وهاب نکوکار

The unique features of multi-rotor unmanned aerial vehicles (MRUAVs) have led to a variety of applications. However, the carrying capacity of MRUAVs remains one of the most critical challenges. Forming an MRUAV swarm can be an effective solution. In MRUAV swarms, followers require a formation control scheme to track one or more leaders. This formation control must address measurement noise, communication delays, model uncertainty, actuator and sensor faults, and cyber-attacks. In this paper, we propose a new cooperative adaptive fault-tolerant formation control for quadrotor swarms in the presence of deception during cyber-attacks. Quadrotors are connected to neighboring drones and a central leader. We evaluate the proposed method's ability to handle model uncertainty, actuator faults, cyber-attacks, and measurement noise through simulation studies. Considering all these challenges simultaneously and evaluating the presented formation control method stand as one of the primary contributions of this paper

At present, the use of unmanned aerial vehicles (UAVs) has been increased dramatically. The reasons for this development are cheapness, smallness, simplicity, and diversity of missions. The simplicity of guidance and control of multi-rotor drones is that they are equipped with an autopilot system. This system is responsible for flying control. UAVs do not have a high weight and often have three-phase high-speed motors which makes a fast and complex flight dynamics. In this paper, a fuzzy adaptive PID controller is applied to control an UAV for carrying a time-varying cargo. The performance of the flight control system implemented on a quadrotor is evaluated, experimentally. A sandbox is used to model the time-varying mass. The sand passes from the beginning of the fly through some holes of the box, and after about a minute all the sand is poured. At the end of the paper, the practical results are compared with results obtained by fixed-parameter PID controller.

The automatic control system of the blood glucose level is known as an artificial pancreas, whose design and implementation has been researched in recent years. The closed-loop control of the blood glucose concentration deals with some challenges such as sudden decreasing of the blood glucose level, the effects of the hydrocarbon intake of meals, physical exercise and physiological changes due to nervous pressure or stress on the blood glucose level. There are also challenges like day-to-day and patient-to-patient variations. In this paper, a novel adaptive fuzzy integral sliding mode control is presented for the regulation of the blood glucose level. The proposed method is evaluated by simulating nine virtual patients. In the simulations, based on the presented virtual patient model, not only the controller performance in dealing with the effects of the hydrocarbon consuming and physical exercises on the glucose concentration is studied, but also the effects of patient-to-patient variations are investigated simultaneously. For each subsystem of the insulin pump and continuous glucose sensor, a dynamic model is considered. The simulation results and a comparison with the results of the PID control, model predictive control, and adaptive fractional sliding mode control show the efficiency of the proposed method.

Sliding mode control is an effective method for controlling the neuromusculoskeletal systems. A major problem of conventional sliding mode control is exponential convergence of the tracking errors. To solve this problem, researchers proposed a robust control strategy called terminal sliding mode control. The main advantages of the terminal sliding mode control are not only robustness against uncertainties and external disturbances but also finite time convergence of tracking errors.

In this paper, we propose a decentralized control strategy which is based on terminal sliding mode control, for control of the ankle joint in paraplegic subjects using functional electrical stimulation. Agonist-antagonist co-activation is used to control the ankle movement.

The proposed control strategy was employed for control of ankle joint in three paraplegic subjects. The control task was to determine the stimulation pattern in order to converge the ankle movement trajectory to the desired trajectory. The experimental results on three paraplegic subjects showed that the proposed controller provided an excellent tracking control of reference trajectories. It could also generate control signals to compensate the effects of muscle fatigue.

The results of this study showed the proposed control strategy as an effective approach for controlling movements in paraplegic subjects using functional electrical stimulation.

One major limitation of walker-supported walking using functional electrical stimulation (FES) in paraplegic subjects is the high energy expenditure and the high upper body effort. Paraplegics should exert high amount of hand force to stabilize the body posture and to compensate lack of the sufficient torques at the lower extremity joints. In this paper, we introduce a 2-D musculoskeletal model of walker-assisted FES-supported walking of paraplegics. Using the developed model and an optimal controller, the stimulation patterns are determined such that the tracking errors of lower joint reference trajectories are minimized and the muscle activations and the handle reaction force (HRF) are reduced. Outputs of the optimal controller are stimulation patterns of the lower body muscles and torque acting on the upper body joints. The results show that the HRF and ground reaction force (GRF) generated by simulation are in agreement with the measured HRF and GRF. Moreover, the results indicate that the simulation-generated stimulation patterns of lower body muscles are in consist with the stimulation patterns reported in the literatures.