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    Force Feedback Control of Lower Extremity Exoskeleton Assisting of Load Carrying Human
    (Trans Tech Publications Ltd, 2014) Sahin, Yusuf; Botsali, Fatih Mehmet; Kalyoncu, Mete; Tinkir, Mustafa; Onen, Umit; Yilmaz, Nihat; Baykan, Omer Kaan
    Lower extremity exoskeletons are wearable robot manipulators that integrate human intelligence with the strength of legged robots. Recently, lower extremity exoskeletons have been specifically developed for rehabilitation, military, industrial applications and rescuing, heavy-weight lifting and civil defense applications. This paper presents controller design of a lower-extremity exoskeleton for a load carrying human to provide force feedback control against to external load carried by user during walking, sitting, and standing motions. Proposed exoskeleton system has two legs which are powered and controlled by two servo-hydraulic actuators. Proportional and Integral (PI) controller is designed for force control of system. Six flexible force sensors are placed in exoskeleton shoe and two load cells are mounted between the end of the piston rod and lower leg joint. Force feedback control is realized by comparing ground reaction force and applied force of hydraulic cylinder. This paper discusses control simulations and experimental tests of lower extremity exoskeleton system.
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    A parallel cooperative hybrid method based on ant colony optimization and 3-Opt algorithm for solving traveling salesman problem
    (Springer, 2018) Gulcu, Saban; Mahi, Mostafa; Baykan, Omer Kaan; Kodaz, Halife
    This article presented a parallel cooperative hybrid algorithm for solving traveling salesman problem. Although heuristic approaches and hybrid methods obtain good results in solving the TSP, they cannot successfully avoid getting stuck to local optima. Furthermore, their processing duration unluckily takes a long time. To overcome these deficiencies, we propose the parallel cooperative hybrid algorithm (PACO-3Opt) based on ant colony optimization. This method uses the 3-Opt algorithm to avoid local minima. PACO-3Opt has multiple colonies and a master-slave paradigm. Each colony runs ACO to generate the solutions. After a predefined number of iterations, each colony primarily runs 3-Opt to improve the solutions and then shares the best tour with other colonies. This process continues until the termination criterion meets. Thus, it can reach the global optimum. PACO-3Opt was compared with previous algorithms in the literature. The experimental results show that PACO-3Opt is more efficient and reliable than the other algorithms.