Practical Formation Control for Multiple Anonymous Robots System with Unknown Nonlinear Disturbances

This paper mainly investigates formation control problems for a group of anonymous mobile robots with unknown nonlinear disturbances on a plane, in which all robots can asymptotically converge to any formation patterns without collision, and maintain any required relative distance with neighboring robots. To solve this problem, all robots are modeled as kinematic points and can only acquire information from other robots and their targets. Furthermore, a flexible distributed control law is designed to solve the formation problem while no collisions between any robots can be guaranteed during the whole process. The outstanding feature of the proposed control method is that it can force all mobile robots to form not only uniform circle formations but also non-uniform and non-circular formations with moving target centers. At last, both theoretical analysis and numerical simulations show the feasibility of the proposed control law.

Compressor Stall Warning Using Nonlinear Feature Extraction Algorithms

Stall is a type of flow instability in compressors that sets the low-flow limit for compressor operation. During the past few decades, efforts to develop a reliable stall warning system have had limited success. This paper focuses on the small nonlinear disturbances prior to deep surge and introduces a new approach to identify these disturbances using nonlinear feature extraction algorithms including phase-reconstruction of time-series signals and evaluation of a parameter called approximate entropy. To the best of our knowledge, this is the first time approximate entropy has been used for stall warning, and thus, its definition and utility are presented in detail. The technique is applied to stall data sets from two different compressors: a high-speed centrifugal compressor that unexpectedly entered rotating stall during a speed transient and a multistage axial compressor with both modal- and spike-type stall inception. In both cases, nonlinear disturbances appear, in terms of spikes in approximate entropy, prior to surge. The presence of these presurge spikes indicates the potential of using the approximate entropy parameter for small disturbance detection and stall warning. The details of the nonlinear feature extraction algorithm, including guidelines for its application as well as results from applying the algorithm to rig-level data, are presented.

Compressor Stall Warning Using Nonlinear Feature Extraction Algorithms

Stall is a type of flow instability in compressors that sets the low-flow limit for compressor operation. During the past few decades, efforts to develop a reliable stall warning system have had limited success. This paper focuses on the small nonlinear disturbances prior to deep surge and introduces a new approach to identify these disturbances using nonlinear feature extraction algorithms including: phase-reconstruction of time-series signals and evaluation of a parameter called approximate entropy. To the best of the authors’ knowledge, this is the first time approximate entropy has been used for stall warning, and thus, its definition and utility are presented in detail. The technique is applied to stall data sets from two different compressors: a high-speed centrifugal compressor that unexpectedly entered rotating stall during a speed transient and a multi-stage axial compressor with both modal- and spike-type stall inception. In both cases, nonlinear disturbances appear, in terms of spikes in approximate entropy, prior to surge. The presence of these pre-surge spikes indicates the potential of using the approximate entropy parameter for small disturbance detection and stall warning. The details of the nonlinear feature extraction algorithm, including guidelines for its application as well as results from applying the algorithm to rig-level data, are presented.

Finite-time H∞ dynamic quantization inputs control for uncertain switched systems

This paper investigates the problem of finite-time stability and finite-time [Formula: see text] stabilization for switched systems with parametric uncertainties and nonlinear disturbances satisfying Lipschitz condition. The dynamic quantization inputs feedback control technology is proposed to utilize quantized input measurements which can significantly reduce the communication burden. Sufficient conditions in terms of linear matrix inequality (LMIs) are presented through applying Lyapunov function method and average dwell approach to ensure the finite-time stability of the switched system. By analysing the feasibility of LMIs’ solution, the feedback gain matrix and the dynamic quantization parameter are obtained. In addition, more constraints are proposed to ensure the finite-time stabilization with a prescribed [Formula: see text] performance index with respect to nonlinear disturbances, and the Lipschitz constant matrix of Lipschitz condition is not required to be known in advance. Finally, with the application to the proposed control of a numerical example and a two-stage chemical reactor system, the validity of the conclusion is verified.

Asynchronous Adaptive Fault-Tolerant Control for Markov Jump Systems with Actuator Failures and Unknown Nonlinear Disturbances

This article investigates an asynchronous adaptive fault-tolerant control problem for Markov jump systems (MJSs) with actuator failures and absolutely unknown nonlinear disturbances. The hidden Markov model (HMM) is used to describe the asynchronization phenomenon between the controller and the modes of the original system. Moreover, by using the designed adaptive law, the values of actuator partial failures and the bounds of the unknown nonlinear disturbances can be estimated, respectively. Furthermore, the HMM-based asynchronous adaptive fault-tolerant controller is provided to ensure that the closed-loop system is asymptotically stable despite of unknown nonlinear disturbances. Finally, numerical examples are given to show the effectiveness of the proposed method.

Modified Technique of Parameter Identification of a Permanent Magnet Synchronous Motor with PWM Inverter in the Presence of Dead-Time Effect and Measurement Noise

The paper considers the problem of parameter identification of the surface mounted permanent magnet synchronous motor (SPMSM) with pulse width modulated (PWM) inverter in the presence of dead time of power switches and other nonlinear distortions. Parameter identification of the SPMSM is required for the tuning of the torque control loop, because in some cases, the exact values of phase resistances and inductances are not known. In the absence of nonlinear disturbances, the problem of SPMSM parameters estimation is not difficult. The influence of the dead-time effect, back electromotive force and measurements noise introduces distortions in experimental output data sets, which leads to incorrect parameter estimation. Thus, there is a need to develop new designs of identification experiments and methods of processing of the experimental data. A detailed mathematical model of SPMSM with a PWM inverter in the presence of dead-time effect is considered in the paper. The negative influence of the dead-time effect on the results of parameter estimation is shown. A modified technique of parameter identification of SPMSM based on the estimation of frequency response function is proposed. The applied design of identification experiments, the type of excitation input signal, and methods of data processing allow us to minimize the influence of nonlinear disturbances and to reduce the variance of estimation of frequency response function. These features provide a high performance of SPMSM parameters estimation.