Velocity Control of a Spherical Rolling Robot Using a Grey-PID Type Fuzzy Controller With an Adaptive Step Size

Kayacan, Erkan; Ramón, Herman; Saeys, Wouter

doi:10.3182/20120905-3-hr-2030.00123

Cited by 18 publications

(11 citation statements)

References 11 publications

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“…By using a 3D virtual prototype, the free fall bouncing, shooting up, and the projectile scenario was presented to show the feasibility of the proposed method. Kayacan 2012 [8] designed a grey-PID-Fuzzy Controller with an adaptive step size to control the velocity of a spherical robot which is considered as a nonlinear system. Fuzzy controller design was intended to tune the step size of the grey predictor.…”

Section: Path Planning and Trajectory Tracking Control Discussionmentioning

confidence: 99%

The Kinematics and Dynamics Motion Analysis of a Spherical Robot

Dewi¹,

Risma²,

Oktarina³

et al. 2019

EECSI

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Mobile robot application has reach more aspect of life in industry and domestic. One of the mobile robot types is a spherical robot whose components are shielded inside a rigid cell. The spherical robot is an interesting type of robot that combined the concept of a mobile robot and inverted pendulum for inner mechanism. This combination adds to more complex controller design than the other type of mobile robots. Aside from these challenges, the application of a spherical robot is extensive, from being a simple toy, to become an industrial surveillance robot. This paper discusses the mathematical analysis of the kinematics and dynamics motion analysis of a spherical robot. The analysis combines mobile robot and pendulum modeling as the robot motion generated by a pendulum mechanism. This paper is expected to give a complete discussion of the kinematics and dynamics motion analysis of a spherical robot.

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Section: Path Planning and Trajectory Tracking Control Discussionmentioning

confidence: 99%

The Kinematics and Dynamics Motion Analysis of a Spherical Robot

Dewi¹,

Risma²,

Oktarina³

et al. 2019

EECSI

View full text Add to dashboard Cite

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“…C ONTROL of uncertain nonlinear systems is one of the most important topics in modern control engineering [1]- [7]. Controllers intend to achieve the best control performance in the presence of the worst uncertainties in robust control approaches, and a high controller gain is the general method to handle the effect of uncertainties in nonlinear control theory [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Closed-Loop Error Learning Control for Uncertain Nonlinear Systems With Experimental Validation on a Mobile Robot

Kayacan

2019

IEEE/ASME Trans. Mechatron.

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This paper develops a Closed-Loop Error Learning Control (CLELC) algorithm for feedback linearizable systems with experimental validation on a mobile robot. Traditional feedback and feedforward controllers are designed based on the nominal model by using Feedback Linearization Control (FLC) method. Then, an intelligent controller is designed based on sliding mode learning algorithm that utilizes closed-loop error dynamics to learn the system behavior. The controllers are working in parallel, and the intelligent controller can gradually replace the feedback controller from the control of the system. In addition to the stability of the sliding mode learning algorithm, the closed-loop stability of an nth order feedback linearizable system is proven. The simulation results demonstrate that CLELC algorithm can improve control performance (e.g., smaller rise time, settling time and overshoot) in the absence of uncertainties, and also provides robust control performance in the presence of uncertainties as compared to traditional FLC method. To test the efficiency and efficacy of CLELC algorithm, the trajectory tracking problem of a tracked mobile robot is studied in real-time. The experimental results demonstrate that CLELC algorithm ensures high-accurate trajectory tracking performance than traditional FLC method.

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“…One of the primary demands for control systems is to be insensitive to matched and mismatched disturbances (Asl et al 2017;Kayacan 2017;Kayacan et al 2012Kayacan et al , 2013Kayacan et al , 2014Kayacan et al , 2016bRahmani et al 2016c;Yan and Liao 2018). Sliding mode control (SMC) has been studied and applied in industrial applications due to its advantage of robustness against parameter variations and disturbances (Precup et al 2017;Yu and Kaynak 2009).…”

Section: Introductionmentioning

confidence: 99%

Sliding mode control for systems with mismatched time-varying uncertainties via a self-learning disturbance observer

Kayacan

2018

Transactions of the Institute of Measurement and Control

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This paper presents a novel sliding mode control (SMC) algorithm to handle mismatched uncertainties in systems via a novel self-learning disturbance observer (SLDO). A computationally efficient SLDO is developed within a framework of feedback-error learning scheme in which a conventional estimation law and a neuro-fuzzy structure (NFS) work in parallel. In this framework, the NFS estimates the mismatched disturbances and becomes the leading disturbance estimator while the former feeds the learning error to the NFS to learn system behaviour. The simulation results demonstrate that the proposed SMC based on SLDO (SMC-SLDO) ensures robust control performance in the presence of mismatched time-varying uncertainties when compared to SMC, integral SMC (ISMC) and SMC based on a basic nonlinear disturbance observer (SMC-BNDO), and also remains the nominal control performance in the absence of mismatched uncertainties. Additionally, the SMC-SLDO not only counteracts mismatched time-varying uncertainties, but also improve the transient response performance in the presence of mismatched time-invariant uncertainties. Moreover, the controller gain of the SMC-SLDO is required to be selected larger than the upper bound of the disturbance estimation error rather than the upper bound of the actual disturbance to guarantee system stability, which results in eliminating the chattering effects on the control signal.

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Velocity Control of a Spherical Rolling Robot Using a Grey-PID Type Fuzzy Controller With an Adaptive Step Size

Cited by 18 publications

References 11 publications

The Kinematics and Dynamics Motion Analysis of a Spherical Robot

The Kinematics and Dynamics Motion Analysis of a Spherical Robot

Closed-Loop Error Learning Control for Uncertain Nonlinear Systems With Experimental Validation on a Mobile Robot

Sliding mode control for systems with mismatched time-varying uncertainties via a self-learning disturbance observer

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