Value iteration based approximate dynamic programming for mobile robot trajectory tracking with persistent inputs

2017 ◽  
Author(s):  
Md Suruz Miah
Keyword(s):  
Dynamic Programming ◽  
Mobile Robot ◽  
Trajectory Tracking ◽  
Approximate Dynamic Programming ◽  
Value Iteration ◽  
Robot Trajectory

Optimal Switching in Anti-Lock Brake Systems of Ground Vehicles Based on Approximate Dynamic Programming

2015 ◽  
Author(s):  
Tohid Sardarmehni ◽  
Ali Heydari
Keyword(s):  
Dynamic Programming ◽  
Approximate Dynamic Programming ◽  
Infinite Horizon ◽  
Brake System ◽  
Iteration Algorithm ◽  
Value Iteration ◽  
Ground Vehicles ◽  
Optimal Switching ◽  
Hydraulic Brake ◽  
And Control

Approximate dynamic programming, also known as reinforcement learning, is applied for optimal control of Antilock Brake Systems (ABS) in ground vehicles. As an accurate and control oriented model of the brake system, quarter vehicle model with hydraulic brake system is selected. Due to the switching nature of hydraulic brake system of ABS, an optimal switching solution is generated through minimizing a performance index that penalizes the braking distance and forces the vehicle velocity to go to zero, while preventing wheel lock-ups. Towards this objective, a value iteration algorithm is selected for ‘learning’ the infinite horizon solution. Artificial neural networks, as powerful function approximators, are utilized for approximating the value function. The training is conducted offline using least squares. Once trained, the converged neural network is used for determining optimal decisions for the actuators on the fly. Numerical simulations show that this approach is very promising while having low real-time computational burden, hence, outperforms many existing solutions in the literature.

Study on Iterative Learning Control of Mobile Robot

2015 ◽  
Vol 775 ◽  
pp. 319-323
Author(s):  
Li Ping Qu ◽  
Yong Yin Qu ◽  
Hao Han Zhou
Keyword(s):  
Mobile Robot ◽  
Trajectory Tracking ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Tracking Problem ◽  
Forgetting Factor ◽  
Robot Trajectory ◽  
Tracking Process

In order to solve the mobile robot trajectory tracking problem better, an iterative learning control (ILC) was applied. And the efficiency of mobile robot trajectory tracking was improved. From the simulation result, ILC with forgetting factor has very good performance for solving mobile robot trajectory tracking problem, and the smooth of trajectory tracking process also improved well.

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