Adaptive Moving-Target Tracking Control of a Vision-Based Mobile Robot via a Dynamic Petri Recurrent Fuzzy Neural Network

Wai, Rong‐Jong; Lin, You-Wei

doi:10.1109/tfuzz.2012.2227974

Cited by 72 publications

(26 citation statements)

References 27 publications

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“…e /2 [32]; α and β are positive constants and set to be 0.15 and 4, respectively, by empirical rules. The token can be removed from its input place to output place when the transition is fired.…”

Section: Membership Layermentioning

confidence: 99%

A Three-Phase Four-Leg Inverter-Based Active Power Filter for Unbalanced Current Compensation Using a Petri Probabilistic Fuzzy Neural Network

2017

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Abstract:A three-phase four-leg inverter-based shunt active power filter (APF) is proposed to compensate three-phase unbalanced currents under unbalanced load conditions in grid-connected operation in this study. Since a DC-link capacitor is required on the DC side of the APF to release or absorb the instantaneous apparent power, the regulation control of the DC-link voltage of the APF is important especially under load variation. In order to improve the regulation control of the DC-link voltage of the shunt APF under variation of three-phase unbalanced load and to compensate the three-phase unbalanced currents effectively, a novel Petri probabilistic fuzzy neural network (PPFNN) controller is proposed to replace the traditional proportional-integral (PI) controller in this study. Furthermore, the network structure and online learning algorithms of the proposed PPFNN are represented in detail. Finally, the effectiveness of the three-phase four-leg inverter-based shunt APF with the proposed PPFNN controller for the regulation of the DC-link voltage and compensation of the three-phase unbalanced current has been demonstrated by some experimental results.

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Section: Membership Layermentioning

confidence: 99%

A Three-Phase Four-Leg Inverter-Based Active Power Filter for Unbalanced Current Compensation Using a Petri Probabilistic Fuzzy Neural Network

2017

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“…In addition, the sharing function k d can be defined as in (13). Therefore, the overall shared-control for the system (2) is given, similarly to (14), by…”

Section: B Shared Controlmentioning

confidence: 99%

“…For instance, [9] has used nonlinear H ∞ control via quasi-linear parameter varying representation to control wheeled robots and [10] has presented an implementation of integral sliding mode controller on a two-wheeled mobile robot. Other control methods, such as back-stepping control [11], adaptive control [12], [13], fuzzy control [14], [15] and control based on the representation as chained system [16], [17], [18], have also been explored and implemented.…”

Section: Introductionmentioning

confidence: 99%

Shared Control for the Kinematic and Dynamic Models of a Mobile Robot

Jiang

Franco

Astolfi

2016

IEEE Trans. Contr. Syst. Technol.

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Abstract-This paper presents shared-control algorithms for the kinematic and the dynamic models of a mobile robot with a feasible configuration set defined by means of linear inequalities. The shared-control laws based on a hysteresis switch are designed in the case in which absolute positions are not available. Instead, we measure the distances to obstacles and angular differences. Formal properties of the closed-loop systems with the sharedcontrol are established by a Lyapunov-like analysis. Simulation results and experimental results are presented to show the effectiveness of the algorithm.

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“…track a given geometry path. Based on adaptive control [6][7][8][9], sliding mode control [10][11][12][13], back-stepping design [14][15], PID control, fuzzy control and high gain control, many trajectory tracking controller were obtained for mobile robot [16][17][18][19]. A smooth linear or non-linear function is often used to describe the desired trajectory when presenting and analyzing problems.…”

Section: Introductionmentioning

confidence: 99%

Robot Trajectory Tracking Control Based on Look-up Table Method

Yang¹,

Zhao²,

Song³

et al. 2018

dteees

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Abstract. This paper proposed a simple and applicable look-up table based trajectory tracking control scheme for robot. Based on the robot's kinematics model and the saturation constraint of the control inputs, a control-trajectory table was established using the European numerical calculation method. The optimal control values can be determined under the look-up table criteria. Finally, the simulation results of trajectories with different characteristics of straight line, sine line and circle show the effectiveness of the proposed scheme.

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Adaptive Moving-Target Tracking Control of a Vision-Based Mobile Robot via a Dynamic Petri Recurrent Fuzzy Neural Network

Cited by 72 publications

References 27 publications

A Three-Phase Four-Leg Inverter-Based Active Power Filter for Unbalanced Current Compensation Using a Petri Probabilistic Fuzzy Neural Network

A Three-Phase Four-Leg Inverter-Based Active Power Filter for Unbalanced Current Compensation Using a Petri Probabilistic Fuzzy Neural Network

Shared Control for the Kinematic and Dynamic Models of a Mobile Robot

Robot Trajectory Tracking Control Based on Look-up Table Method

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