This paper develops a feedforward neural network based input output model for a general unknown nonlinear dynamic system identification when only the inputs and outputs are accessible observations. In the developed m...This paper develops a feedforward neural network based input output model for a general unknown nonlinear dynamic system identification when only the inputs and outputs are accessible observations. In the developed model, the size of the input space is directly related to the system order. By monitoring the identification error characteristic curve, we are able to determine the system order and subsequently an appropriate network structure for systems identification. Simulation results are promising and show that generic nonlinear systems can be identified, different cases of the same system can also be discriminated by our model.展开更多
Industrial noise can be successfully mitigated with the combined use of passive and Active Noise Control (ANC) strategies. In a noisy area, a practical solution for noise attenuation may include both the use of baffle...Industrial noise can be successfully mitigated with the combined use of passive and Active Noise Control (ANC) strategies. In a noisy area, a practical solution for noise attenuation may include both the use of baffles and ANC. When the operator is required to stay in movement in a delimited spatial area, conventional ANC is usually not able to adequately cancel the noise over the whole area. New control strategies need to be devised to achieve acceptable spatial coverage. A three-dimensional actuator model is proposed in this paper. Active Noise Control (ANC) usually requires a feedback noise measurement for the proper response of the loop controller. In some situations, especially where the real-time tridimensional positioning of a feedback transducer is unfeasible, the availability of a 3D precise noise level estimator is indispensable. In our previous works [1,2], using a vibrating signal of the primary source of noise as an input reference for spatial noise level prediction proved to be a very good choice. Another interesting aspect observed in those previous works was the need for a variable-structure linear model, which is equivalent to a sort of a nonlinear model, with unknown analytical equivalence until now. To overcome this in this paper we propose a model structure based on an Artificial Neural Network (ANN) as a nonlinear black-box model to capture the dynamic nonlinear behaveior of the investigated process. This can be used in a future closed loop noise cancelling strategy. We devise an ANN architecture and a corresponding training methodology to cope with the problem, and a MISO (Multi-Input Single-Output) model structure is used in the identification of the system dynamics. A metric is established to compare the obtained results with other works elsewhere. The results show that the obtained model is consistent and it adequately describes the main dynamics of the studied phenomenon, showing that the MISO approach using an ANN is appropriate for the simulation of the investigated process. A clear conclusion is reached highlighting the promising results obtained using this kind of modeling for ANC.展开更多
对于一类具有三角结构的单输入单输出的不确定非线性系统,用反步法(backstepping)和动态面控制方法(dynamic surface control technique)设计了一种使用神经网络补偿未知非线性的L2--增益鲁棒控制器.控制器设计中没有直接解HJI(Hamilton...对于一类具有三角结构的单输入单输出的不确定非线性系统,用反步法(backstepping)和动态面控制方法(dynamic surface control technique)设计了一种使用神经网络补偿未知非线性的L2--增益鲁棒控制器.控制器设计中没有直接解HJI(Hamilton-Jacobi-Isaac)不等式.合理的选择了L2--增益性能指标,将被控系统各个状态变量的跟踪误差和神经网络各权值的跟踪误差看作整个控制系统的各个状态变量,并用Lyapunov定理和HJI不等式证明了使用提出的控制器后,这些状态变量具有小于等于事先规定的正实数γ的L2--增益.当系统的扰动信号为零向量时,提出的控制器在原点是大范围渐近稳定的.仿真研究结果表明所提出的控制器具有很好的跟踪性能和很强的鲁棒性.展开更多
文摘This paper develops a feedforward neural network based input output model for a general unknown nonlinear dynamic system identification when only the inputs and outputs are accessible observations. In the developed model, the size of the input space is directly related to the system order. By monitoring the identification error characteristic curve, we are able to determine the system order and subsequently an appropriate network structure for systems identification. Simulation results are promising and show that generic nonlinear systems can be identified, different cases of the same system can also be discriminated by our model.
基金CAPES and CNPq(Brazilian federal research agencies)for their financial support.
文摘Industrial noise can be successfully mitigated with the combined use of passive and Active Noise Control (ANC) strategies. In a noisy area, a practical solution for noise attenuation may include both the use of baffles and ANC. When the operator is required to stay in movement in a delimited spatial area, conventional ANC is usually not able to adequately cancel the noise over the whole area. New control strategies need to be devised to achieve acceptable spatial coverage. A three-dimensional actuator model is proposed in this paper. Active Noise Control (ANC) usually requires a feedback noise measurement for the proper response of the loop controller. In some situations, especially where the real-time tridimensional positioning of a feedback transducer is unfeasible, the availability of a 3D precise noise level estimator is indispensable. In our previous works [1,2], using a vibrating signal of the primary source of noise as an input reference for spatial noise level prediction proved to be a very good choice. Another interesting aspect observed in those previous works was the need for a variable-structure linear model, which is equivalent to a sort of a nonlinear model, with unknown analytical equivalence until now. To overcome this in this paper we propose a model structure based on an Artificial Neural Network (ANN) as a nonlinear black-box model to capture the dynamic nonlinear behaveior of the investigated process. This can be used in a future closed loop noise cancelling strategy. We devise an ANN architecture and a corresponding training methodology to cope with the problem, and a MISO (Multi-Input Single-Output) model structure is used in the identification of the system dynamics. A metric is established to compare the obtained results with other works elsewhere. The results show that the obtained model is consistent and it adequately describes the main dynamics of the studied phenomenon, showing that the MISO approach using an ANN is appropriate for the simulation of the investigated process. A clear conclusion is reached highlighting the promising results obtained using this kind of modeling for ANC.
文摘对于一类具有三角结构的单输入单输出的不确定非线性系统,用反步法(backstepping)和动态面控制方法(dynamic surface control technique)设计了一种使用神经网络补偿未知非线性的L2--增益鲁棒控制器.控制器设计中没有直接解HJI(Hamilton-Jacobi-Isaac)不等式.合理的选择了L2--增益性能指标,将被控系统各个状态变量的跟踪误差和神经网络各权值的跟踪误差看作整个控制系统的各个状态变量,并用Lyapunov定理和HJI不等式证明了使用提出的控制器后,这些状态变量具有小于等于事先规定的正实数γ的L2--增益.当系统的扰动信号为零向量时,提出的控制器在原点是大范围渐近稳定的.仿真研究结果表明所提出的控制器具有很好的跟踪性能和很强的鲁棒性.
