Trajectory tracking of an omnidirectional mobile robot using Gaussian process regression

2021 ◽  
Vol 69 (8) ◽  
pp. 656-666
Author(s):  
Hannes Eschmann ◽  
Henrik Ebel ◽  
Peter Eberhard
Keyword(s):  
Mobile Robots ◽  
Gaussian Process ◽  
Trajectory Tracking ◽  
Closed Loop ◽  
Gaussian Process Regression ◽  
Industrial Applications ◽  
Loop Control ◽  
Common Control ◽  
Model Extension ◽  
Flexible Operation

Abstract Mobile robots are enjoying increasing popularity in a number of different automation tasks. Omnidirectional mobile robots especially allow for a very flexible operation. They are able to accelerate in every direction, regardless of their orientation. In this context, we developed our own robot platform for research on said types of robots. It turns out that these mobile robots show interesting behaviour, which commonly used models for omnidirectional mobile robots fail to reproduce. As the exact sources and structures of mismatches are still unknown, non-parametric Gaussian process regression is used to develop a data-based model extension of the robot. A common control task for industrial applications is trajectory tracking, where a robot needs to follow a predefined path, for example in a warehouse, as close as possible in space and time. Appropriate feed-forward solutions for the data-based model are developed and finally leveraged in closed-loop control via nonlinear model predictive control. In real-world experiments, the results are compared to commonly used proportional position-based feedback. This novel contribution builds upon the preliminary work in [7] but, for the first time, includes also closed-loop (trajectory) tracking.

scholarly journals Formation Control and Distributed Goal Assignment for Multi-Agent Non-Holonomic Systems

2019 ◽  
Vol 9 (7) ◽  
pp. 1311 ◽  
Author(s):  
Wojciech Kowalczyk
Keyword(s):  
Mobile Robots ◽  
Collision Avoidance ◽  
Trajectory Tracking ◽  
Formation Control ◽  
Closed Loop ◽  
Linear Feedback ◽  
Planning Problem ◽  
Closed Loop Control ◽  
Loop Control ◽  
Loop Control System

This paper presents control algorithms for multiple non-holonomic mobile robots moving in formation. Trajectory tracking based on linear feedback control is combined with inter-agent collision avoidance. Artificial potential functions (APF) are used to generate a repulsive component of the control. Stability analysis is based on a Lyapunov-like function. Then the presented method is extended to include a goal exchange algorithm that makes the convergence of the formation much more rapid and, in addition, reduces the number of collision avoidance interactions. The extended method is theoretically justified using a Lyapunov-like function. The controller is discontinuous but the set of discontinuity points is of zero measure. The novelty of the proposed method lies in integration of the closed-loop control for non-holonomic mobile robots with the distributed goal assignment, which is usually regarded in the literature as part of trajectory planning problem. A Lyapunov-like function joins both trajectory tracking and goal assignment analyses. It is shown that distributed goal exchange supports stability of the closed-loop control system. Moreover, robots are equipped with a reactive collision avoidance mechanism, which often does not exist in the known algorithms. The effectiveness of the presented method is illustrated by numerical simulations carried out on the large formation of robots.

scholarly journals Performance Evaluation of Closed-loop Industrial Applications Over Imperfect Networks

2019 ◽  
pp. 32-38
Author(s):  
Sándor Rácz ◽  
Géza Szabó ◽  
József Pető
Keyword(s):  
Closed Loop ◽  
Industrial Robot ◽  
Industrial Applications ◽  
Maximum Speed ◽  
Closed Loop Control ◽  
Robot Arm ◽  
5G Networks ◽  
Network Delay ◽  
Performance Impact ◽  
Loop Control

5G networks provide technology enablers targeting industrial applications. One key enabler is the Ultra Reliable Low Latency Communication (URLLC). This paper studies the performance impact of network delay on closed-loop control for industrial applications. We investigate the performance of the closed-loop control of an UR5 industrial robot arm assuming fix delay. The goal is to stress the system at the upper limit of the possible network delay. We prove that to achieve the maximum speed, URLLC is a must have.

Fuzzy-Based Matrix Converter Drive for Induction Motor

2017 ◽  
pp. 219-245
Author(s):  
Chitra Venugopal
Keyword(s):  
Fuzzy Logic ◽  
Induction Motor ◽  
Closed Loop ◽  
Electrical Energy ◽  
Industrial Applications ◽  
Fuzzy Controllers ◽  
Loop Control ◽  
Squirrel Cage ◽  
Save Energy ◽  
Fuzzy Logic Technique

In industrial applications, approximately, 60% of world's consumption of electrical energy passes through the windings of squirrel-cage induction motors. Hence it is necessary to select an efficient drive circuit for induction motor to save energy. The MC are preferred to replace VSC in industrial applications. To control the performance of the MC, fuzzy logic technique is proposed and simulated using Matlab/Simulink. In this chapter, the basic concepts of MCs are discussed. The implementation of fuzzy logic technique to improve the performance of MC in driving induction motor is discussed in detail. The design of fuzzy controllers and the closed loop control of induction motor is shown. It seen that the introduction of fuzzy controllers in the closed loop helped to reduce the overshoot at starting and maintain the reference speed when running with load torque. Also the input and output voltage of the MC is maintained sinusoidal.

scholarly journals Implicit Euler Implementation of Twisting Controller and Super-Twisting Observer without Numerical Chattering: Precise Quasi-Static MEMS Mirrors Control

2019 ◽  
Vol 256 ◽  
pp. 03004 ◽  
Author(s):  
Dong Luo ◽  
Xiaogang Xiong ◽  
Shanhai Jin ◽  
Wei Chen
Keyword(s):  
Sliding Mode Control ◽  
Closed Loop ◽  
Sliding Mode ◽  
Industrial Applications ◽  
Euler Method ◽  
Open Loop ◽  
Closed Loop Control ◽  
Control Input ◽  
Loop Control ◽  
Mode Control

The quasi-static operations of MEMS mirror are very sensitive to undesired oscillations due to its very low damping. It has been shown that closed-loop control can be superior to reduce those oscillations than open-loop control in the literature. For the closed-loop control, the conventional way of implementing sliding mode control (SMC) algorithm is forward Euler method, which results in numerical chattering in the control input and output. This paper proposes an implicit Euler implementation scheme of super twisting observer and twisting control for a commercial MEMS mirror actuated by an electrostatic staggered vertical comb (SVC) drive structure. The famous super-twisting algorithm is used as an observer and twisting SMC is used as a controller. Both are discretized by an implicit Euler integration method, and their implementation algorithms are provided. Simulations verify that, as compared to traditional sliding mode control implementation, the proposed scheme reduces the chattering both in trajectory tracking output and control input in presence of model uncertainties and external disturbances. The comparison demonstrates the potential applications of the proposed scheme in industrial applications in terms of feasibility and performance.

scholarly journals Controller parameter optimization for complex industrial system with uncertainties

2019 ◽  
Vol 52 (7-8) ◽  
pp. 888-895
Author(s):  
Heping Chen ◽  
Seth Bowels ◽  
Biao Zhang ◽  
Thomas Fuhlbrigge
Keyword(s):  
Gaussian Process ◽  
Parameter Optimization ◽  
Optimization Algorithm ◽  
Gaussian Process Regression ◽  
Optimization Method ◽  
Industrial Applications ◽  
Bayesian Optimization ◽  
Potential Candidate ◽  
Bayesian Optimization Algorithm ◽  
Controller Parameter

Proportional–integral–derivative control system has been widely used in industrial applications. For complex systems, tuning controller parameters to satisfy the process requirements is very challenging. Different methods have been proposed to solve the problem. However these methods suffer several problems, such as dealing with system complexity, minimizing tuning effort and balancing different performance indices including rise time, settling time, steady-state error and overshoot. In this paper, we develop an automatic controller parameter optimization method based on Gaussian process regression Bayesian optimization algorithm. A non-parametric model is constructed using Gaussian process regression. By combining Gaussian process regression with Bayesian optimization algorithm, potential candidate can be predicted and applied to guide the optimization process. Both experiments and simulation were performed to demonstrate the effectiveness of the proposed method.

Research on the Cooperative Control Method for Trajectory Tracking of Automatic Guided Vehicle Based on Dynamic Position/Force

2013 ◽  
Vol 457-458 ◽  
pp. 1298-1302 ◽  
Author(s):  
Xuan Zuo Liu ◽  
Qiao Yun Yan ◽  
Fei Yun Tang
Keyword(s):  
Trajectory Tracking ◽  
Cooperative Control ◽  
Closed Loop ◽  
Control Structure ◽  
Sliding Mode ◽  
Closed Loop Control ◽  
Control Input ◽  
External Interference ◽  
Loop Control ◽  
Automatic Guided Vehicle

AbstractConsidering the influence of the dynamic characteristic of automatic guided vehicle (AGV) on trajectory tracking controlling, double closed loop control structure is proposed to realize the position/force cooperative control. The outer loop controlling uses backstepping to design corresponding position controller for kinematics model of AGV, while the inner control uses the integral sliding mode controlling. Self-adaptive controlling law is used to estimate the uncertain external interference in the driving force controller and stability of AGV trajectories tracking proof is proposed. In order to make the system achieve better control performance and prevent the occurrence of severe wobble, the hyperbolic tangent function in the control law of sliding mode control replaces the sign function to ensure a continuously smooth control input and states of the system. In the Matlab/simulink environment, tracking a given splayed trajectory generated by the S function to verify the double closed loop control structure and the effectiveness of the control algorithm proposed in this paper.

Fuzzy-Based Matrix Converter Drive for Induction Motor

Fuzzy Systems ◽  
2017 ◽  
pp. 738-762
Author(s):  
Chitra Venugopal
Keyword(s):  
Fuzzy Logic ◽  
Induction Motor ◽  
Closed Loop ◽  
Electrical Energy ◽  
Industrial Applications ◽  
Fuzzy Controllers ◽  
Loop Control ◽  
Squirrel Cage ◽  
Save Energy ◽  
Fuzzy Logic Technique

In industrial applications, approximately, 60% of world's consumption of electrical energy passes through the windings of squirrel-cage induction motors. Hence it is necessary to select an efficient drive circuit for induction motor to save energy. The MC are preferred to replace VSC in industrial applications. To control the performance of the MC, fuzzy logic technique is proposed and simulated using Matlab/Simulink. In this chapter, the basic concepts of MCs are discussed. The implementation of fuzzy logic technique to improve the performance of MC in driving induction motor is discussed in detail. The design of fuzzy controllers and the closed loop control of induction motor is shown. It seen that the introduction of fuzzy controllers in the closed loop helped to reduce the overshoot at starting and maintain the reference speed when running with load torque. Also the input and output voltage of the MC is maintained sinusoidal.

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