Ashraf Saleem

An Undergraduate Mechatronics Project Class at Philadelphia University, Jordan: Methodology and Experience

Mechatronics is a branch of engineering whose final product should involve mechanical movements controlled by smart electronics. The design and implementation of functional prototypes are an essential learning experience for the students in this field. In this paper, the guidelines for a successful mechatronics project class are presented, evaluated, and discussed. Furthermore, the paper introduces a general mechatronic system design methodology that should equip students to carry out a successful mechatronics project in their undergraduate training. Three student projects at Philadelphia University, Jordan, are examined in detail, with descriptions of their goals, design, and implementation.

Identification and cascade control of servo-pneumatic system using Particle Swarm Optimization

Abstract This paper presents a cascade control methodology for pneumatic systems using Particle Swarm Optimization (PSO). First, experimental data is collected and used to identify the servo-pneumatic system where an Auto-Regressive Moving-Average (ARMA) model is formulated using PSO algorithm. Then, cascaded Proportional–Integral–Derivative (PID) controller with PSO tuning is proposed and implemented on real system using Hardware-In-the-Loop (HIL). The identified model is validated experimentally and the performance of the cascaded-PID controller is tested under various conditions of speed variation. Experimental results show that cascaded-PID with PSO tuning performs better than single-PID, especially in disturbance rejection (a practical challenge in industrial pneumatic systems). Results also show that cascaded-PID with PSO-tuning performs better than cascaded-PID with self-tuning in the transient and steady-state responses.

A methodology for identification and control of electro-mechanical actuators.

Graphical abstract

Regional pole placement with saturated control for DC-DC buck converter through Hardware-in-the-Loop

In this paper, the problem of robust stability and tracking of saturated control systems for buck DC-DC converters is considered. Linear Matrix Inequalities (LMIs) are used to insert the constraints in the design phase while imposing positivity in the closed-loop state. The control objective is to design a control law for the converter that limit duty ratio between 0 and 1. This will allow the system to switch between two topologies in the continuous conduction mode (CCM), to achieve a tracking reference condition. This has been developed using uncertain saturated control and regional pole placement techniques. The proposed controller is applied to a real DC-DC buck converter through a Hardware-In-the-Loop (HIL) test system. Experimental and simulation results show the effectiveness and the success of the proposed controller in tracking a reference voltage with limiting the duty ratio between 0 and 1. Results also show that the proposed controller performed better than the conventional one.

Mechatronic system design course for undergraduate programmes

Technology advancement and human needs have led to integration among many engineering disciplines. Mechatronics engineering is an integrated discipline that focuses on the design and analysis of complete engineering systems. These systems include mechanical, electrical, computer and control subsystems. In this paper, the importance of teaching mechatronic system design to undergraduate engineering students is emphasised. The paper offers the collaborative experience in preparing and delivering the course material for two universities in Jordan. A detailed description of such a course is provided and a case study is presented. The case study used is a final year project, where students applied a six-stage design procedure that is described in the paper.

On-line identification of induction motors: Experiments and results

Induction motors are widely used in the industry. However, due to their involved mathematical models those depend on difficult to measure parameters such as leakage inductance. Therefore, simplified model approximations are usually used instead. In this paper, a system identification method based on auto regressive moving average (ARMA) models and hardware-in-the-loop (HIL) concept has been employed to identify and predict the behavior of a squirrel cage induction motor. The motor transfer function is identified online using an impulse input. Then, the identified model response is compared to the real system response with different input signals. Results show that the model used follows the real system response with good accuracy.

Smooth Variable Structure Filter for pneumatic system identification

The Smooth Variable Structure Filter (SVSF) is a newly-developed predictor-corrector filter for state and parameter estimation [1]. The SVSF is based on the Sliding Mode Control concept. It defines a hyperplane in terms of the state trajectory and then applies a discontinuous corrective action that forces the estimate to go back and forth across that hyperplane. The SVSF is suitable for fault detection and identification applications because of its stability and robustness in modeling uncertainties. The SVSF has two indicators of performance; the a posteriori output error and the chattering. The latter — as a signal-contains the systems information which is proven and explored in this paper. The SVSF is applied for the identification of pneumatic systems in order to verify the proposed method. Furthermore, the proposed method is compared with neural network and the results reveal that SVSF is better in identifying nonlinear systems.

Design of a brain controlled hand exoskeleton for patients with motor neuron diseases

Patients suffering with motor neuron diseases (MND) are characterized by their inability to control essential voluntary muscle activity. This situation may lead to what is known as Locked-in syndrome (LIS). As the name suggests, LIS describes the state of being locked inside a paralyzed body with a functioning mind. With recent advances in robotics and signal processing technologies, patients with motor neuron disease may be able to partially overcome their disability and regain some control over their external environments. In this paper, we propose a design for brains controlled hand exoskeleton. The proposed system uses a dual loop control, namely the brain-hand control and a local (hand) force control. The hand exoskeleton design consists of three main parts: four fingers with a three-layered sliding spring mechanism, an extension to fix the thumb of the patient and the main body which connects all the fingers with the linear actuator. The proposed system is implemented and tested successfully. Two actions are performed, namely grasping and releasing a light foam ball. After a training period, the brain-controlled exoskeleton was able to perform the two actions accurately and smoothly.

Design and Experimental Implementation of a Beam-Type Twin Dynamic Vibration Absorber for a Cantilevered Flexible Structure Carrying an Unbalanced Rotor: Numerical and Experimental Observations

This paper presents experimental and numerical results about the effectiveness of a beam-type twin dynamic vibration absorber for a cantilevered flexible structure carrying an unbalanced rotor. An experimental laboratory prototype setup has been built and implemented in our laboratory and numerical investigations have been performed through finite element analysis. The proposed system design consists of a primary cantilevered flexible structure with an attached dual-mass cantilevered secondary dynamic vibration absorber arrangement. In addition, an unbalanced rotor system is attached to the tip of the flexible cantilevered structure to inspect the system response under harmonic excitations. Numerical findings and experimental observations have revealed that significant vibration reductions are possible with the proposed dual-mass, cantilevered dynamic vibration absorber on a flexible cantilevered platform carrying an unbalanced rotor system at its tip. The proposed system is efficient and it can be practically tuned for variety of design and operating conditions. The designed setup and the results in this paper can serve for practicing engineers, researchers and can be used for educational purposes.

Weighted parametric model identification of induction motors with variable loads using FNN structure and NN2TF algorithm

Induction motors exhibit non-linear behaviour and are difficult to model. Furthermore, load disparities cause speed variations and therefore predicting the motor’s response is challenging. In this paper, feedforward neural networks (FNNs) are used to model induction motors at two load levels (i.e. no-load and full-load). The two FNN models are then transformed into auto-regressive moving-average (ARMA) models using a new NN2TF algorithm. A weighted parametric model is then formulated by combining both ARMA models to provide appropriate transfer functions at different loads ranging from no-load to full-load. In order to validate the developed model, experimental data (with voltage/speed as input/output) is collected from an induction motor plant at five different load levels and used to test the proposed model. Simulation results show that the estimated model produced dynamic responses that follow the experimental data with good accuracy, regardless of the load.