Tracking Control of Multi-Agent Systems Using a Networked Predictive PID Tracking Scheme

With the rapid development of network technology and control technology,a networked multi-agent control system is a key direction of modern industrial control systems,such as industrial Internet systems.This paper studies the tracking control problem of networked multi-agent systems with communication constraints,where each agent has no information on the dynamics of other agents except their outputs.A networked predictive proportional integral derivative(PPID)tracking scheme is proposed to achieve the desired tracking performance,compensate actively for communication delays,and simplify implementation in a distributed manner.This scheme combines the past,present and predictive information of neighbour agents to form a tracking error signal for each agent,and applies the proportional,integral,and derivative of the agent tracking error signal to control each individual agent.The criteria of the stability and output tracking consensus of multi-agent systems with the networked PPID tracking scheme are derived through detailed analysis on the closed-loop systems.The effectiveness of the networked PPID tracking scheme is illustrated via an example.

Distributed tracking for networked Euler-Lagrange systems without velocity measurements

The problem of distributed coordinated tracking control for networked Euler-Lagrange systems without velocity measurements is investigated. Under the condition that only a portion of the followers have access to the leader, sliding mode estimators are developed to estimate the states of the dynamic leader in finite time. To cope with the absence of velocity measurements, the distributed observers which only use position information are designed. Based on the outputs of the estimators and observers, distributed tracking control laws are proposed such that all the fol- lowers with parameter uncertainties can track the dynamic leader under a directed graph containing a spanning tree. It is shown that the distributed observer-controller guarantees asymptotical stability of the closed-loop system. Numerical simulations are worked out to illustrate the effectiveness of the control laws.

Neural Network Based Adaptive Tracking of Nonlinear Multi-Agent System

In this paper,the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered.An adaptive control strategy is proposed to smooth the agent’s trajectory,and the neural network is constructed to estimate the system’s unknown components.The consensus conditions are demonstrated for tracking a leader with nonlinear dynamics under an adaptive control algorithm in the absence of model uncertainties.Then,the results are extended to the system with unknown time-varying disturbances by applying the neural network estimation to compensating for the uncertain parts of the agents’models.Update laws are designed based on the Lyapunov function terms to ensure the effectiveness of robust control.Finally,the theoretical results are verified by numerical simulations,and a comparative experiment is conducted,showing that the trajectories generated by the proposed method exhibit less oscillation and converge faster.

Observation-Driven Multiple UAV Coordinated Standoff Target Tracking Based on Model Predictive Control

An observation-driven method for coordinated standoff target tracking based on Model Predictive Control(MPC)is proposed to improve observation of multiple Unmanned Aerial Vehicles(UAVs)while approaching or loitering over a target.After acquiring a fusion estimate of the target state,each UAV locally measures the observation capability of the entire UAV system with the Fisher Information Matrix(FIM)determinant in the decentralized architecture.To facilitate observation optimization,only the FIM determinant is adopted to derive the performance function and control constraints for coordinated standoff tracking.Additionally,a modified iterative scheme is introduced to improve the iterative efficiency,and a consistent circular direction control is established to maintain long-term observation performance when the UAV approaches its target.Sufficient experiments with simulated and real trajectories validate that the proposed method can improve observation of the UAV system for target tracking and adaptively optimize UAV trajectories according to sensor performance and UAV-target geometry.

A METHOD FOR PREDICTING SHORT-AND MID-RANGE TURNING AND ACCELERATION MOTION TREND OF TROPICAL CYCLONES OVER THE SOUTH CHINA SEA

An approach is proposed for predicting turning and acceleration motion trend of the tropical cyclones over the South China Sea for 72 h in the extrapolated track coordinates.Cross-track (CT)and along-track(AT)components are defined according to the persistently extrapolated track coodinates based on observed positions at the initial and past 24 h times.A kind of straight- forward measure may be provided with CT and AT components for typhoon turning motion and ac- celeration motion.Canonical correlation analysis(CCA)is performed to reveal the correlaotions be- tween tropical cyclone tracks and environmental 500 hPa geopotential height fields.A stepwise dis- criminate analysis technique is adopted to derive the classification functions of the respective three categories for AT and CT components.Especially,categorical combinations of CT and AT compo- nents are divided into possible 9 regions corresponding with tropical cyclone behaviors.Not only can 9 motion trends of a tropical cyclone be predicted,but also the location and its maximum error at least in certain direction are available.The perfect prediction(PP)verifications indicate that the percent corrects for the CT and AT categories are 67% and 69% in the independent samples,73% and 53% in the dependent samples,respectively,higher than that of 33.3% for random chance; moreover,the rate for successfully forecasting that in which one of the nine regions the tropical cy- clones will fall at 72 h is about 40%,also higher than the stochastic probability of 11%.The method has been proved to be skillful and promising.