We conducted an experiment of a visuomotor tracking task undertaken by human participants and compared it with numerical simulations of the same task performed by a nonlinear stochastic model comprising additive and multiplicative white Gaussian noise, state feedback terms, and a deadband function. We identified the model parameters using particle swarm optimization to minimize squared residuals between the probability density functions (PDFs) of the human and those of the model. All experimentally obtained PDFs were in close agreement with those simulated by the model. We finally propose a reduced model for system identification in order to decrease the number of model parameters and demonstrate that it also reproduces accurate PDFs without prior knowledge of an experimental system.
In pronunciation practice, the trainees essentially need interactive supports to progress. We have developed a pronunciation training system, where the lip posture and tongue shape that are estimated by the formants extracted by AR model using Burg’s method are shown to the trainee. Realization of real time operations is one of the most important problems in this system. However, formants estimation requires long computation time. In order to avoid spectrum estimation, we attempt to reconstruct these visual instructions from the reflection coefficients of Burg’s method. Here, we investigate relationship between the acoustic feature or articulation manner of the sound and the reflection coefficients. The vocal tract area function calculated from the reflection coefficient was confirmed to acquire feature of tongue shape extracted from MR image taken during uttering more precisely than the formant. This gave possibility to improve visual support way based on the formants.
This paper is concerned with the hydrogen ratio control in a horizontal continuous annealing furnace used in steelmaking. We first model the nonlinear dynamics of the atmosphere in the furnace so that the hydrogen ratio control may be carried out by applying control theory. We then determine an appropriate equilibrium around which its linearized model is determined. We then take a two-step procedure for designing a linear controller, in which we first apply a minor feedback loop for pressure control and then apply integral compensation so that the hydrogen ratio tracks the reference input without any steady state error. The effectiveness of the resulting controller is verified through a simulation with the original nonlinear model of the furnace.
This paper proposes a decision method which is to change better parts variation for customers by estimating their needs, establishing a better schedule for a manufacturer and providing better products for customers. This paper optimizes part selection and production scheduling by using an optimization method which imitates negotiation among manufacturer and customers, and improves specification of mass-parts for next term. Customers memorize and share a history of negotiation as pheromone for cooperating among customers. The proposed decision method attempts to change parts variation for customers by estimating their needs from pheromone distribution.
The output of photovoltaic (PV) systems is sensitive to environmental changes such as solar radiation and panel temperature. In addition, when PV cells are partially shaded, the power consumption characteristics become multimodal. To obtain the maximum power adaptively according to such operating conditions, particle swarm optimization (PSO)-based maximum power point tracking (MPPT) control methods have been proposed so far. However, these conventional methods still cannot cope with monotonically small changes of operating conditions. In this paper, a PSO-based MPPT control method that can resolve this problem is proposed by introducing appropriate conditions and methods in the algorithm. In simulation, the proposed method is compared with several existing methods to illustrate its effectiveness.