hsia
Keyword(s):  
Image Processing ◽  
Control System ◽  
Mobile Robot ◽  
Pid Control ◽  
Balance Control ◽  
Tracking System ◽  
Control Law ◽  
Moving Target ◽  
System A ◽  
Feedback Data

The purpose of this paper is to develop an intelligent mobile robot using image processing technology. The mobile robot is composed of a visual tracking system, a loading platform, a balance control system, a PC-based controller, four ultrasonic sensors and a power system. We develop a PC based control system for image processing and path planning. The mobile robot can track a moving target and adjust the loading platform by the balance control system simultaneously. The Image processing based on OpenCV use two different tracking methods, MTLT (Match Template Learning Tracking) and TLD (Tracking, Learning and Detection), to track moving targets. The efficiencies of both methods for tracking the moving target on the mobile robot are compared in this paper. The loading platform control system uses HOLTEK Semiconductor Company's HT66F Series 8-bit microprocessor as the processor, and receives the feedback data from the FAS-A inclinometer sensor. The controller of the loading platform uses the PID control law according to the feedback signals of the inclinometer sensor, and controls the rotation speed of the platform motor to tune the balance level. Keywords— Intelligent mobile robot, Image processing, OpenCV, MTLT, TLD, HOLTEK, FAS-A inclinometer sensor, PID control.

Improving the Tracking of Generalized Predictive Control Controllers

1996 ◽  
Vol 210 (3) ◽  
pp. 169-182 ◽  
Author(s):  
J A Rossiter ◽  
B G Grinnell
Keyword(s):  
Frequency Domain ◽  
Predictive Control ◽  
Time Domain ◽  
Degree Of Freedom ◽  
Generalized Predictive Control ◽  
Control Law ◽  
Set Point ◽  
The Poor ◽  
Fixed Order ◽  
Two Degree Of Freedom

One of the advantages of predictive control is its ability to take optimal account of information about future set point changes in the specification of the control law. However, the optimum GPC (generalized predictive control) prefilter that uses this information can lead to a deterioration rather than an improvement in the accuracy of tracking. Some simple modifications to GPC to overcome this problem are discussed. It will then be shown how some simple algorithms can be used to design an optimal prefilter that does not have any of the poor effects arising from the standard choice and hence always improves the performance. The basis of the technique is analogous to the two-degree-of-freedom designs common in the literature on H∞. However, here the emphasis is on fixed-order prefilters designed from a time domain, not a frequency domain, objective.

scholarly journals Velocity control of a two-wheeled inverted pendulum mobile robot: a fuzzy model-based approach

2019 ◽  
Vol 8 (3) ◽  
pp. 808-817
Author(s):  
Mustapha Muhammad ◽  
Amir A. Bature ◽  
Umar Zangina ◽  
Salinda Buyamin ◽  
Anita Ahmad ◽  
...  
Keyword(s):  
Lyapunov Function ◽  
Mobile Robot ◽  
Inverted Pendulum ◽  
Fuzzy Model ◽  
Control Law ◽  
Velocity Control ◽  
Wheeled Inverted Pendulum ◽  
Fuzzy Lyapunov Function ◽  
Tracking Controller ◽  
Velocity Tracking

This paper presents the design of a fuzzy tracking controller for balancing and velocity control of a Two-Wheeled Inverted Pendulum (TWIP) mobile robot based on its Takagi-Sugino (T-S) fuzzy model, fuzzy Lyapunov function and non-parallel distributed compensation (non-PDC) control law. The T-S fuzzy model of the TWIP mobile robot was developed from its nonlinear dynamical equations of motion. Stabilization conditions in a form of linear matrix inequalities (LMIs) were derived based on the T-S fuzzy model of the TWIP mobile robot, a fuzzy Lyapunov function and a non-PDC control law. Based on the derived stabilization conditions and the T-S fuzzy model of the TWIP mobile robot, a state feedback velocity tracking controller was then proposed for the TWIP mobile robot. The balancing and velocity tracking performance of the proposed controller was investigated via simulations. The simulation result shows the effectiveness of the proposed control scheme.

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