Adaptive constraint‐following control for uncertain nonlinear mechanical systems with measurement error

Yang, Zeyu; Huang, Jin; Hu, Zhanyi; Yin, Hui; Zhong, Zhihua

doi:10.1002/rnc.5506

Cited by 11 publications

(8 citation statements)

References 34 publications

(52 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Extensive efforts have been dedicated to robust path following controllers design. [15][16][17][18] Chen et al 19 investigated the robust H∞ path tracking control design of network-based autonomous vehicles with delay and packet dropout. Tagne et al 20 proposed a higher-order sliding-mode controller, and verified the robustness against the incorrect value of tire stiffness coefficient and vehicle mass.…”

Section: Introductionmentioning

confidence: 99%

“…Experimental results verified that this strategy could strengthen the robustness of the controlled system and achieve better path tracking accuracy. 21 Yang et al 18 innovatively formulated the vehicle path tracking task as a constraint-following problem and proposed an optimal robust control for autonomous vehicles with fuzzy uncertainties. Nevertheless, these control strategies are usually based on a precisely known bound, which is not suitable for practical engineering systems without the knowledge of uncertainty bounds.…”

Section: Introductionmentioning

confidence: 99%

“…The existence of time‐varying uncertainty may result in poor tracking performance or even unsteady motions, which can be dangerous for autonomous vehicles. Extensive efforts have been dedicated to robust path following controllers design 15‐18 . Chen et al 19 investigated the robust

H\infty

path tracking control design of network‐based autonomous vehicles with delay and packet dropout.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive robust path tracking control for autonomous vehicles with measurement noise

Huang

Yang

et al. 2022

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

In practical autonomous vehicle systems, model uncertainty and measurement noise are two challenging factors that deteriorate path tracking accuracy and system stability. This article proposes an adaptive robust controller to deal with these two factors and enhance path tracking accuracy. First, the path tracking model considering time‐varying uncertainty is built and represented as nominal and uncertain portions. Second, the control law is designed separately for both portions. A linear quadratic regulator controller is utilized to stabilize the nominal system. An additional robust control law is proposed to suppress the matched uncertainty and measurement noise, with an adaptive scheme aimed at estimating the bound outside uncertainty. The path tracking system with the developed control law is proved to possess uniform boundedness, uniform ultimate boundedness properties, and robustness against mismatched uncertainty. Eventually, the effectiveness of the developed controller is validated using both MATLAB/Simulink‐TruckSim co‐simulation and an autonomous vehicle platform. The results demonstrate that the designed adaptive robust control can achieve accurate path tracking in the presence of time‐varying uncertainty and measurement noise.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

H\infty

path tracking control design of network‐based autonomous vehicles with delay and packet dropout.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive robust path tracking control for autonomous vehicles with measurement noise

Huang

Yang

et al. 2022

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

show abstract

“…There have been many studies focusing on designing controllers to achieve merging safety and high efficiency, but most of them do not focus on binding the controller performance with static spatial locations in the environment. Our team's previous work [16][17][18][19][20][21][22][23] mainly concentrates on the robust controller design and doesn't deal with this spatial-dependence, neither. Spatial-dependence means that the controller can assure that each vehicle satisfies the safety requirements to avoid collision at specific spatial locations, especially at the most dangerous merging point.…”

Section: Introductionmentioning

confidence: 99%

Collision-free control strategy for on-ramp merging: A spatial-dependent constraint following approach

Meng

Huang

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

This paper focuses on the problem of on-ramp merging control under the cooperation of intelligent and connected vehicles. A decentralized collision-free control strategy is proposed for on-ramp merging control. Each vehicle in the virtual platoon constructed by all vehicles on the arterial road and the on-ramp is equipped with a spatial-dependent constraint following controller. Under nonlinear vehicle dynamics, the proposed controller is proved to be uniformly bounded, thus assuring that each vehicle can satisfy the safety requirements to avoid collision at any specific spatial location, especially at the most dangerous merging point. Compared with time-dependence, this spatial-dependence means much more stability because spatial conditions during the on-ramp merging process are more static and invariant. Finally, a simulation containing six vehicles with relatively extreme testing conditions is conduct to validate the effectiveness of the proposed approach. The results demonstrate that the spacing errors can converge to 0 with respect to varying spatial-dependent desired spacings. The spacing errors of the six vehicles are kept at a relatively low level with a maximum value of 3.0778m. The maximal acceleration is 0.6060 m/s 2 and the maximal deceleration is -1.4042 m/s 2. All vehicles can achieve collision-free safety for on-ramp merging with a smooth and non-saturated control input generated by the proposed controller.

show abstract

“…Based on the U-K equation, take the motion requirements as constraints, the constraint following control has been presented [8][9][10][11] in motion control. The advantage of this method is that we only need to list the constraint equation corresponding to the desired trajectory [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

An Alternative Modeling and Control Approach for Fixed Boundary Motion Requirement in Industrial Automation and Robotics Using Constraint Following Method

Zhang

Zhao

Chen

et al. 2022

Preprint

View full text Add to dashboard Cite

In industrial automation and robotics, the motion control of the automatic equipment (e.g., mobile robots, assembling manipulators, machine tools, etc.) is an important task for intelligent and flexible manufacturing. Because of the limited space on the factory floor and limited operating conditions, most tasks require work in confined spaces and subject to regulatory requirements. This confined motion control objective is considered as the constraints in the paper (hence, it belongs to the constraint following control field). There are two categories of constraints for the motion control: equality and inequality which are corresponding to the two motion requirements. The problem of motion requirement with fixed boundary has not been solved systematically using constraint following method. The equation of motion for a constrained mechanical system which addresses both constraints is presented. This can be considered a generalization of the Udwadia-Kalaba (U-K) equation. The advantages of the equation include that it does not require additional pseudo variables and the solution is analytical. This exhibits profound applications. As a demonstration, a pan/tilt device mounted under the firefighting unmanned aerial vehicles (UAVs) is manipulated. The water-jet nozzle need motion requirements of swaying horizontally and not overshooting limits in vertical. Simulation and experimental results are presented to validate the effectiveness of the proposed approach.

show abstract

Adaptive constraint‐following control for uncertain nonlinear mechanical systems with measurement error

Cited by 11 publications

References 34 publications

Adaptive robust path tracking control for autonomous vehicles with measurement noise

Adaptive robust path tracking control for autonomous vehicles with measurement noise

Collision-free control strategy for on-ramp merging: A spatial-dependent constraint following approach

An Alternative Modeling and Control Approach for Fixed Boundary Motion Requirement in Industrial Automation and Robotics Using Constraint Following Method

Contact Info

Product

Resources

About