Nonlinear tracking control for nonholonomic mobile robots with input constraints: an experimental study

Ren, Wei; Sun, Jisang; Beard, Randal W.; McLain, Timothy W.

doi:10.1109/acc.2005.1470775

Cited by 18 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, accurate data collection of the spreading pattern of the fire fronts is very crucial in wildfire management. Many path-planning algorithms have already been developed for autonomous system of vehicles and robots [49][50][51][52][53][54][55][56][57][58][59]. The approach introduced here has the following advantages, which makes it different and profitable:…”

Section: Introductionmentioning

confidence: 99%

Optimal control approaches for consensus and path planning in multi-agent systems

Shobeiry

View full text Add to dashboard Cite

Optimal control is one of the most powerful, important and advantageous topics in control engineering. The two challenges in every optimal control problem are defining the proper cost function and obtaining the best method to minimize it. In this study, innovative optimal control approaches are developed to solve the two problems of consensus and path planning in multi-agent systems (MASs). The consensus problem for general Linear-Time Invariant systems is solved by implementing an inverse optimal control approach which enables us to start by deriving a control law based on the stability and optimality condition and then according to the derived control define the cost function. We will see that this method in which the cost function is not specified a priori as the conventional optimal control design has the benefit that the resulting control law is guaranteed to be both stabilizing and optimal. Three new theorems in related linear algebra are developed to enable us to use the algorithm for all the general LTI systems. The designed optimal control is distributed and only needs local neighbor-to-neighbor information based on the communication topology to make the agents achieve consensus and track a desired trajectory. Path planning problem is solved for a group are Unmanned Aerial Vehicles (UAVs) that are assigned to track the fronts of a fires in a process of wildfire management. We use Partially Observable Markov Decision Process (POMDP) in order to minimize the cost function that is defined according to the tracking error. Here the challenge is designing the algorithm such that (1) the UAVs are able to make decisions autonomously on which fire front to track and (2) they are able to track the fire fronts which evolve over time in random directions. We will see that by defining proper models, the designed algorithms provides real-time calculation of control variables which enables the UAVs to track the fronts and find their way autonomously. Furthermore, by implementing Nominal Belief-state Optimization (NBO) method, the dynamic constraints of the UAVs is considered and challenges such as collision avoidance is addressed completely in the context of POMDP.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal control approaches for consensus and path planning in multi-agent systems

Shobeiry

View full text Add to dashboard Cite

show abstract

Predictive proportional nonlinear control for stable-target tracking of a mobile robot: an experimental study

Sugisaka

Hazry

2006

Artif Life Robotics

View full text Add to dashboard Cite

Seam tracking control for mobile welding robot based on vision sensor

Zhang

Yang

2010

J. Cent. South Univ. Technol.

View full text Add to dashboard Cite

To solve the seam tracking problem of mobile welding robot, a new controller based on the dynamics of mobile welding robot was designed using the method of backstepping kinematics into dynamics. A self-turning fuzzy controller and a fuzzy-Gaussian neural network (FGNN) controller were designed to complete coordinately controlling of cross-slider and wheels. The fuzzy-neural control algorithm was described by applying the Gaussian function and back propagation (BP) learning rule was used to tune the membership function in real time by applying the FGNN controller. To make the tracking more quickly and smoothly, the neural network controller based on dynamic model was designed, which utilized self-learning and self-adaptive ability of the neural network to deal with the partial uncertainty and the disturbances of the parameters of the robot dynamic model and real-time compensate the dynamics coupling. The results show that the selected control input torques make the system globally and asymptotically stable based on the Lyapunov function selected out; the accuracy of the proposed controller tracing is within ±0.4 mm and can satisfy the requirements of practical welding project.

show abstract

Nonlinear tracking control for nonholonomic mobile robots with input constraints: an experimental study

Cited by 18 publications

References 17 publications

Optimal control approaches for consensus and path planning in multi-agent systems

Optimal control approaches for consensus and path planning in multi-agent systems

Predictive proportional nonlinear control for stable-target tracking of a mobile robot: an experimental study

Seam tracking control for mobile welding robot based on vision sensor

Contact Info

Product

Resources

About