On Output Feedback Trajectory Tracking Control of an Omni-Mobile Robot

Andreev, Aleksandr; Peregudova, Olga

doi:10.1016/j.ifacol.2019.08.045

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Thus, the comparative analysis has confirmed that the proposed controller (3.16), (3.17), (4.5) provides better performance than that of [24]. Compare also the performances of the controller (3.16)- (3.17) and that obtained in [6] and expressed as follows:…”

supporting

confidence: 60%

“…To date, a nonlinear PI controller has been proposed in [5] to solve the position stabilization problem. The problem of trajectory tracking control design for such a robot model was considered in the previous papers of the authors [6,7] wherein the output position feedback controllers were constructed taking into account such features as periodicity of the model equations in the angular coordinate of the robot as well as the presence of delay in the control feedback.…”

Section: Introductionmentioning

confidence: 99%

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On Global Trajectory Tracking Control for an Omnidirectional Mobile Robot with a Displaced Center of Mass

Andreev¹,

Peregudova²

2020

Nelin. Dinam.

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This paper addresses the trajectory tracking control design of an omnidirectional mobile robot with a center of mass displaced from the geometrical center of the robot platform. Due to the high maneuverability provided by omniwheels, such robots are widely used in industry to transport loads in narrow spaces. As a rule, the center of mass of the load does not coincide with the geometric center of the robot platform. This makes the trajectory tracking control problem of a robot with a displaced center of mass relevant. In this paper, two controllers are constructed that solve the problem of global trajectory tracking control of the robot. The controllers are designed based on the Lyapunov function method. The main difficulty in applying the Lyapunov function method for the trajectory tracking control problem of the robot is that the time derivative of the Lyapunov function is not definite negative, but only semidefinite negative. Moreover, the LaSalle invariance principle is not applicable in this case since the closed-loop system is a nonautonomous system of differential equations. In this paper, it is shown that the quasi-invariance principle for nonautonomous systems of differential equations is much convenient for the asymptotic stability analysis of the closed-loop system. Firstly, we construct an unbounded state feedback controller such as proportional-derivative term plus feedforward. As a result, the global uniform asymptotic stability property of the origin of the closed-loop system has been proved. Secondly, we find that, if the damping forces of the robot are large enough, then the saturated position output feedback controller solves the global trajectory tracking control problem without velocity measurements. The effectiveness of the proposed controllers has been verified through simulation tests. Namely, a comparative analysis of the bounded controller obtained and the well-known "PD+" like control scheme is carried out. It is shown that the approach proposed in this paper saves energy for control inputs. A. S. Andreev, O. A. PeregudovaBesides, a comparative analysis of the bounded controller and its analogue constructed earlier in a cylindrical phase space is carried out. It is shown that the controller provides lower values for the root mean square error of the position and velocity of the closed-loop system.

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supporting

confidence: 60%

Section: Introductionmentioning

confidence: 99%

On Global Trajectory Tracking Control for an Omnidirectional Mobile Robot with a Displaced Center of Mass

Andreev¹,

Peregudova²

2020

Nelin. Dinam.

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“…If there is a sudden change in speed, the robotic arm will be impacted and vibrated accordingly. This will affect the stability of the manipulator and reduce the efficiency [25][26][27]. The change curve of each joint angle and velocity reflected in Figure 6 is continuous and smooth.…”

Section: Robot Displacement Trajectory Tracking Experimentsmentioning

confidence: 99%

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

Hua-ying

2022

Wireless Communications and Mobile Computing

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With the development and leap of artificial intelligence technology, more and more robots have penetrated into all walks of life. Today’s agriculture is changing in the direction of modernization and automation. On the one hand, because of rising labor costs, it cannot afford to consume a large amount of labor for agricultural operations. On the other hand, the population is growing rapidly, and traditional agricultural production, picking, and other links have been unable to keep pace with the development of the times. Therefore, it is very necessary to use artificial intelligence technology to transform traditional agriculture. The purpose of this paper is to use artificial intelligence technology to plan, track, and optimize the displacement trajectory of the agricultural picking robot, so as to improve the working efficiency of the picking robot. In this paper, the neural network, the D-H modeling method of the manipulator, and the forward and reverse motion of the manipulator are explained and analyzed, and based on the relevant algorithms of neural network, the manipulator is modeled, and then the, forward and reverse motion of the manipulator is analyzed in detail, and the digital model of the picking robot is constructed. Then, the angle and motion speed of each joint of the robot are analyzed to reduce the motion trajectory error caused by friction and other factors. Then, the simulation experiment of the displacement trajectory tracking control is carried out, and the linear trajectory motion and the arc trajectory motion are deeply analyzed, and the axis error is greatly reduced after 6 iterations. Finally, the displacement trajectory is optimized. The optimized total movement time is shortened by 6.84 seconds, which enables the picking robot to not only ensure work efficiency but also accurately complete the planned displacement trajectory. After continuous experiments on the algorithm model and the picking robot, the actual trajectory of the picking robot at 0.7 seconds can be expected. The trajectories are completely coincident, indicating that the neural network plays a very important role in the trajectory research of picking robots.

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“…et al, 2014). With the continuous advancement and application of drone technology, drones have become a mature tool in the fields and a new weapon for pesticide spraying (Andreev A. S. et al, 2019). Compared with traditional manual spraying methods, drone pesticide spraying has the advantages of high speed, high efficiency, and large-scale operation (Batliner M. et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Agricultural Uav Crop Spraying Path Planning Based on Pso-A* Algorithm

FAN

2023

INMATEH

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Currently, drones have been gradually applied in the field of agriculture, and have been widely used in various types of agricultural aerial operations such as precision sowing, pesticide spraying, and vegetation detection. The use of agricultural UAVs for pesticide spraying has become an important task in the agricultural plant protection process. However, in the crop spraying process of agricultural UAVs, it is necessary to traverse multiple spray points and plan obstacle avoidance paths, which greatly affects the efficiency of agricultural UAV crop spraying operations. To address the above issues, traditional particle swarm optimization (PSO) algorithms have strong solving capabilities, but they are prone to falling into local optima. Therefore, this study proposes an improved PSO algorithm combined with the A* algorithm, which introduces a nonlinear convergence factor balancing algorithm for global search and local development capabilities in the traditional PSO algorithm, and adopts population initialization to enhance population diversity, so that the improved PSO algorithm has stronger model solving capabilities. This study designs two scenarios for agricultural UAV crop spraying path planning: one without obstacles and one with obstacles. Experimental simulation results show that using the PSO algorithm to solve the obstacle-free problem and then using the A* algorithm to correct the path obstructed by obstacles in the obstacle scenario, the agricultural UAV crop spraying trajectory planning based on the PSO-A* algorithm is real and effective. This research can provide theoretical basis for agricultural plant protection and solve the premise of autonomous operation of UAVs.

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On Output Feedback Trajectory Tracking Control of an Omni-Mobile Robot

Cited by 7 publications

References 14 publications

On Global Trajectory Tracking Control for an Omnidirectional Mobile Robot with a Displaced Center of Mass

On Global Trajectory Tracking Control for an Omnidirectional Mobile Robot with a Displaced Center of Mass

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

Agricultural Uav Crop Spraying Path Planning Based on Pso-A* Algorithm

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