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The development and improvement of control techniques has attracted many researchers for many years.Especially in the controller design of complex and nonlinear systems, various methods have been proposed to determine the ideal control parameters. One of the most common and effective of these methods is determining the controller parameters with optimization algorithms.In this study, LQR controller design was implemented for position control of the double inverted pendulum system on a cart. First of all, the equations of motion of the inverted pendulum system were obtained by using Lagrange formulation. These equations were linearized by Taylor series expansion around the equilibrium position to obtain the state-space model of the system. The LQR controller parameters required to control the inverted pendulum system were determined by using a trial and error method. The determined parameters were optimized by using five different configurations of three different optimization algorithms (GA, PSO, and ABC). The LQR controller parameters obtained as a result of the optimization study with five different configurations of each algorithm were applied to the system and the obtained results were compared with each other. In addition, the configurations that yielded the best control results for each algorithm were compared with each other and the control results were evaluated in terms of response speed and response smoothness.

In this study, interval type2 fuzzy logic (IT2FL) and PID controller is designed for swing-up position control of double inverted pendulum (DIP) system. The double inverted pendulum system consists of two rigid bars connected by a revolute joint. Mass of the revolute joint is included in the dynamic model.Rigid bars in the system are assumed to experience planar motion. The pendulum system is connected to the base by means of a revolute joint. Torque provided through a motor mounted to the base is used for position control of the system. PID (Proportional-Derivative-Integral) and interval type2 fuzzy logic controllers are developed by using the same performance criteria for position control of double inverted pendulum system. IT2FL controller is similar with type1 fuzzy logic controller. IT2FL system provides soft decision boundaries, whereas a type-1 fuzzy logic system provides a hard decision boundary. Membership function in interval type2 fuzzy logic set as an area called Footprint of Uncertainty (FOU) which limited by two type1 membership function those are upper membership function (UMF) and lower membership function (LMF).System behaviour is obtained by computer simulation using developed controllers respectively. Computer simulation results are compared in order to evaluate applicability of developed controllers. MATLAB/Simulink software is used in computer simulations.

A modelling approach for neuro-fuzzy control of a single-link flexible robot manipulator that uses a computer-aided design (CAD) program is proposed. Initially, a CAD model of the flexible link is created using experimentally determined values of system parameters. This CAD model is then exported to MATLAB software and the Simulink/ SimMechanics toolbox. An adaptive-network-based fuzzy logic controller is used for position and vibration control of the flexible link.Experimental and simulation results are presented that validate the proposed approach.

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.