Globalised Dual Heuristic Dynamic Programming in Tracking Control of the Wheeled Mobile Robot

Author(s):  
Marcin Szuster
2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Marcin Szuster ◽  
Zenon Hendzel

Network-based control systems have been emerging technologies in the control of nonlinear systems over the past few years. This paper focuses on the implementation of the approximate dynamic programming algorithm in the network-based tracking control system of the two-wheeled mobile robot, Pioneer 2-DX. The proposed discrete tracking control system consists of the globalised dual heuristic dynamic programming algorithm, the PD controller, the supervisory term, and an additional control signal. The structure of the supervisory term derives from the stability analysis realised using the Lyapunov stability theorem. The globalised dual heuristic dynamic programming algorithm consists of two structures: the actor and the critic, realised in a form of neural networks. The actor generates the suboptimal control law, while the critic evaluates the realised control strategy by approximation of value function from the Bellman’s equation. The presented discrete tracking control system works online, the neural networks’ weights adaptation process is realised in every iteration step, and the neural networks preliminary learning procedure is not required. The performance of the proposed control system was verified by a series of computer simulations and experiments realised using the wheeled mobile robot Pioneer 2-DX.


2015 ◽  
Vol 220-221 ◽  
pp. 60-66 ◽  
Author(s):  
Zenon Hendzel ◽  
Marcin Szuster

The article presents a new approach to the sensor-based navigation of wheeled mobile robot Pioneer 2-DX in the unknown 2-D environment with static obstacles. The navigation task has been developed using a discrete hierarchical control system with a path planning layer and a tracking control layer designed using approximate dynamic programming algorithms. The navigator realises a behavioural control approach to the path planning process using the adaptive coordination of two simple behaviours: “goal-seeking” and “obstacle avoiding”. The main part of the navigator is the Action-Dependant Heuristic Dynamic Programming structure realised in a form of the actor and critic neural networks. To avoid the time consuming trial and error learning, additional proportional controllers generating signals that prompt the direction of the sub-optimal control law seeking process at the beginning of the NNs adaptation process are arranged in the navigator. The tracking control layer is composed of a PD controller, the Dual Heuristic Dynamic Programming algorithm and a supervisory term. It generates control signal for DC motors of the robot. The performance of the proposed discrete control system was verified by a series of experiments conducted using wheeled mobile robot Pioneer 2-DX equipped with one laser and eight ultrasonic range finders that provide object detection.


2010 ◽  
Vol 164 ◽  
pp. 419-424 ◽  
Author(s):  
Zenon Hendzel ◽  
Marcin Szuster

In presented paper we propose a discrete tracking control algorithm for a two-wheeled mobile robot. The control algorithm consists of discrete Adaptive Critic Design (ACD) in Action Dependant Heuristic Dynamic Programming (ADHDP) configuration, PD controller and a supervisory term, derived from the Lyapunov stability theorem and based on the variable structure systems theory. Adaptive Critic Designs are a group of algorithms that use two independent structures for estimation of optimal value function from Bellman equation and estimation of optimal control law. ADHDP algorithm consists of Actor (ASE - Associate Search Element) that estimates the optimal control law and Critic (ACE - Adaptive Critic Element) that evaluates quality of control by estimation of the optimal value function from Bellman equation. Both structures are realized in a form of Neural Networks (NN). ADHDP algorithm does not require a plant model (the wheeled mobile robot (WMR) model) for ACE or ASE neural network weights update procedure (in contrast with other ACD configurations e.g. Heuristic Dynamic Programming or Dual Heuristic Programming that use the plant model). In presented control algorithm Actor-Critic structure is supported by PD controller and the supervisory term, that guarantee stable implementation of tracking in an initial adaptive critic neural networks learning phase, and robustness in a face of disturbances. Verification of proposed control algorithm was realized on the two-wheeled mobile robot Pioneer-2DX.


2021 ◽  
pp. 107754632199918
Author(s):  
Rongrong Yu ◽  
Shuhui Ding ◽  
Heqiang Tian ◽  
Ye-Hwa Chen

The dynamic modeling and trajectory tracking control of a mobile robot is handled by a hierarchical constraint approach in this study. When the wheeled mobile robot with complex generalized coordinates has structural constraints and motion constraints, the number of constraints is large and the properties of them are different. Therefore, it is difficult to get the dynamic model and trajectory tracking control force of the wheeled mobile robot at the same time. To solve the aforementioned problem, a creative hierarchical constraint approach based on the Udwadia–Kalaba theory is proposed. In this approach, constraints are classified into two levels, structural constraints are the first level and motion constraints are the second level. In the second level constraint, arbitrary initial conditions may cause the trajectory to diverge. Thus, we propose the asymptotic convergence criterion to deal with it. Then, the analytical dynamic equation and trajectory tracking control force of the wheeled mobile robot can be obtained simultaneously. To verify the effectiveness and accuracy of this methodology, a numerical simulation of a three-wheeled mobile robot is carried out.


IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 15592-15602
Author(s):  
Xueshan Gao ◽  
Rui Gao ◽  
Peng Liang ◽  
Qingfang Zhang ◽  
Rui Deng ◽  
...  

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