Parrot Mambo mini-drone is a readily available commercial quadrotor platform to understand and analyze the behavior of a quadrotor both in indoor and outdoor applications. This study evaluates the performance of three alternative controllers on a Parrot Mambo mini-drone in an interior environment, including Proportional–Integral–Derivative (PID), Linear Quadratic Regulator (LQR), and Model Predictive Control (MPC). To investigate the controllers’ performance, initially, the MATLAB®/Simulink™ environment was considered as the simulation platform. The successful simulation results finally led to the implementation of the controllers in real-time in the Parrot Mambo mini-drone. Here, MPC surpasses PID and LQR in ensuring the system’s stability and robustness in simulation and real-time experiment results. Thus, this work makes a contribution by introducing the impact of MPC on this quadrotor platform, such as system stability and robustness, and showing its efficacy over PID and LQR. All three controllers demonstrate similar tracking performance in simulations and experiments. In steady state, the maximal pitch deviation for the PID controller is 0.075 rad, for the LQR, it is 0.025 rad, and for the MPC, it is 0.04 rad. The maximum pitch deviation for the PID-based controller is 0.3 rad after the take-off impulse, 0.06 rad for the LQR, and 0.17 rad for the MPC.

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The purpose of the designed H ∞ controller is to drive effectively the hydraulic system and to make it follow the desired trajectory, which is commanded by a joystick, steering wheel or geographic based data. To design the controller we used the experimentally identified SIMO linear mathematical model. The identification procedure is based on random excitation signals and prediction error methods for regressive discrete-time equations. The input of the mathematical model is the voltage applied to the spool driver EH module (PVE) and the outputs of the model are the spool position of the proportional valve in the steering unit, flow rate and the cylinder piston position. These output variables carry important information about the state of the system and can be used to increase performance in respect to the case, when using only the cylinder piston position or pressure signals. We have already experimented with various control strategies for the identified mathematical model. Experimental studies are performed on a laboratory test rig for electrohydraulic steering systems based on the 32-bit microcontroller and taking into account the technical specifications of mobile machine manufacturers and standards. In addition to the requirements of the safety standards, new advantages are achieved in terms of precise tracking control and providing a variable steering ratio between the steering wheel and the steering cylinder supported by the designed H ∞ controller.

This paper is devoted to various issues related to the design and practical implementation of high order robust control laws. We consider derivation of plant uncertainty models using analytical or identification procedures, implementation of different schemes for µ-synthesis, choice of weighting filters and controller order reduction. Additional important problems arising in the framework of embedded control systems, like removing the sensor drifts, generation of control code from Simulink R ⃝ and effect of single precision arithmetic on the controller stability, are discussed in some details. As a case study we present the robust control of two-wheeled robot using µ-controller of order 30. The experimental results confirm that the closed-loop system achieves both robust stability and robust performance in respect to the uncertainties related to the identification of robot model.