Optimal Control of a Wheeled Robot

Hendzel, Zenon; Penar, Paweł

doi:10.1007/978-3-030-13273-6_44

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…The combination of maneuverability, cost-effectiveness, and ease of integration positions differential drive mobile robots as a versatile and efficient solution in the ever-evolving landscape of robotics and automation. In order to control the performance variables of such robotic vehicles, several control approaches have been proposed, including, among many, PID type of controllers (see [10][11][12]), linear static state feedback controllers (see [5]), optimal type of controllers (see [13][14][15][16]), predictive controllers (see [17,18]), and fuzzy (see [19,20]) and adaptive controllers (see [21,22]).…”

Section: Introductionmentioning

confidence: 99%

A Two Stage Nonlinear I/O Decoupling and Partially Wireless Controller for Differential Drive Mobile Robots

Kouvakas,

Koumboulis,

Sigalas

2024

Robotics

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Differential drive mobile robots, being widely used in several industrial and domestic applications, are increasingly demanding when concerning precision and satisfactory maneuverability. In the present paper, the problem of independently controlling the velocity and orientation angle of a differential drive mobile robot is investigated by developing an appropriate two stage nonlinear controller embedded on board and also by using the measurements of the speed and accelerator of the two wheels, as well as taking remote measurements of the orientation angle and its rate. The model of the system is presented in a nonlinear state space form that includes unknown additive terms arising from external disturbances and actuator faults. Based on the nonlinear model of the system, the respective I/O relation is derived, and a two-stage nonlinear measurable output feedback controller, analyzed into an internal and an external controller, is designed. The internal controller aims to produce a decoupled inner closed-loop system of linear form, regulating the linear velocity and angular velocity of the mobile robot independently. The internal controller is of the nonlinear PD type and uses real time measurements of the angular velocities of the active wheels of the vehicle, as well as the respective accelerations. The external controller aims toward the regulation of the orientation angle of the vehicle. It is of a linear, delayed PD feedback form, offering feedback from the remote measurements of the orientation angle and angular velocity of the vehicle, which are transmitted to the controller through a wireless network. Analytic formulae are derived for the parameters of the external controller to ensure the stability of the closed-loop system, even in the presence of the wireless transmission delays, as well as asymptotic command following for the orientation angle. To compensate for measurement noise, external disturbances, and actuator faults, a metaheuristic algorithm is proposed to evaluate the remaining free controller parameters. The performance of the proposed control scheme is evaluated through a series of computational experiments, demonstrating satisfactory behavior.

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Section: Introductionmentioning

confidence: 99%

A Two Stage Nonlinear I/O Decoupling and Partially Wireless Controller for Differential Drive Mobile Robots

Kouvakas,

Koumboulis,

Sigalas

2024

Robotics

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“…The new method has a low worst-cost ration than nonlinear model predictive control method (NMPC), but cannot ensure safety in its running because no physical limits are considered. In Reference [9], an adaptive optimal control method is presented to solve the H∞ problem under changing environmenalt conditions, which can adapt to a complex environment, but it seems that the gradient-descent based solver is used to solve the optimal problem.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Model Predictive Control for Mobile Robot Using Varying-Parameter Convergent Differential Neural Network

Zhang

et al. 2019

Robotics

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The mobile robot kinematic model is a nonlinear affine system, which is constrained by velocity and acceleration limits. Therefore, the traditional control methods may not solve the tracking problem because of the physical constraint. In this paper, we present the nonlinear model predictive control (NMPC) algorithm to track the desired trajectory based on neural-dynamic optimization. In the proposed algorithm, the NMPC scheme utilizes a new neural network named the varying-parameter convergent differential neural network (VPCDNN) which is a Hopfifield-neural network structure with respect to the differential equation theory to solve the quadratic programming (QP) problem. The new network structure converges to the global optimal solution and it is more efficient than traditional numerical methods. In the simulation, we verify that the proposed method is able to successfully track reference trajectories with a two-wheel mobile robot. The experimental validation has been conducted in simulation and the results show that the proposed method is able to precisely track the trajectory maintaining a high robustness based on the VPCDNN solver.

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Optimal Control of a Wheeled Robot

Cited by 2 publications

References 11 publications

A Two Stage Nonlinear I/O Decoupling and Partially Wireless Controller for Differential Drive Mobile Robots

A Two Stage Nonlinear I/O Decoupling and Partially Wireless Controller for Differential Drive Mobile Robots

Nonlinear Model Predictive Control for Mobile Robot Using Varying-Parameter Convergent Differential Neural Network

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