Study On Control And Optimization Problem Of Nonlinear Systems With Limited Communication Resources

Nonlinear systems control is one of the hot research issues in modern control theory,because most of the practical systems are nonlinear,or can be transformed into the form of nonlinear systems.The popularity of computers and large-scale networks occupies a great deal of communication resources.Event-triggered control and quantitative strategy can reduce the unnecessary bandwidth occupation.The overall performance of network control systems is guaranteed and the communication resources are saved.Therefore,event-triggered control and quantitative strategy are worth studying.In this thesis,an effective controller is proposed for strict-feedback nonlinear system/nonlinear multi-agent systems by using the gain method,event-triggered control and quantitative strategy.The control and optimization of the systems are proved according to the Lyapunov stability theory and distributed gradient projection algorithm.The research contents of this thesis include the following five aspects:1.Event-triggered-based control of strict-feedback nonlinear systemThe event-triggered communication control problem for strict-feedback nonlinear system is investigated.Only the output of the system can be measured directly.The unmeasured state makes the control problem more challenging.For this,firstly,the communication resources of the whole system are saved by designing two event-triggered communication schemes from sensor to controller and from controller to actuator.At the same time,Zeno behavior can be excluded under the proposed triggering schemes.Secondly,the state of the system is estimated by establishing an observer.The controller is proposed according to the estimated state information and the parameters of the state observer,controller and event-trigger mechanism are jointly designed.Finally,the state of the closed-loop system converges to the origin.2.Distributed control of nonlinear multi-agent systems with static quantizationThe distributed control problem for strict-feedback nonlinear multi-agent systems with uniform quantization and logarithmic quantization is investigated.Firstly,the uniform quantizer or the logarithmic quantizer is considered in the communication channels within and between agents,which reduces the information transmission in spatial aspects.Secondly,an observer is constructed for each agent to estimate the unmeasurable state.Furthermore,an observer-based distributed control protocol is proposed for nonlinear multi-agent systems.Finally,the δ-asymptotic distributed consensus is achieved under the uniform quantizer and the asymptotic distributed consensus is achieved under the logarithmic quantizer.3.Distributed control of nonlinear multi-agent systems with dynamic quantizationThe distributed control problem for strict-feedback nonlinear multi-agent systems with dynamic quantization is investigated.Firstly,the uniform quantizer with scaling function(dynamic quantizer)is employed in the communication channels to reduce the communication burden of the systems.Secondly,an observer is designed for each agent with the help of dynamic gain function,which rebuilt the states of the nonlinear multi-agent systems.Finally,a distributed control protocol is proposed by jointly designing the dynamic quantizer and observer,which achieves distributed asymptotic consensus of nonlinear multi-agent systems with either known or unknown Lipschitz constant through[log2(2R)]-bit information exchange between each pair of agents.4.Event-triggered-based distributed tracking control of nonlinear multi-agent systems with dynamic quantizationThe event-triggered distributed tracking control problem for nonlinear multiagent systems with dynamic quantization is investigated.Firstly,limited capacity of the communication channels within and between agents is considered jointly in temporal and spatial aspects.Event-trigger mechanism and dynamic quantizer are set up to reduce information transmission.Secondly,neural network is utilized to handle the unknown nonlinear functions.Finally,in order to estimate the unmeasurable states,a neural network-based state observer is designed for each agent by using dynamic gain function.To settle the difficulty caused by the coupling effects of event-triggered conditions,scaling function in dynamic quantizers and observers,a distributed control protocol with estimated information of its neighbors is designed,which ensures distributed consensus tracking of the nonlinear multi-agent systems without incurring Zeno behavior.5.Distributed constrained optimization of nonlinear multi-agent systemsThe distributed constrained optimization problem for nonlinear multi-agent systems is studied.Because of the complex structure of nonlinear multi-agent systems,it is very difficult to design control protocol directly.To do this,firstly,we design auxiliary system for each agent by using the gradient algorithm and the projection algorithm.The state of the auxiliary system can asymptotically converge to the solution of the constrained optimization problem.Secondly,a controller is designed for each agent.When the constant coefficients of the nonlinear functions is known,the output of the nonlinear multi-agent systems can asymptotically converge to the auxiliary variables.When the constant coefficients of the nonlinear functions is unknown,the tracking errors of the output of the nonlinear multi-agent systems to the auxiliary variables can converge to an arbitrary neighborhood of zero.Finally,the designed auxiliary systems and controllers guarantee the distributed constrained optimization of nonlinear multiagent systems.