Most of the control strategies require a mathematical model or reasonable knowledge that is difficult to obtain for complex systems. Model-free control is a good alternative to avoid the difficulties and complex modeling procedures, especially if the knowledge about the system is insufficient. This paper presents a new control scheme completely independent of the system model. The proposed scheme combines sliding mode control (SMC) with intelligent proportional integral derivative (iPID) control based on a local model and extended state observer (ESO). Although the iPID control makes the proposed method model-free, it cannot guarantee that the tracking errors converge to zero asymptotically except the system is in a steady-state regime. Therefore, the SMC is added to the control scheme to ensure the convergence by minimizing the estimation errors of the observer. The proposed iPIDSMC controller is tested in the presence of different parameter variations and external disturbances on an inverted pendulum - cart (IPC), which is a highly unstable underactuated system with nonlinear coupled dynamics. The proposed controller is compared with the PID, iPID and Hierarchical Sliding Mode Control (HSMC) for a clearer evaluation. Simulation results showed that the proposed controller is extremely insensitive to parameter variations, matched and mismatched disturbances and the control signal of the proposed method is chattering-free, even though it is based on a discontinuous control action.

In this study, a fuzzy logic controller is designed in order to use in trajectory control of a robot manipulator. The considered robot manipulator consists of a rotating-prismatic joint housing a sliding flexible arm that carries a concentrated mass at the tip end. The tip end of the flexible arm traces a multi-straight-line path. This study is aimed to use a fuzzy logic controller in controlling the trajectory traced by the tip end of the flexible arm so as to reduce the vibrations induced in the flexible arm. The designed fuzzy controller is aimed to control both the position of the tip end of the flexible robot arm while the tip end traces a multi-straight-line path and the vibrations induced in the flexible arm. Numerical simulations obtained by using a developed computer program are presented and physical trend of obtained numerical results are discussed. The performance of the fuzzy logic control system is evaluated on the basis of the simulation results.

In this study, modeling and LQR control of a reaction wheel inverted pendulum system is described. The reaction wheel inverted pendulum model is created by using a 3D CAD platform and exported to Simscape Multibody. The multibody model is linearized to derive a state-space representation. A LQR (Linear-quadratic regulator) controller is designed and applied for balance control of the pendulum. The results show that deriving a state-space representation from multibody is an easy and effective way to model dynamic systems and balance control of the reaction wheel inverted pendulum is successfully achieved by LQR controller. Results are given in the form of graphics.

Two-Wheeled Self-Balancing Robots are widely used in various fields today. These systems have a highly unstable nature due to their underactuated structures. On the other hand, parameter uncertainties and external disturbances significantly affect their control performance. The best way to deal with parameter uncertainties that can easily lead controllers to instability is to use robust control methods. Dealing with these uncertainties is particularly crucial in control of underactuated and unstable systems such as Two-Wheeled Self-Balancing Robots. In this study, trajectory tracking control of a two wheeled self-balancing robot by using Sliding Mode Control (SMC) was realized. The chattering problem inherent in the SMC method was eliminated by employing tangent hyperbolic (tanh) switching function instead of signum function. The performance of the SMC controller has been examined under five different cases including external disturbance and various parameter uncertainties and compared with PID and LQR methods. The results showed that the SMC method is much more insensitive to parameter changes than the PID and LQR methods. It has also been observed that all three controllers maintain their stability against disturbance inputs, but the SMC method offers a better control performance.

In this study, a method is proposed to find the unknown simulation parameters of an industrial electro-hydraulic proportional valve. Electro-hydraulic servo systems are one of the most widely used actuator systems, and proportional valves are commonly found in hydraulic systems. Hydraulic systems are complicated to linearize, because nearly whole elements are nonlinear, with the inclusion of the valve actuators. The system model must be linearized to make it feasible for analysis of the system linearly or the characteristics must be identified by comparison the output responses suitable for the inputs. However, it is not easy to obtain a perfect system model for use in simulation studies without prior knowledge of the system. Obtaining a perfect model of a system that is not easy to model can be done in two different ways; applying one of the system identification methods after collecting experimental data or using manufacturers' datasheets. In this paper, simulation parameters of MOOG brand D675 series proportional servo valve system were obtained using The Bees Algorithm, an optimization algorithm, to match the dynamic specifications in the datasheet provided by the manufacturer. The obtained system model and the answers are shown graphically and the effectiveness of the proposed method is discussed.