Magdy M. Abdelhameed

Adaptive neural network based controller for robots

Abstract A new Adaptive Neural Network (ANN) controller for robot trajectory trackingproblem is developed. A novel and efficient training algorithm for the proposed controller ispresented in this paper. The proposed training algorithm is based on updating the weights of thenetwork each step by minimizing the quadrant tracking errors and their derivatives. A simulation study is carried out on a polar robot manipulator to assure the effectivenessof the proposed trajectory tracking robot control system. The effects of the new controllerparameters and noisy external load disturbances on the control performance are studied. Thesimulation results of the proposed adaptive ANN controller are compared with those of aconventional ANN controller. The obtained results assured the robustness of the proposed ANNcontroller for: (i) uncertainties of the robot arm dynamic model and/or parameters, (ii) variousnoisy external load disturbances. Also, the simulation results assure the effectiveness of theproposed adaptive ANN controller against the conventional ANN one.

Design and Implementation of a Flexible Manufacturing Control System Using Neural Network

Design and implementation of a sequential controller based on the concept of artificial neural networks for a flexible manufacturing system are presented. The recurrent neural network (RNN) type is used for such a purpose. Contrary to the programmable controller, an RNN-based sequential controller is based on a definite mathematical model rather than depending on experience and trial and error techniques. The proposed controller is also more flexible because it is not limited by the restrictions of the finite state automata theory. Adequate guidelines of how to construct an RNN-based sequential controller are presented. These guidelines are applied to different case studies. The proposed controller is tested by simulations and real-time experiments. These tests prove the successfulness of the proposed controller performances. Theoretical as well as experimental results are presented and discussed indicating that the proposed design procedure using Elmans RNN can be effective in designing a sequential controller for event-based type manufacturing systems. In addition, the simulation results assure the effectiveness of the proposed controller to outperform the effect of noisy inputs.

A recurrent neural network-based sequential controller for manufacturing automated systems

The objective of this paper is to propose a recurrent neural network (RNN)-based sequential controller to be used in an automated manufacturing system. Contrary to the programmable controller, an RNN-based sequential controller is based on a definite mathematical model rather than a trial and error technique. The proposed controller is also more flexible since it is not limited by the restrictions of finite state automata theory. A design procedure to use Elmans RNN-based sequential controller is presented, and applied to different case studies. The proposed controller is tested experimentally and proves successful. Theoretical results as well as experimental results are presented and discussed indicating that the proposed design procedure using Elmans RNN can be effective in designing a sequential controller for different types of manufacturing systems.

Adaptive learning algorithm for Cerebellar model articulation controller

Cerebellar model articulation controller (CMAC) was developed two decades ago, yet lacks an adequate learning algorithm. Examining the performance of a CMAC based controller showed that the control system become unstable after a long period of real time runs. A new adaptive learning algorithm is proposed. The resultant controller is applied for the trajectory tracking control of a piezoelectric actuated tool post. The performance of the proposed controller is compared with those of conventional controllers (PI controller and the conventional CMAC based controller). The experimental results showed that performance of the CMAC based controller using the proposed learning algorithm is stable and more effective than that of the conventional controllers.

Lower limb gait activity recognition using Inertial Measurement Units for rehabilitation robotics

In this paper, The authors considered a human lower limb gait activity recognition algorithm, using an IMU sensory network consisting of 4 IMUs distributed to the lower limb. The proposed algorithm depends on Random Forest for classification and a Hybrid Mutual Information and Genetic Algorithm (HMIGA) as a features selection technique. HMIGA selects the most distinguishing features from Discrete Wavelet Coefficient (DWT) features and other statistical and physical (self designed) features. The proposed algorithm is compared with Support Vector Machine (SVM) to classify 5 activities and the results are presented on 6 subjects with 2% average error rate with 1.9% superiority on SVM. Moreover, HMIGA as a feature selector is compared to the traditional feature selectors and DWT as a feature also compared to statistical and physical features, showing their influence on the activity recognition process. Finally, the most important features selected by HMIGA are presented, proving the important role of the shanks sensor on the recognition process, where almost 50% of the selected features are from the shank sensor.

Cerebellar model articulation controller proportional velocity parameters variations and learning schemes—A study on electrohydraulic servo system

Cerebellar model articulation controller neural networks is one of the computational intelligence tools that can be applied for modeling, classification, and control. Proportional velocity controller is a servo-type controller, which is commonly applied to motion control systems. This paper presents a novel combination of cerebellar model articulation controller neural networks and optimal proportional velocity controller. A simple mathematical model for applying and studying cerebellar model articulation controller is introduced, and a study of its parameters is presented individually. The effect of parameters variation on cerebellar model articulation controller performance is identified. Learning algorithms highly affect the cerebellar model articulation controller behavior even when the parameters are optimized, and proper selection of the learning scheme must be taken under consideration. Three different learning algorithms are studied for evaluating transient and steady-state cerebellar model articulation controller responses. The results showed that the change of cerebellar model articulation controller generalization size and scale of the control signal has a marked effect on the performance of cerebellar model articulation controller. Furthermore, the constant learning rate algorithm gives the best overall performance.

Development of integrated brakes and engine traction control system

Traction control system (TCS) helps regulating wheel slip when vehicle starts from rest or accelerates excessively. It limits the slip either by applying braking torque or suppressing the engine torque or by using both techniques. The brakes has faster response than the engine; however, overusing brakes decrease its lifetime and increase the fuel consumed. In this paper, a TCS is proposed that utilizes both the brakes and the engine to control the wheel slip. Fuzzy and PID control strategies are used to control the brakes and engine respectively. When slip exceeds a certain threshold, the brakes TCS is activated, after a couple seconds the control is switched smoothly to the engine TCS and then the Brakes TCS is deactivated. The controller design was verified through simulations on the Simulink environment. The results show that the controller is able to maintain slip ratio at the desired value with smooth transition during switching controllers in addition to its capability of coping with sudden change in road conditions.

Modeling and simulation of Photovoltaic/Thermal hybrid system

In this paper an analytical model for a Photovoltaic/Thermal (PV/T) hybrid system is presented. The simulation in this work is based on an analytical model in solving the equations and determining the Photovoltaic (PV) cells characteristics using both MATLAB and COMSOL Multiphysics. COMSOL is simulating the electromagnetic waves produced by the Sun through solving Maxwells equations in three dimensions. Beside that COMSOL is used in studying the material absorption capabilities and calculating the material absorption coefficient using its refractive index. In addition to the above a thermal analysis for the PV module and the piping water is presented where the input, output temperatures, rate of heat transfer, overall heat transfer coefficient and thermal efficiency are calculated. As a result, a significant enhancement in the total electrical efficiency is observed with acceptable increased in the output water temperature.

Development of a Robust Hybrid Vehicle Power Management Control System

Hybrid vehicles (HVs) have the advantages of both internal combustion (IC) engine vehicles and electric vehicles (EVs) and overcome their disadvantages. In order to enhance the performance of hybrid vehicle, “Arduino” based control system is utilized to design power management strategies for parallel HVs .The models of hybrid vehicle are developed by electric vehicle simulation software ADVISOR which uses a hybrid backward/forward approach. The results demonstrate that the proposed control system provide a good approach for the advanced power management system of robust hybrid vehicle. Keywords—Robust hybrid vehicles; control systen; power mangment system; parallel hybrid vehicle; Ardueno; stateflow; simscape; matlab simulink; ADVISOR.

CMAC Based Hybrid Control System for Solving Electrohydraulic System Nonlinearities

Automation based electrohydraulic servo systems have a wide range of applications in nowadays industry. However, they still suffer from several nonlinearities like deadband in electrohydraulic valves, hysteresis, stick-slip friction in valves and cylinders. In addition, all hydraulic system parameters have uncertainties in their values due to the change of temperature while working. This paper addresses these problems by designing a suitable intelligent control system that has the ability to deal with the system nonlinearities and parameters uncertainties using a fast and online learning algorithm. A novel hybrid control system based on Cerebellar Model Articulation Controller (CMAC) neural network is presented. The proposed controller is composed of two parallel controllers. The first is a conventional Proportional-Velocity (PV) servo type controller which is used to decrease the large initial error of the closed-loop system. The second is a CMAC neural network which is used as an intelligent controller to overcome nonlinear characteristics of the electrohydraulic system. A fourth order model for the electrohydraulic system is introduced. PV controller parameters are tuned to get optimal values. Simulation and experimental results show a good tracking performance obtained using the proposed controller. The controller shows its robustness in two working environments. The first is by adding different inertia loads and the second is working with noisy level input signals.