Shinnosuke Mori

Data-Driven Skill-Based PID Control of a Pilot-Scale Helicopter Model

Lots of works for representing human skills which are cultivated in long experiences have been reported. The technique for representing human skills plays an important role in various industries, for example, manufacturing systems, production systems and process systems. In this paper, a skill-based PID controller is newly proposed, which extracts skills of human experts as PID gains. This controller is designed by using a data-driven (DD)control approach. The DD approach is based on the local modeling, and attracted attention as a useful tool to express the nonlinear model. The effectiveness of the proposed skill-based PID control scheme is experimentally investigated by employing for a helicopter control model.

Design and experimental evaluation of a data-driven skill-based PID controller

Recently, lots of works for representing human skills which are cultivated in long experiences have been reported. The technique for representing human skills plays an important role in various industries, for example, manufacturing systems, production systems and process systems. In this paper, a skill-based PID controller is newly proposed, which extracts skills of human experts as PID gains. This controller is designed by using a data-driven (DD)control approach. The DD approach is based on the local modeling, and recently attracted attention as a useful tool to express the nonlinear model. The effectiveness of the proposed skill-based PID control scheme is experimentally investigated by employing for a helicopter control model.

Design of Neural Networks Based FRIT PID Controllers and Its Applications

Abstract Some design schemes of model-free controllers which do not require any system models have been considered in the last decade. FRIT(Fictitious Reference Iterative Tuning) method that directly computes the control parameters from the operating data have been proposed as the one of model-free controllers. FRIT has some useful practical features. One is that it does not require system identification. Another is that the control parameters can be directly computed using only a set of closed loop input/output data and the desired output signal. The calculations of the control parameters needs the optimization of the cost functions. The ordinary approach is the gradient method. However, this calculations derives only linear parameters. Therefore, the applications of FRIT are limited for linear systems. In this paper, a new approach to the discrete FRIT-based nonlinear PID control is proposed. The neural network is utilized for the optimization of FRIT. PID parameters are adequately adjusted corresponding to the nonlinear properties. The conventional schemes by using the neural networks require the information of system Jacobian to update weighting factors. This proposed method can calculate the control parameters without the information of system Jacobian or system parameters except for the information about the time-delay.

Design of a Human Skill-Based Controller Using Data-Driven Control Approach

Recently, lots of works for representing human skills which are cultivated in long experiences have been reported. The technique for representing human skills plays an important role in various industries, for example, manufacturing systems, production systems and process systems. In this paper, a skill-based PID controller is newly proposed, which extracts skills of human experts as PID gains. This controller is designed by using a data-driven (DD) control approach. The DD approach is based on the local modeling, and recently attracted attention as a useful tool to express the nonlinear model. The effectiveness of the proposed skill-based PID control scheme is experimentally investigated by employing for a helicopter control model