In this paper, we present a framework for selflocalization of parking robots in a parking lot innovatively using square-like landmarks, aiming to provide a positioning solution with low cost but high accuracy. It utilizes square structures common in parking lots such as pillars, corners or charging piles as robust landmarks and deduces the global pose of the robot in conjunction with an off-line map. The localization is performed in real-time via Particle Filter using a single line scanning LiDAR as main sensor, an odometry as secondary information sources. The system has been tested in a simulation environment built in V-REP, the result of which demonstrates its positioning accuracy below 0.20 m and a corresponding heading error below 1 • .
Starting from a component-level nonlinear model of a turboprop engine, the high-pressure turbine speed and power turbine speed output data at six steady-state operating points are linearized and fitted, and a turboprop engine state variable model is established. Based on these state variable models, the Proportional Integral Derivative (PID) control method, the augmented Linear Quadratic Regulator (LQR) control method and the Linear Quadratic Gaussian/Loop Transfer Recover (LQG/LTR) control method are used to design the controllers respectively, and the relative converted speed of the high-pressure turbine is selected as the scheduling parameter of the Linear Parameter Varying (LPV) model, and the controller is called to control the turboprop engine’s non-linear speed. Linear model for large envelope control. Finally, the control effects of the above three control methods are compared and analyzed, and their advantages and disadvantages are compared. The simulation results show that the LPV controller designed based on the LQG/LTR method is more effective than the controllers designed by the other two control methods on the nonlinear turboprop model.
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