Trajectory-Tracking Control of Mobile Robot Systems Incorporating Neural-Dynamic Optimized Model Predictive Approach

Li, Zhijun; Deng, Jun; Lu, Renquan; Bai, Jianjun; Su, Chun‐Yi

doi:10.1109/tsmc.2015.2465352

Cited by 334 publications

(112 citation statements)

References 40 publications

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“…Given the satisfactory numerical results for solving tracking and stabilization problems of vehicles with Ackermann steering, the proposed feedback law can be qualitatively compared with the model predictive control law presented in [27] in that the proposed feedback law does not rely on the complexity of the reference trajectory of the vehicle as opposed to [27].…”

Section: Trajectory Trackingmentioning

confidence: 99%

“…In most cases, see [26,27], for example, the reference trajectory has to be satisfied by the robot's kinematic model which is not the case considered in the present work.…”

Section: Trajectory Trackingmentioning

confidence: 99%

“…Model predictive control techniques are quite popular and have been extensively used for solving tracking problems of mobile robots in the optimal control literature, see [22,23,24], however, they suffer from defining appropriate feedback laws for partially observed states. Authors in [25,26,27] have recently adopted model predictive control laws for solving the tracking problems of nonholonomic systems. Their results are satisfactory at the cost of hardware needed to implement the control laws.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Linear Time-Varying Feedback Law for Vehicles With Ackermann Steering

Miah¹,

Farkas²,

Gueaieb³

et al. 2017

International Journal of Robotics and Automation

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In this paper, we propose an optimal state feedback control law for addressing point stabilization and tracking problems of nonholonomic vehicles with Ackermann steering in a unified manner. Unlike other feedback controllers that perform dynamic linearization of vehicle models, the proposed optimal feedback controller provides the state feedback control to the original nonlinear vehicle model for achieving excellent state-tracking performance.In addition, nonlinear control techniques suggested in the literature to date require that the desired trajectory of the robot is generated using persistently excited inputs. This may be too restrictive and non-realistic hypothesis to mimic a real scenario. Here, we address this issue by developing a smooth state-feedback control law that is formulated by modifying the classical Pontryagin's minimum principle. The proposed control law can be applied for solving control problems of a general class of nonlinear affine systems. The proposed control scheme offers a modular solution to other control techniques for a large number of mobile robot applications. The theoretical results are validated through computer simulations.

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Section: Trajectory Trackingmentioning

confidence: 99%

“…In most cases, see [26,27], for example, the reference trajectory has to be satisfied by the robot's kinematic model which is not the case considered in the present work.…”

Section: Trajectory Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Linear Time-Varying Feedback Law for Vehicles With Ackermann Steering

Miah¹,

Farkas²,

Gueaieb³

et al. 2017

International Journal of Robotics and Automation

View full text Add to dashboard Cite

show abstract

“…Recently, a lot of research have been conducted in order to control mobile robots [1], [2] and [3]. The mobile robots with a steering wheel (unicycle) or two independent drive wheels like "TURTLEBOT" are examples with considerably important engineering interest.…”

Section: Introductionmentioning

confidence: 99%

Trajectory tracking control for a nonholonomic mobile robot under ROS

Besseghieur

Trȩbiński

Kaczmarek

et al. 2018

J. Phys.: Conf. Ser.

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“…However, those articles do not consider the interference of unknown functions. In [38], considering a class of special nonlinear systems with the hysteretic output mechanism and the unmeasured states, adaptive neural output feedback control is presented to guarantee the prescribed convergence of the tracking error [39]. The article in [40] has studied adaptive neural control of uncertain stochastic nonlinear systems with dead zone and output constraint.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

2017

Complexity

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An adaptive neural network control problem is addressed for a class of nonlinear hydraulic servo-systems with time-varying state constraints. In view of the low precision problem of the traditional hydraulic servo-system which is caused by the tracking errors surpassing appropriate bound, the previous works have shown that the constraint for the system is a good way to solve the low precision problem. Meanwhile, compared with constant constraints, the time-varying state constraints are more general in the actual systems. Therefore, when the states of the system are forced to obey bounded time-varying constraint conditions, the high precision tracking performance of the system can be easily realized. In order to achieve this goal, the time-varying barrier Lyapunov function (TVBLF) is used to prevent the states from violating time-varying constraints. By the backstepping design, the adaptive controller will be obtained. A radial basis function neural network (RBFNN) is used to estimate the uncertainties. Based on analyzing the stability of the hydraulic servo-system, we show that the error signals are bounded in the compacts sets; the time-varying state constrains are never violated and all singles of the hydraulic servo-system are bounded. The simulation and experimental results show that the tracking accuracy of system is improved and the controller has fast tracking ability and strong robustness.

show abstract

Trajectory-Tracking Control of Mobile Robot Systems Incorporating Neural-Dynamic Optimized Model Predictive Approach

Cited by 334 publications

References 40 publications

Linear Time-Varying Feedback Law for Vehicles With Ackermann Steering

Linear Time-Varying Feedback Law for Vehicles With Ackermann Steering

Trajectory tracking control for a nonholonomic mobile robot under ROS

Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

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