Design of an adaptive nonlinear PID controller for nonholonomic mobile robot based on posture identifier

Abstract

This paper proposes an adaptive nonlinear controller to guide a nonholonomic mobile robot during continuous and non-continuous trajectory tracking. The structure of the controller consists of two models that describe the kinematics and dynamics of the mobile robot system and the feedforward neural controller. The models are modified Elman neural network and feedforward multi-layer perceptron respectively. The trained Elman neural model acts as the position and orientation identifier The feedforward neural controller is trained off-line and adaptive weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback PID neural controller is based on the posture neural identifier and quadratic performance index optimization algorithm in order to tune automatically the PID controller parameters on-line for generating an optimal torque action in the transient state for N-step-ahead prediction. The general back propagation algorithm is used to learn the feedforward neural controller and the posture neural identifier. The simulation results show the effectiveness of the proposed adaptive nonlinear control algorithm; this is demonstrated by the minimised tracking error and the smoothness of the torque control signal obtained, especially with regards to the external disturbance attenuation problem.

Arabic Sentiment Analysis (ASA) Using Deep Learning Approach

“…In the biological intelligence, the information is analyzing incrementally but with keeping an internal model of what is being analyzed, developed based on past data and continually updated each time new data is considered. RNN works in the same manner but in a very basic mechanism (Al-Araji, et al, 2011): that is, it deals with sequences as feed-forward neural network by iterating through the sequence elements and preserving a state that has data belongs to what it has lastly noticed (CHOLLET, 2018) & (Al-Araji, 2015). All RNNs have the configuration like a chain of repeating modules of neural network (Al-Araji, 2014).…”

An adaptive neural control methodology design for dynamics mobile robot

“…The state of pure-rolling of non-holonomic conditions must be achieved as well as the non-slipping state as in ( 5) [25].…”

“…Euler Lagrange formulation is used to demonstrate the dynamics model of the mobile robot platform as in ( 6) [24], [25].…”

“…From the simulation results, the excellent the free-navigation for the mobile robot and high tracking performance are depicted in Figure 9. Table 3 demonstrates the performance of the proposed controller compared with those of other nonlinear controllers utilized in [25], [29], [30] in terms of the minimum value of the performance index, X-axis and Y-axis maximum error values as well as the number of iterations. Figure 10 shows the online cost index mean square error (MSE) response which shows the performance improvement of the proposed neural network controller in terms of minimizing the mobile robot's position error after 50 samples.…”

PSO-based optimized neural network PID control approach for a four wheeled omnidirectional mobile robot

“…In [14], Wahhab and Al-Araji have presented design based on Convolutional Neural Network Trajectory Tracking (CNNTT) controller to control mobile robot to find optimal path in the presence of obstacles using hybrid swarm optimization. In [15], Al-Araji et al proposed an adaptive nonlinear controller for trajectory tracking of nonholonomic mobile robot. The controller consists of feedforward multi-layer perceptron and modified Elman neural network.…”