Steer-by-wire (SBW) systems in a passenger car can improve vehicle steering capability and design flexibility by replacing the mechanical linkage between the steering wheel and front wheels by a control circuit. The steering controller, however, should provide good performance in response to driver's input signal. This includes fast response, absence of overshoot or oscillatory behavior, and good accuracy with minimal steady-state error. In this paper, an optimal control strategy based on observed system states is proposed and implemented on an electrohydraulic SBW system of a passenger car. First, a linear mathematical model is developed using gray-box system identification techniques. A standard input signal, pseudorandom binary sequence (PRBS), is designed to stimulate the system in the concerned bandwidth. Then, a linear-quadratic regulator (LQR) together with a full-state system observer is designed. Based on simulation, the LQR parameters and the observer poles are chosen to satisfy the aforementioned performance criteria for good steering. Finally, the control strategy is applied in a real-time environment to test the tracking capability, where the system is given high-rate reference signals (relative to the human rate of steering). The results show that the steering system tracks the reference signal with high accuracy even in the existence of high external force disturbances.

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Quadrotor Unmanned Arial Vehicles (UAVs) are commonly used for complex tasks such as, surveillance, search and rescue in hazard locations for its small size, lightness and robustness. However, the stability of UAVs represents a big challenge due to its high Nonlinear, multivariable, strongly coupled nature. The present work investigates two commonly-used control strategies namely, PD-control with low pass filter and Nonlinear feedback linearization control. The parameters of each controller are optimized to set the time-domain performance within specific constrains. The performances of the two control strategies are simulated and the results are validated on real experiments. The results indicate that Nonlinear control can substantially expand the region of controllable flight angles compared to linear control. It can stabilize the quadrotor system in case of multi angle disturbances. PD-controller with low passes filter shows poor performance when it synchronously controls more than one angle at the same time.

In several food processing and chemical industries, liquid is pumped and kept in interrelating coupled tanks. However, automatic regulation of the liquid level and flow control between these tanks is a challenging problem because of the complexity and high non linearity of such system. This paper deals with the liquid level control of two horizontal coupled tanks system. A comprehensive comparative study is made for most popular sliding mode control (SMC) algorithms found in literature, namely Proportional-Derivative Sliding Mode Control (PD-SMC), Proportional-Integral-Derivative SMC (PID-SMC), Fractional Order SMC and finally dynamic SMC. Special emphasis is put on the effect of the sensor noise on the controller performance. Simulated experiments including robustness to variation in plant parameters and step input disturbances are made. Control algorithms parameters are selected to optimize designed performance indices by using MATLAB optimization toolbox. Simulation results reveal that dynamic SMC is superior to other control algorithms in the presence of sensor noise and has a significant reduction in the actuator chattering phenomenon.

Purpose The study proposed a human–robot interaction (HRI) framework to enable operators to communicate remotely with robots in a simple and intuitive way. The study focused on the situation when operators with no programming skills have to accomplish teleoperated tasks dealing with randomly localized different-sized objects in an unstructured environment. The purpose of this study is to reduce stress on operators, increase accuracy and reduce the time of task accomplishment. The special application of the proposed system is in the radioactive isotope production factories. The following approach combined the reactivity of the operator’s direct control with the powerful tools of vision-based object classification and localization. Design/methodology/approach Perceptive real-time gesture control predicated on a Kinect sensor is formulated by information fusion between human intuitiveness and an augmented reality-based vision algorithm. Objects are localized using a developed feature-based vision algorithm, where the homography is estimated and Perspective-n-Point problem is solved. The 3D object position and orientation are stored in the robot end-effector memory for the last mission adjusting and waiting for a gesture control signal to autonomously pick/place an object. Object classification process is done using a one-shot Siamese neural network (NN) to train a proposed deep NN; other well-known models are also used in a comparison. The system was contextualized in one of the nuclear industry applications: radioactive isotope production and its validation were performed through a user study where 10 participants of different backgrounds are involved. Findings The system was contextualized in one of the nuclear industry applications: radioactive isotope production and its validation were performed through a user study where 10 participants of different backgrounds are involved. The results revealed the effectiveness of the proposed teleoperation system and demonstrate its potential for use by robotics non-experienced users to effectively accomplish remote robot tasks. Social implications The proposed system reduces risk and increases level of safety when applied in hazardous environment such as the nuclear one. Originality/value The contribution and uniqueness of the presented study are represented in the development of a well-integrated HRI system that can tackle the four aforementioned circumstances in an effective and user-friendly way. High operator–robot reactivity is kept by using the direct control method, while a lot of cognitive stress is removed using elective/flapped autonomous mode to manipulate randomly localized different configuration objects. This necessitates building an effective deep learning algorithm (in comparison to well-known methods) to recognize objects in different conditions: illumination levels, shadows and different postures.