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This research investigates the performance of an adaptive controller for five-bar linkage robot. The proposed controller was entirely independent on the physical specifications of the robot. The controller was examined as the dynamics of the mechanical system varies for various environment conditions, which make it an ambiguous system. In this study, the introduced controller was designed based on the governing ideal Euler-Lagrange equations on the robot but assessed using the on-line dynamic simulation of the mechanism for different target configurations which guarantees the high performance and effectiveness of the designed controller.
The Rotary Inverted Pendulum (RIP) system is a significant classical problem of control engineering which has been investigated in the past decades. This study presents an optimum Input-Output Feedback Linearization (IOFL) cascade controller utilized Genetic Algorithm (GA). Due to the non-minimum phase behavior of the system, IOFL controller leads to unstable internal dynamics. Therefore a cascade structure is proposed consisting IOFL controller for inner loop with PD controller forming the outer loop. The primary design goal is to balance the pendulum in an inverted position. The control criterion is to minimize the Integral Absolute Error (IAE) of system angles. By minimizing the objective function related to IAE using Binary Genetic Algorithm (BGA), the optimal controller parameters can be assigned. The results verified capability and competent characteristics of the proposed controller. The method can be considered as a promising way for control of various similar nonlinear and under-actuated systems.
This research presents an optimum approach for designing
Rotary Inverted Pendulum (RIP) controller using PSO algorithm. The primary
design goal is to balance the pendulum in an inverted position and the
control criterion is to minimize the integral absolute error of system
angles. Simulation results demonstrate the robustness and effectiveness
of proposed controller with regard to parameter variations, various reference
trajectory and load disturbances. The proposed method can be considered
as a promising way for control of various similar nonlinear systems.
In this study, we present the local reconstruction of
differential-drive mobile robots position and orientation with an accurate
odometry calibration. Starting from the encoders readings and assuming
an absolute measurement available, Augmented Extended and Unscented Kalman
Filters (AEUKF) are proposed to localize the vehicle while estimating
a proper set of odometric parameters.In order to compare their
estimation performances explicitly, both observers are designed for the
same mobile robot model and run with the equal covariance matrices under
the identical initial conditions. In the simulation results, it is shown
that Augmented Unscented Kalman Filter (AUKF) outperforms the Augmented
Extended Kalman Filter (AEKF).
