Multi-agent Coordinated Control and Collision Avoidance with Unknown Disturbances

The formation problem of multi-agent systems via coordinated control is investigated,where the multiple agents can achieve the common velocity with leader and avoid collision during the evolution.In the real-world situation,the communication is often disturbed and inaccurate.Hence,the unknown disturbances are considered in the velocity measurements,which is assumed to be bounded and does not need to be modelled.Moreover,a complicated nonlinear interaction among agents is presented in the design of control.Based on the existing work of multi-agent systems,a flocking control protocol is proposed to address the formation problem in the dynamic topology.The stability analysis is given to prove that the velocities of all agents can converge to the velocity of leader and the stable motion with collision avoidance can be achieved eventually.Finally,some simulations are presented to verify the effectiveness of the proposed algorithm.

Subspace Identification for Closed-Loop Systems With Unknown Deterministic Disturbances

This paper presents a subspace identification method for closed-loop systems with unknown deterministic disturbances.To deal with the unknown deterministic disturbances,two strategies are implemented to construct the row space that can be used to approximately represent the unknown deterministic disturbances using the trigonometric functions or Bernstein polynomials depending on whether the disturbance frequencies are known.For closed-loop identification,CCF-N4SID is extended to the case with unknown deterministic disturbances using the oblique projection.In addition,a proper Bernstein polynomial order can be determined using the Akaike information criterion(AIC)or the Bayesian information criterion(BIC).Numerical simulation results demonstrate the effectiveness of the proposed identification method for both periodic and aperiodic deterministic disturbances.

Precision motion control for electro-hydraulic axis systems under unknown time-variant parameters and disturbances

This article focuses on asymptotic precision motion control for electro-hydraulic axis systems under unknown time-variant parameters,mismatched and matched disturbances.Different from the traditional adaptive results that are applied to dispose of unknown constant parameters only,the unique feature is that an adaptive-gain nonlinear term is introduced into the control design to handle unknown time-variant parameters.Concurrently both mismatched and matched disturbances existing in electro-hydraulic axis systems can also be addressed in this way.With skillful integration of the backstepping technique and the adaptive control,a synthesized controller framework is successfully developed for electro-hydraulic axis systems,in which the coupled interaction between parameter estimation and disturbance estimation is avoided.Accordingly,this designed controller has the capacity of low-computation costs and simpler parameter tuning when compared to the other ones that integrate the adaptive control and observer/estimator-based technique to dividually handle parameter uncertainties and disturbances.Also,a nonlinear filter is designed to eliminate the“explosion of complexity”issue existing in the classical back-stepping technique.The stability analysis uncovers that all the closed-loop signals are bounded and the asymptotic tracking performance is also assured.Finally,contrastive experiment results validate the superiority of the developed method as well.

ADRC BASED INPUT DISTURBANCE REJECTION FOR MINIMUM-PHASE PLANTS WITH UNKNOWN ORDERS AND/OR UNCERTAIN RELATIVE DEGREES

The capability of ADRC is studied for linear time-invariant SISO minimum-phase systems with unknown orders, uncertain relative degrees, and unknown input disturbances. It is proved that ADRC can reject the unknown input disturbance and guarantee the close-loop stability for the plants with unknown but bounded relative degrees. Meanwhile, some close-loop performances can be achieved. The influence of the sensor noise is also discussed. And it is demonstrated by numerical examples that one ADRC with fixed parameters can be applied to a group of plants of different orders, relative degrees, and parameters.

Adaptive neural network based boundary control of a flexible marine riser system with output constraints

In this study,we develop an adaptive neural network based boundary control method for a flexible marine riser system with unknown nonlinear disturbances and output constraints to suppress vibrations.We begin with describing the dynamic behavior of the riser system using a distributed parameter system with partial differential equations.To compensate for the effect of nonlinear disturbances,we construct a neural network based boundary controller using a radial basis neural network to reduce vibrations.Under the proposed boundary controller,the state of the riser is guaranteed to be uniformly bounded based on the Lyapunov method.The proposed methodology provides a way to integrate neural networks into boundary control for other flexible robotic manipulator systems.Finally,numerical simulations are given to demonstrate the effectiveness of the proposed control method.