Development of a stochastic predictive PID controller

Dynamic system model predict and adaptive controller design

“…Tuffs in 1987 [3], [5], [13], can be applied to the adaptive controls when the system parameter have the variety, time delay and can find out the best PID control parameters with this method [4], [5], [6], [8], [10]. The main control method introduce as follow:…”

State-Space Digital PI Controller Design for Linear Stochastic Multivariable Systems with Input Delay

“…PID, as the most popular controller of simple fi xed structure in the fi eld, is not synthesized from process or disturbance model, and therefore subject to the question: How close do PID controllers achieve ideal performance in term of minimum variance under stochastic disturbance? To achieve such a goal, Miller et al (1995) and Kowk et al (2000) derived the stochastic discrete predictive PID control law by approximating the generalized predictive control (GPC) with steady-state weighting. Based on generalized minimum variance control (GMVC), some self-tuning PID schemes (Miura et al, 1998;Yamamoto et al, 1999;Sato et al, 2002) have been proposed for discrete-time systems in face of stochastic disturbances.…”

Self-tuning PID control using recurrent wavelet neural networks

“…Yamamoto et al [2] explored a design of self-tuning PID controllers with minimization of a generalized control cost function, and theoretically proved its closed-loop stability. Furthermore, Miller et al [3] studied the development of a stochastic predictive PID controller. Omatu, Lu and Tsai and other authors in [4][5][6][7] proposed an adaptive generalized minimized variance temperature control based on the predictive model, and deduced a self-tuning PID control for the plants modeled by the second-order models with time delay, and then obtained satisfactory properties for plastic injection molding temperature control process.…”

“…INTRODUCTION PID control has the benefits of simple structure, robustness, and easy tuning, and has been widely used as a prevalence control method in industry [1][2][3][4][5][6][7]. Over present and past decades, self-tuning PID control has been extensively developed and has also been shown to have four features, including (i) it can be used to eliminate errors from an incorrect model, or a model with parameter variations and external disturbances; (ii) it can achieve good control performance for a wide range of control systems; (iii) it can be robust against modeling error and parameter variations; (iv) it can be applicable to systems with having variable time-delay, non-minimum phase or open-loop unstable properties.…”

“…These self-tuning PID controllers can be roughly classified into four technical categories based on their design approaches: mathematical modeling, limit cycling, automatic step test and intelligent tuning [1][2][3][4][5][6][7]. For the mathematical modeling approach, Vega et al [1] issued a kind of self-tuning PID controller minimizing the quadratic cost function involving predictive outputs, and their computer simulations illustrated the proposed method was applied to the systems with nonminimum-phase or having variable time-delay.…”