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
Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial...Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial basis function (RBF) neural networks identification technology is applied to set up the temperature nonlinear model of MCFC stack, and the identification structure, algorithm and modeling training process are given in detail. A fuzzy controller of MCFC stack is designed. In order to improve its online control ability, a neural network trained by the I/O data of a fuzzy controller is designed. The neural networks can memorize and expand the inference rules of the fuzzy controller and substitute for the fuzzy controller to control MCFC stack online. A detailed design of the controller is given. The validity of MCFC stack modelling based on neural networks and the superior performance of the fuzzy neural networks controller are proved by Simulations.展开更多
One synthetical control method of AGC/LPC system based on intelligence control theory-neural networks internal model control method is presented. Genetic algorithm (GA) is applied to optimize the parameters of the neu...One synthetical control method of AGC/LPC system based on intelligence control theory-neural networks internal model control method is presented. Genetic algorithm (GA) is applied to optimize the parameters of the neural networks. Simulation results prove that this method is effective.展开更多
Day by day, networked control system（NCS） methods have been promoted for distributed closed-loop control systems.Interestingly, the integration of control and computing theories enhanced the development of networked...Day by day, networked control system（NCS） methods have been promoted for distributed closed-loop control systems.Interestingly, the integration of control and computing theories enhanced the development of networked control systems through remote control for wide applications employing the internet. Two further directions to networked control technology are LeaderFollower systems and model predictive control systems. Cloud control system is looked at an extension of networked control systems（NCS） using internet of things（IOT） methodologies. In this paper, a comprehensive literature survey of the new technology of control systems application performed on cloud computing is presented.展开更多
In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control （ABFCNC） system for motion/force control of the mobile-manipulator robot （MMR） is proposed. By applying t...In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control （ABFCNC） system for motion/force control of the mobile-manipulator robot （MMR） is proposed. By applying the ABFCNC in the tracking-position controller, the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, uncertain disturbances. Based on the tracking position-ABFCNC design, an adaptive robust control strategy is also developed for the nonholonomicconstraint force of the MMR. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Therefore, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation results.展开更多
In this paper, we address the characteristic model-based discrete-time consensus problem of networked robotic manipulators with dynamic uncertainties. The research objective is to achieve joint-position consensus of m...In this paper, we address the characteristic model-based discrete-time consensus problem of networked robotic manipulators with dynamic uncertainties. The research objective is to achieve joint-position consensus of multiple robotic agents interconnected on directed graphs containing a spanning tree. A novel characteristic model-based distributed adaptive control scenario is proposed with a state-relied projection estimation law and a characteristic model-based distributed controller. The performance analysis is also unfolded where the uniform ultimate boundedness(UUB) of consensus errors is derived by resorting to the discrete-time-domain stability analysis tool and the graph theory. Finally, numerical simulations illustrate the effectiveness of the proposed theoretical strategy.展开更多
This paper addresses a channel scheduling problem for group of dynamically decoupled nonlinear subsystems with actuators connected through digital communication channels and controlled by a centralized controller. Due...This paper addresses a channel scheduling problem for group of dynamically decoupled nonlinear subsystems with actuators connected through digital communication channels and controlled by a centralized controller. Due to the limited communication capacity, only one channel can be activated and hence there is only one pair of sensor and actuator can communicate with the controller at each time instant. In addition, the communication channels are not reliable so Markovian packed dropout is introduced. A predictive control framework is adopted for controller/scheduler co-design to alleviate the performance loss caused by the limited communication capacity. Instead of sending a single control value, the controller sends a sequence of predicted control values to a selected actuator so that there are control input candidates which can be fed to the subsystem when the actuator does not communicate with the controller. A stochastic algorithm is proposed to schedule the usage of the communication medium and sufficient conditions on stochastic stability are given under some mild assumptions.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.
文摘Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial basis function (RBF) neural networks identification technology is applied to set up the temperature nonlinear model of MCFC stack, and the identification structure, algorithm and modeling training process are given in detail. A fuzzy controller of MCFC stack is designed. In order to improve its online control ability, a neural network trained by the I/O data of a fuzzy controller is designed. The neural networks can memorize and expand the inference rules of the fuzzy controller and substitute for the fuzzy controller to control MCFC stack online. A detailed design of the controller is given. The validity of MCFC stack modelling based on neural networks and the superior performance of the fuzzy neural networks controller are proved by Simulations.
文摘One synthetical control method of AGC/LPC system based on intelligence control theory-neural networks internal model control method is presented. Genetic algorithm (GA) is applied to optimize the parameters of the neural networks. Simulation results prove that this method is effective.
基金supported by the deanship of scientific research(DSR) at KFUPM through distinguished professorship research project(No.IN141003)
文摘Day by day, networked control system（NCS） methods have been promoted for distributed closed-loop control systems.Interestingly, the integration of control and computing theories enhanced the development of networked control systems through remote control for wide applications employing the internet. Two further directions to networked control technology are LeaderFollower systems and model predictive control systems. Cloud control system is looked at an extension of networked control systems（NCS） using internet of things（IOT） methodologies. In this paper, a comprehensive literature survey of the new technology of control systems application performed on cloud computing is presented.
基金supported by the National Natural Science Foundation of China(Nos.6117075,60835004)the National High Technology Research and Development Program of China(863 Program)(Nos.2012AA111004,2012AA112312)
文摘In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control （ABFCNC） system for motion/force control of the mobile-manipulator robot （MMR） is proposed. By applying the ABFCNC in the tracking-position controller, the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, uncertain disturbances. Based on the tracking position-ABFCNC design, an adaptive robust control strategy is also developed for the nonholonomicconstraint force of the MMR. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Therefore, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation results.
基金supported by the National Natural Science Foundation of China(Grant Nos.6133300861273153&61304027)
文摘In this paper, we address the characteristic model-based discrete-time consensus problem of networked robotic manipulators with dynamic uncertainties. The research objective is to achieve joint-position consensus of multiple robotic agents interconnected on directed graphs containing a spanning tree. A novel characteristic model-based distributed adaptive control scenario is proposed with a state-relied projection estimation law and a characteristic model-based distributed controller. The performance analysis is also unfolded where the uniform ultimate boundedness(UUB) of consensus errors is derived by resorting to the discrete-time-domain stability analysis tool and the graph theory. Finally, numerical simulations illustrate the effectiveness of the proposed theoretical strategy.
基金supported by the Energy Innovation Research Programme of Singapore under Grant No.NRF2013EWT-EIRP004-012Qilu Youth Scholar Discipline Construction Funding from Shandong University+1 种基金the National Natural Science Foundation of China(NSFC)under Grant Nos.61573220,61633014Projects of Major International(Regional)Joint Research Program NSFC under Grant No.61720106011
文摘This paper addresses a channel scheduling problem for group of dynamically decoupled nonlinear subsystems with actuators connected through digital communication channels and controlled by a centralized controller. Due to the limited communication capacity, only one channel can be activated and hence there is only one pair of sensor and actuator can communicate with the controller at each time instant. In addition, the communication channels are not reliable so Markovian packed dropout is introduced. A predictive control framework is adopted for controller/scheduler co-design to alleviate the performance loss caused by the limited communication capacity. Instead of sending a single control value, the controller sends a sequence of predicted control values to a selected actuator so that there are control input candidates which can be fed to the subsystem when the actuator does not communicate with the controller. A stochastic algorithm is proposed to schedule the usage of the communication medium and sufficient conditions on stochastic stability are given under some mild assumptions.
