Adaptive full-state constrained tracking control for mobile robotic system with unknown dead-zone input

Luo, Xi; Mu, Dianrui; Wang, Zhen; Ning, Pengjun; Chen, Hua

doi:10.1016/j.neucom.2022.12.025

Cited by 19 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The control of the mobile robot`s motion is performed on two levels, as demonstrated in Figure 2. The low-level control is in charge of controlling the robot`s wheel speeds, while the high-level control determines the required robot speeds considering its first-order kinematics (Koukas et al, 2022;Luo et al, 2023;Teck & Dewil, 2022;Zhao et al, 2023). This two-layer architecture is very common in practice because most mobile robots and manipulators usually do not allow the user to impose accelerations or torques at the inputs.…”

Section: Robot Description and Control Architecturementioning

confidence: 99%

See 1 more Smart Citation

The Feasibility of Trajectory Controller by SS Equation of an Error Model in WMR

Qev

Meshach

2022

IJEC

View full text Add to dashboard Cite

There has been an increasing amount of research on WMRs. The WMRs have lots of advantages: their mechanical system is usually simple; the whole movement mostly contains two dependent motors; and their motions in two-dimensional space are quite adaptable. At the same time, the WMRs have better power efficiency. WMRs have been studied from many points of view, such as modelling control, obstacle avoidance, localization, tip-over stability, and so on. This paper discusses the control algorithm. WMRs have nonholonomic constraints. That is, the mobile robot may not be able to reach a specific position and angle in a specific time; however, the robot can reach the target in a different motion or within a limited time. In this paper, an error model based on the WMR is introduced. The model explains the error between the actual position and the ideal or target position in the continuous motion of WMRs. The error model will be transformed into a state-space equation in order to demonstrate that the algorithm that controls the WMR`s movement is controllable. By splitting the inputs into and , the whole equation is transformed into a zero-input state-space system. Then, the errors can be minimized with the optimization of the feedback. The results show that by optimizing the time-varying feedback matrix, the error is asymptotically stable and close to zero. Therefore, by adding this control law to the controller, an approach to making mobile robots follow the planned trajectory is obtained.

show abstract

Section: Robot Description and Control Architecturementioning

confidence: 99%

“…Usually, the reference trajectory is obtained by using a reference robot. Therefore, all the kinematic constraints are implicitly considered by the reference trajectory (Luo et al, 2023). The control inputs are mostly obtained by a combination of feedforward inputs, calculated from reference trajectory, and feedback control law.…”

Section: Introductionmentioning

confidence: 99%

The Feasibility of Trajectory Controller by SS Equation of an Error Model in WMR

Qev

Meshach

2022

IJEC

View full text Add to dashboard Cite

show abstract

“…Advantages of Legged Robots (Luo et al, 2023). Because this paper only needs to carry out a simple task simulation of taking target object, and the main environment is flat ground, as shown in figure 3, this paper will use four wheels as the mobile system, two front wheels as the driving wheels, respectively connect the motor, judge the distance obtained through the sensor system and use PID to control to carry out the mobile task (Shafaei & Mousazadeh, 2023).…”

Section: Mobile Systemmentioning

confidence: 99%

A Mixed Fast-RRT-A To Solve the Problem of Generating Nodes near Obstacles for Optimal Paths

Suwoyo

Hidayat

Abdurohman

et al. 2022

IJEC

View full text Add to dashboard Cite

This paper will focus on the application of machine vision in mobile robots and take moving to the appropriate position and grasping the designated target as the task. This paper will describe and simulate the vision technology that mobile robots need to apply in the task process. This paper mainly uses the camera as the sensor. The difficulties of vision technology are mainly divided into three parts: scene depth information acquisition, positioning and mapping, and image processing. In order to obtain the depth information of the scene, this paper mainly introduces the depth information acquisition methods of monocular camera and binocular camera. In the aspect of localization and mapping, this paper mainly introduces the simulation of visual odometer to understand the basic process of mobile robot obtaining navigation map and its own route. Then, the gray gradient 2D maximum entropy algorithm is introduced to segment the scene and target, and extract features to judge the required target. Compared with other segmentation algorithms, the gray gradient 2D maximum entropy algorithm has higher segmentation accuracy, but the operation time is longer. This paper has simply optimized its operation efficiency. Finally, this paper describes the positioning method of grasping the object with a two degree of freedom manipulator using the knowledge of inverse kinematics. Because of the epidemic situation, schools cannot obtain experimental equipment. This paper mainly demonstrates the effectiveness of the algorithm through simulation.

show abstract

“…Liu et al 17 proposed an effective control strategy to solve the fixed-time control problem of the nonlinear systems with linear dead-zone inputs. Luo et al 18 utilized adaptive control techniques to propose a novel robust control algorithm for compensating the adverse effects of unknown dead-zone inputs, and the tracking control was realized. And Gao et al 19 investigated the tracking control strategy for the heterogeneous vehicles with unknown dead-zone inputs.…”

Section: Introductionmentioning

confidence: 99%

An adaptive bearing rigid formation control of multi-agent systems with nonlinear dead-zone inputs

Wang,

Chen,

et al. 2023

Measurement and Control

View full text Add to dashboard Cite

In this paper, an adaptive bearing rigid formation control strategy for a class of nonlinear system with unknown dead-zone inputs and external disturbance is proposed. Firstly, the I-Type fuzzy system is used to approximate the unknown nonlinear dynamics of the formation model, and the approximation errors and unknown external disturbance are eliminated by the parameter adaptive estimation. Furthermore, the adaptive dynamic estimation algorithm is utilized to estimate and compensate the unknown dead-zone parameters, effectively suppressing the impact of dead-zone on formation system performance. Finally, the stability of the formation system is proved based on LaSalle’s invariance principle, and the effectiveness of the algorithm is verified by simulation results.

show abstract

Adaptive full-state constrained tracking control for mobile robotic system with unknown dead-zone input

Cited by 19 publications

References 26 publications

The Feasibility of Trajectory Controller by SS Equation of an Error Model in WMR

The Feasibility of Trajectory Controller by SS Equation of an Error Model in WMR

A Mixed Fast-RRT-A To Solve the Problem of Generating Nodes near Obstacles for Optimal Paths

An adaptive bearing rigid formation control of multi-agent systems with nonlinear dead-zone inputs

Contact Info

Product

Resources

About

Adaptive full-state constrained tracking control for mobile robotic system with unknown dead-zone input

Cited by 19 publications

References 26 publications

The Feasibility of Trajectory Controller by SS Equation of an Error Model in WMR

The Feasibility of Trajectory Controller by SS Equation of an Error Model in WMR

A Mixed Fast-RRT*-A* To Solve the Problem of Generating Nodes near Obstacles for Optimal Paths

An adaptive bearing rigid formation control of multi-agent systems with nonlinear dead-zone inputs

Contact Info

Product

Resources

About

A Mixed Fast-RRT-A To Solve the Problem of Generating Nodes near Obstacles for Optimal Paths