Simultaneous Stabilization and Tracking of Nonholonomic Mobile Robots: A Lyapunov-Based Approach

Wang, Yaonan; Miao, Zhiqiang; Zhong, Huicai; Pan, Qi

doi:10.1109/tcst.2014.2375812

Cited by 145 publications

(100 citation statements)

References 45 publications

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“…Por otro lado, Wang et al en [10] presentaron un control visual, para el seguimiento de trayectorias de RMRs diferenciales, sin medición directa de la posición vía un algoritmo adaptivo. Wang et al en [11] desarrollaron un control suave variante en el tiempo basado en el método de Lyapunov para regulación y seguimiento de un RMR. Liang et al en [12] diseñaron un control adaptivo basado en un sistema de visión que no requiere el conocimiento exacto de la posición de la cámara.…”

Section: A Modelo Cinemático De La Estructura Mecánicaunclassified

Assessment of an Average Tracking Controller that Considers all the Subsystems Involved in a WMR: Implementation via PWM or Sigma-Delta Modulation

Sánchez

Tavera-Mosqueda

Silva‐Ortigoza

et al. 2016

IEEE Latin Am. Trans.

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This paper has two aims; the first, to present the design of a three-level average controller (that do not require electromechanical sensors) for the trajectory tracking task in a differential drive wheeled mobile robot (WMR). Such a controller considers, for the first time in literature, the dynamics of the three subsystems composing a WMR, i.e., mechanical structure, actuators, and power stage. The proposed controller is designed as follows: At the high level, a kinematic control for the mechanical structure is proposed. At the medium level, a control based on differential flatness for the actuators is presented. And at the low level, an average flatness control for the power is proposed. The second aim of this paper is to present an assessment, via numerical simulations using MATLAB-Simulink, of the three-level average controller performance when it is implemented using a PWM or a Σ-Δ-modulator. In these simulations the robustness of the controller is shown when multiple abrupt variations are considered in some parameters of the WMR.

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Section: A Modelo Cinemático De La Estructura Mecánicaunclassified

Assessment of an Average Tracking Controller that Considers all the Subsystems Involved in a WMR: Implementation via PWM or Sigma-Delta Modulation

Sánchez

Tavera-Mosqueda

Silva‐Ortigoza

et al. 2016

IEEE Latin Am. Trans.

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“…The authors of [18] proposed a Model Predictive Control (MPC) approach and Kalman filter-based strategy was presented in [19]. Lyapunovbased scheme was reported in [20] and controller design by approximate linearization utilizing Taylor expansion was proposed in [21]. In order to enhance the perfomance of traditional control methods, Least Square Policy Iteration (LSPI) and Dynamic Heuristic Programming (DHP) algorithms were utilized in [22] for optimizing a Proportional-Derivative (PD) controller.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Control Optimization with Neural Network for Autonomous Vehicles

Bamgbose¹,

Li²

2019

Adv. sci. technol. eng. syst. j.

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For mission-critical and time-sensitive navigation of autonomous vehicles, controller design must exhibit excellent tracking performance with respect to the speed of convergence to reference command and steady-state accuracy. In this article, a novel design integration of the neural network with the traditional control system is proposed to adaptively obtain optimized controller parameters resulting in improved transient and steady-state performance of motion and position control of autonomous vehicles. Application of the proposed intelligent control scheme to mobile robot navigation was presented for an eightshaped trajectory by optimizing a Lyapunov-based nonlinear controller. Furthermore, a Linear Quadratic Regulator-based controller was optimized based on the proposed strategy to control the pitch and yaw angles of a 2-Degree-of -Freedom helicopter. The simulation results showed that the proposed scheme outperforms the traditional controllers in terms of the speed of convergence to the desired trajectory and overall error minimization.In order to execute maneuvers for unmanned Air Vehicles (UAVs), feedback linearization [24,25] and sliding mode control [26] have been proposed, but they have failed for certain models due to the nonlinear dynamics of the vehicle [27]. For helicopter control, backstepping control strategy [28] and Linear Quadratic Regulator (LQR)-based control [29] have been proposed.

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“…In recent years, various control design techniques have been proposed for nonholonomic systems in chained form, and much effort has been devoted to the stabilization and tracking control, see [5,7,10,19,20]. Among the techniques mentioned in the literature, Sliding Mode Control (SMC) has attracted much attention of the researchers due to its simplicity, fast response, and robustness to external noise and parameter variation [1,11,21].…”

Section: Introductionmentioning

confidence: 99%

Smooth super twisting sliding mode based steering control for nonholonomic systems transformable into chained form

Abbasi¹,

Rehman²,

Shah³

2018

Kybernetika

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Simultaneous Stabilization and Tracking of Nonholonomic Mobile Robots: A Lyapunov-Based Approach

Cited by 145 publications

References 45 publications

Assessment of an Average Tracking Controller that Considers all the Subsystems Involved in a WMR: Implementation via PWM or Sigma-Delta Modulation

Assessment of an Average Tracking Controller that Considers all the Subsystems Involved in a WMR: Implementation via PWM or Sigma-Delta Modulation

Trajectory Tracking Control Optimization with Neural Network for Autonomous Vehicles

Smooth super twisting sliding mode based steering control for nonholonomic systems transformable into chained form

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