Smart element aware gate controller for intelligent wheeled robot navigation

<p>The directing of a wheeled robot in an unknown moving environment with physical barriers is a difficult proposition. In particular, having an optimal or near-optimal path that avoids obstacles is a major challenge. In this paper, a modified neuro-controller mechanism is proposed for controlling the movement of an indoor mobile robot. The proposed mechanism is based on the design of a modified Elman neural network (MENN) with an effective element aware gate (MEEG) as the neuro-controller. This controller is updated to overcome the rigid and dynamic barriers in the indoor area. The proposed controller is implemented with a mobile robot known as Khepera IV in a practical manner. The practical results demonstrate that the proposed mechanism is very efficient in terms of providing shortest distance to reach the goal with maximum velocity as compared with the MENN. Specifically, the MEEG is better than MENN in minimizing the error rate by 58.33%.</p>

Design and Implementation of Intelligent Hybrid Neuro Controller for Automatic Voltage Regulator

In this paper, we presented a hybrid controller by combining the advantages of both PID and Neuro controllers for automatic voltage regulator (AVR) system. A Neuro controller is designed using multilayer feedforward neural network and Levenberg-Marquardt backpropagation algorithm is used for training the network. Also, hybrid controller is achieved by blending the characteristics of classical PID and proposed Neuro controller using a switching mechanism based on the error. The proposed PID, Neuro and Hybrid controllers are simulated in MATLAB environment and their transient response parameters are compared. The simulation results clearly indicated the improvement in the transient output of the automatic voltage regulator system with proposed hybrid controller even in the presence of uncertainties in the system.

Neuro-controller for antagonistic bi-articular muscle actuation in robotic arms based on terminal attractors

The conventional limitations of the robotic actuation mechanisms have led to many researchers needing to explore novel biomimetic motor mechanisms as the antagonistic human motor system. In this way, it is of interest to understand the inherent adaptive stiffness, or compliance, and modulation, in different alternative actuation architectures such as the antagonistic bi-articular (AbA) system. These novel AbA actuation mechanisms are characterized by resembling the efficient tendon and muscle build-up over our skeletal structure. In this paper, we propose a Cartesian neuro-controller for a robot manipulator actuated by a simplified adaptive viscoelastic linear AbA system. It is shown that the adaptive closed-loop system enforces terminal attractors, induced by a continuous model-free sliding mode control, simultaneously with a learning algorithm to compensate parametric uncertainties of AbA system through a low dimensional neural network. Numerical simulation results exhibit the feasibility of this approach.

Introduction and Application Aspects of Machine Learning for Model Reference Adaptive Control With Polynomial Neurons

This chapter recalls the nonlinear polynomial neurons and their incremental and batch learning algorithms for both plant identification and neuro-controller adaptation. Authors explain and demonstrate the use of feed-forward as well as recurrent polynomial neurons for system approximation and control via fundamental, though for practice efficient machine learning algorithms such as Ridge Regression, Levenberg-Marquardt, and Conjugate Gradients, authors also discuss the use of novel optimizers such as ADAM and BFGS. Incremental gradient descent and RLS algorithms for plant identification and control are explained and demonstrated. Also, novel BIBS stability for recurrent HONUs and for closed control loops with linear plant and nonlinear (HONU) controller is discussed and demonstrated.

Modal-Energy-Based Neuro-Controller for Seismic Response Reduction of a Nonlinear Building Structure

This study presents a neuro-control algorithm based on structural modal energy that outputs an optimal control signal to reduce vibration during earthquakes. The modal energy of a structure is used in the objective function during the training process of a neural network. The modal energy and control signal are then minimized by the proposed neuro-control technique. A three-story nonlinear building was installed with an active mass damper, which was used to verify the applicability of the proposed control algorithm. The El Centro earthquake was adopted to train the modal-energy-based neuro-controller. The six recorded earthquakes were employed to consider unknown earthquake effects after training. The results obtained from the proposed control algorithm were compared with those obtained from a non-controlled response and a multilayer perceptron. The numerical results show that the proposed control algorithm is quite effective in reducing the structural response and modal energy. While nonlinear hysteretic behaviors appear in the non-controlled responses, these nonlinear behaviors almost entirely disappear with control.