In this paper, the Jacobian‐linearization‐ and feedback‐linearization‐based techniques of obtaining linearized model approaches are combined with a family of robust LQR control laws to identify the pairing which results in superior control performance of the bicycle robot, despite uncertainty and constraints, what is the main contribution of the paper. The control performance is analyzed using various indices, related,e.g. to energy consumption of the considered laws, with the experiments conducted on a real bicycle robot. As a result, the easily‐implementable controller is obtained, which requires only to perform a set of off‐line computations with a single additional parameterδin comparison with a standard linear‐quadratic controller, to obtain a state‐feedback vector, which, when implemented to the control system, ensures proper regulation of the output signal of the plant, despite uncertainty or possible actuator failures, obtaining energy‐efficient control law.

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.

The aging fracture of bonding wire is one of the main reasons for failure of insulated gate bipolar transistor (IGBT). This paper proposes an online monitoring method for IGBT bonding wire aging that does not interfere with the normal operation of the IGBT module. A quantitative analysis of aging degree was first performed, and the results of multivariate and univariate monitoring were compared. Based on the relationship between the monitoring parameters and the aging of the IGBT bonding wire, gradual damage of the IGBT bond wire was implemented to simulate aging failure and obtain the aging data. Moreover, the change of junction temperature was considered to regulate monitoring parameters. Then, the aging degree was evaluated by an artificial neural network (ANN) algorithm. The experimental results showed the effectiveness of the proposed method.

This paper presents a new optimization-based tuning method for fractionaland 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 2and 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. (C) 2020 The Authors. Published by Elsevier Ltd on behalf of ISA.

This letter shows for the first time why it is important and how to optimize the gains of a position controller on board of a fully-actuated aerial vehicle with bounded lateral force, via an auto-tuning approach. In such vehicles, most of the control authority is expressed along a principal thrust direction, while along the lateral directions smaller forces can be exploited to achieve full-pose tracking. The nonlinear and hard to model interplay between the constraint imposed on the lateral force and the gains of the position controller is overcome employing the OPTIM-tune calibration method. Several experimental tests, performed fully autonomously during flight, clearly show the practicability and benefits of the approach.

Essential ingredients for robust control are the ability to cope with different types of system behavior following modeling imperfections and the ability to assure a certain performance level. In this paper, we propose to use an actuator fault-tolerant control law to govern, during experiments, the stabilization of a bicycle robot with an inertial wheel in order to take into account unmodeled uncertainty introduced by using a linearized model in an LQR fashion. Our proposal is illustrated by signal plots and the values of performance indices obtained from a set of experiments