Line following robots have ability to track a given path autonomously using feedback mechanisms. The path is usually a black line on a white surface or a white line on a black surface. Today, line following robots are used in medical, industrial and automotive industries. Therefore, the studies on the line following robots have been increased recently. In this study, a robust, non-chattering sliding mode control (SMC) is designed and applied for a line following robot. The mobile robot is designed to sense the straight or curved path with its infrared sensors mounted on the robot. Therefore, these infrared sensors provide continuous streaming of the defined path to guide or direct changes in robot by activating motors on right wheel or/and left wheel. The control strategy is curial to track complex paths accurately and to have a fast, stable and accurate line following robot. Thus, for comparison, conventional proportional-integral-derivative (PID) is also applied to robot. The main purpose of this study is to investigate performance of sliding mode control during path tracking. For this, numerical solution and experimental study were carried out. From the results, it was understood that sliding mode controller is highly efficient in tracking the path.

Adaptive control has been used for active vibration isolation and vehicle suspensions systems. A model reference adaptive control law is used for the plant to track the ideal reference model. In a model reaching adaptive control approach, the ideal of a skyhook target without using a reference model is achieved. In this paper, a novel approach, a model reaching adaptive-robust control law is studied for active vibration isolation systems. A dynamic manifold for ideal system is defined using the ideal of a skyhook target model system parameters. First, a new Lyapunov function is defined. Based on the Lyapunov stability theory, a model reaching adaptive and a robust control laws are derived for the uncertain system to reach the ideal manifold. Parameters and upper bounding functions are estimated as a trigonometric function depending on the relative displacements, velocities and the defined manifold. The developed adaptive and the robust compensators are combined and this combination is proposed as an adaptive-robust control law. After that, the controller is applied to a vehicle suspension system and the ideal of a skyhook target without using a reference model is achieved. The results also show that the proposed robust control law can increase the comfort of the vehicle active suspension systems and the ride comfort is remarkably increased.

This study presents the design and performance evaluation of an adaptive control system for a biped robot with five degrees of freedom. The primary goal is to achieve stable and accurate control despite uncertainties in the robot’s dynamic parameters and changes in environmental conditions. By analyzing the robot’s dynamic structure, an adaptive control strategy is developed, and its effectiveness is assessed through simulations. The results indicate that the proposed approach delivers superior performance. In particular, the system’s ability to update dynamic parameters in real time significantly reduces tracking errors and ensures the robot’s stability under uncertain conditions. Based on a passivity-based control framework commonly referenced in the literature, the adaptive controller enables precise tracking of desired reference signals, even in complex dynamic environments. Overall, the findings demonstrate that adaptive control is a highly effective solution, especially for complex systems such as biped robots.

This article aims to improve the ride comfort of a vehicle keeping a satisfactory without road-holding performance by a novel adaptive control method that is insensitive to unknown system dynamics, model parameter changes, external disturbances and without any loss in suspension working space. With this purpose in mind, a new fuzzy integrated model-based adaptive control law for vehicle suspension systems is proposed in this study to improve the ride comfort and to ensure the robustness of system towards unknown model parameters and external disturbances. First, a model-based adaptive control law, which has the robust characteristics is presented. Afterwards, a multi-input multi-output fuzzy logic controller is designed to determine the controller gains dynamically. The stability of controller is ensured by Lyapunov Theory to achieve uniform boundedness error convergence. A 4 degree-of-freedom half-car model with active suspension system is used in this study to assess the performance of the controller. The results are compared among passive, model-based adaptive control law-controlled and novel fuzzy model-based adaptive control law-controlled systems. It has been concluded that fuzzy model-based adaptive control law further attenuates linear and angular motions of the vehicle increasing the ride comfort. The robustness is also verified for vehicle components having different possible parameter values. It is noteworthy that suspension working length returns to its initial position. Thus, the vehicle ride comfort is improved with no suspension working space loss. Finally, the economic feasibility of controllers has been checked in terms of energy consumption.

Endüstri ve tıp alanında, kendi kendini dengeleyen robotlar hakkında tüm dünyada yapılan çalışmalar son yıllarda hız kazanmıştır. Bu tip robotların kullanım alanının genişlemesiyle birlikte karşılaşılan en büyük sorunun, robotların kontrol mekanizmaları olduğu anlaşılmaktadır. Yapıları gereği kontrol edilmesi zor olan bu robotlar için literatürde çok sayıda çalışma bulunmaktadır. Bu çalışmalar incelendiğinde klasik kontrol metotlarının, bu tip robotları kontrol etmekte yetersiz kaldığı görülmektedir. Fakat klasik kontrolcüler kullanılarak geliştirilen yeni nesil karma kontrol metotlarının robotlara uygulanmasıyla birlikte robotların istenilen hareketleri daha hızlı ve kararlı bir şekilde gerçekleştirdiği gözlemlenmektedir. Bu nedenle, klasik kontrol metotlarından PID kontrolcü parametrelerinin, bulanık mantıklı kontrol metodu ile belirlenerek oluşturulan karma bir kontrol metodu bu çalışmada önerilmekte ve kendi kendini dengeleyen robota uygulanmaktadır. Ayrıca yapılan çalışmalar incelendiğinde, sadece kendi kendini dengeleyen robotların analizinin gerçekleştirildiği görülmektedir. Yapılan bu çalışmada sürücü de sisteme dahil edilerek analiz edilmiştir. İlk olarak iki tekerlekli kendi kendini dengeleyen robot modellenmiş ve bu sistem için sadece PID (oransal, integral ve türevsel) kontrolcü ve Bulanık Mantık-PID (Fuzzy Logic-PID) kontrolcü tasarlanmıştır. Bulanık Mantık-PID kontrolcünün değişken kazanım katsayılarına sahip olması klasik kontrolcülerden daha hızlı tepki vermesini sağlamaktadır. PID ve Bulanık Mantık-PID kontrolcüler ile elde edilen benzetim sonuçları karşılaştırılmıştır. Çalışmanın sonunda Bulanık Mantık-PID kontrolcünün, kontrolcü kazanımlarını kendi kendine ayarlayabildiği ve istenilen hız ve pozisyona ulaşmakta başarılı bir performansa sahip olduğu anlaşılmıştır.