Direct tuning of gain-scheduled controller for electro-pneumatic clutch position control

Abstract

This study proposes a gain-scheduled controller with direct tuning for the position control of a pneumatic clutch actuator that is installed in heavy-duty trucks. Pneumatic clutch actuators are highly nonlinear systems and cannot be easily controlled. Industries require a simple controller design that is easy to understand and requires few trial-and-error calibrations. Therefore, we adopted a gain-scheduled proportional integral derivative (PID) control law, which is a well-known and easy-to-understand nonlinear control method. In this approach, a gain scheduler is expressed using polynomials composed of coefficient parameters and controlled object states. The unknown coefficient parameters of the polynomials are directly tuned from the controlled object input/output data without having to use a controlled object model. The proposed controller design procedure is simple and does not require system identification or trial-and-error tuning. The effectiveness of the proposed method is verified by an experiment using an actual vehicle. The experimental results confirm the effectiveness of the proposed method for the position control of pneumatic clutch actuators.

Non-Iterative Data-Driven Tuning of Model-Free Control Based on an Ultra-Local Model

“…L2-norm regularization was introduced (33) to suppress overlearning. L1-norm regularization can also be applied to obtain a sparse controller, as in the literature [27]. However, since there are only four MFC design parameters, we employ L2-norm regularization, with which we can obtain an analytical solution.…”

“…MFAC-DLT, MFC-ULM, and AFC can be applied to nonlinear systems and have been widely studied [10][11][12][13][14][15][16][17][18][19][20]. Additionally, control system design methods that do not use system models to be controlled are being applied to industrial systems, such as process systems and automotive systems [21][22][23][24][25][26][27].…”

Direct Data-Driven Tuning of Look-Up Tables for Feedback Control Systems

“…Two-dimensional look-up table [17,18]. Next, the basis function 𝜙 (𝑥, 𝑐) is introduced to obtain the generalized expression for (10). This function finds the four nodes surrounding the current operating point and computes the corresponding normalized region using (11) [17,18].…”

“…Set the reference model 𝑀 and prefilter 𝐿. Similar to previous studies [5,10,14], the prefilter used in this paper is 𝑀 .…”

“…Virtual Reference Feedback Tuning (VRFT) [4,5] and Fictitious Reference Iterative Tuning (FRIT) [6] have attracted attention because controller parameters can be obtained offline from a set of input/output data without repeated experiments. The above control methods, which do not use models of the controlled object, are also being applied to industrial systems [7][8][9][10]. VRFT and FRIT have not only been applied to linear systems but they have been extended to nonlinear systems [11][12][13][14], including database-driven FRIT [11], VRFT using neural networks [12], VRFT for linear parameter varying systems [13], and VRFT for sparse gain-scheduled PID [14].…”