This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used ...This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.展开更多
Comprehensive study on novel Linear-Dispersion Division Multiple-Access(LDDMA) for multi-user uplink Multiple-Input Multiple-Output(MIMO)systems is proposed.In the new multi-plexing scheme,each user’s information sym...Comprehensive study on novel Linear-Dispersion Division Multiple-Access(LDDMA) for multi-user uplink Multiple-Input Multiple-Output(MIMO)systems is proposed.In the new multi-plexing scheme,each user’s information symbol is dispersed by a User-Specific Matrix(USM)both inspace and time domain and linearly combined at base-station side.And a simple random search al-gorithm,based on capacity maximization criteria,is developed to generate a bank of USMs.Simulationresults are presented to demonstrate the advantages of LDDMA.When the Bit Error Rate(BER)reaches 10–3,the performance gains are 3dB and 5dB,compared with Time-Division Linear DispersionCodes(TD-LDC)and BLAST,respectively.展开更多
An adaptive inverse controller for nonliear discrete-time system is proposed in this paper. A compound neural network is constructed to identify the nonlinear system, which includes a linear part to approximate the no...An adaptive inverse controller for nonliear discrete-time system is proposed in this paper. A compound neural network is constructed to identify the nonlinear system, which includes a linear part to approximate the nonlinear system and a recurrent neural network to minimize the difference between the linear model and the real nonlinear system. Because the current control input is not included in the input vector of recurrent neural network (RNN), the inverse control law can be calculated directly. This scheme can be used in real-time nonlinear single-input single-output (SISO) and multi-input multi-output (MIMO) system control with less computation work. Simulation studies have shown that this scheme is simple and affects good control accuracy and robustness.展开更多
The paper addresses the global output tracking of a class of multi-input multi-output （MIMO） nonlinear systems affected by disturbances, which are generated by a known exosystem. An adaptive controller is designed b...The paper addresses the global output tracking of a class of multi-input multi-output （MIMO） nonlinear systems affected by disturbances, which are generated by a known exosystem. An adaptive controller is designed based on the proposed observer and the backstepping approach to asymptotically track arbitrary reference signal and to guarantee the boundedness of all the signals in the closed loop system. Finally, the numerical simulation results illustrate the effectiveness of the ProPosed scheme.展开更多
基金Supported by the National Natural Science Foundation of China(21076179)the National Basic Research Program of China(2012CB720500)
文摘This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.
基金the National Natural Science Foundation of China(No.60572066)863 Program of China(No.2006AA01Z266).
文摘Comprehensive study on novel Linear-Dispersion Division Multiple-Access(LDDMA) for multi-user uplink Multiple-Input Multiple-Output(MIMO)systems is proposed.In the new multi-plexing scheme,each user’s information symbol is dispersed by a User-Specific Matrix(USM)both inspace and time domain and linearly combined at base-station side.And a simple random search al-gorithm,based on capacity maximization criteria,is developed to generate a bank of USMs.Simulationresults are presented to demonstrate the advantages of LDDMA.When the Bit Error Rate(BER)reaches 10–3,the performance gains are 3dB and 5dB,compared with Time-Division Linear DispersionCodes(TD-LDC)and BLAST,respectively.
基金Supported by the National Natural Science Foundation of China (60575009, 60574036)
文摘An adaptive inverse controller for nonliear discrete-time system is proposed in this paper. A compound neural network is constructed to identify the nonlinear system, which includes a linear part to approximate the nonlinear system and a recurrent neural network to minimize the difference between the linear model and the real nonlinear system. Because the current control input is not included in the input vector of recurrent neural network (RNN), the inverse control law can be calculated directly. This scheme can be used in real-time nonlinear single-input single-output (SISO) and multi-input multi-output (MIMO) system control with less computation work. Simulation studies have shown that this scheme is simple and affects good control accuracy and robustness.
基金This research is supported by the National Nature Science Foundation of China under Grant No.60574007the Nature Science Foundation of Shandong Province under Grant No.Y2003G02.
文摘The paper addresses the global output tracking of a class of multi-input multi-output （MIMO） nonlinear systems affected by disturbances, which are generated by a known exosystem. An adaptive controller is designed based on the proposed observer and the backstepping approach to asymptotically track arbitrary reference signal and to guarantee the boundedness of all the signals in the closed loop system. Finally, the numerical simulation results illustrate the effectiveness of the ProPosed scheme.
