ABSTRACT
The paper addresses the loop shaping problem in the altitude control of an unmanned aerial vehicle to land the flying robot with a specific landing scenario adopted. The proposed solution is optimal, in the sense of the selected performance indices, namely minimum-time, minimum-energy, and velocity-penalized related functions, achieving their minimal values, with numerous experiments conducted throughout the development and preparation to the Mohamed Bin Zayed International Robotics Challenge (MBZIRC 2020). A novel approach to generation of a reference altitude trajectory is presented, which is then tracked in a standard, though optimized, control loop. Three landing scenarios are considered, namely: minimum-time, minimum-energy, and velocity-penalized landing scenarios. The experimental results obtained with the use of the Simulink Support Package for Parrot Minidrones, and the OptiTrack motion capture system proved the effectiveness of the proposed approach.
ABSTRACT
This paper presents a new optimization-based tuning method for fractional- and integer-order controllers. The method is based on minimizing an arbitrary cost function which is taken here as the integrated absolute tracking error, and it can be used for tuning purposes as well as the assessment of controller performance. The presented results are obtained from experiments with a servo drive system with a 2- and 3-parameter fractional-order controller. They depict performance improvement and ability of the proposed method to find the global minimum of a selected performance index in experimental conditions. The method does not need any model of the plant, and it has great potential for applications in general controllers or for an extension to different parameter tuning problems. The method offers a new way of changing dynamic properties of closed-loop systems, by rapid tuning of controllers. This makes possible new applications of this method, as opposed to other tuning methods, which basically require knowledge of the plant.
ABSTRACT
The paper presents a novel autotuning approach for finding locally-best parameters of controllers on board of unmanned aerial vehicles (UAVs). The controller tuning is performed fully autonomously during flight on the basis of predefined ranges of controller parameters. Required controller properties may be simply interpreted by a cost function, which is involved in the optimization process. For example, the sum of absolute values of the tracking error samples or performance indices, including weighed functions of control signal samples, can be penalized to achieve very precise position control, if required. The proposed method relies on an optimization procedure using Fibonacci-search technique fitted into bootstrap sequences, enabling one to obtain a global minimizer for a unimodal cost function. The approach is characterized by low computational complexity and does not require any UAV dynamics model (just periodical measurements from basic onboard sensors) to obtain proper tuning of a controller. In addition to the theoretical background of the method, an experimental verification in real-world outdoor conditions is provided. The experiments have demonstrated a high robustness of the method to in-environment disturbances, such as wind, and its easy deployability.
