Alternative Approach to Use RLS Algorithm in Multivariable Online Adaptive PID Controllers for MIMO Systems

Advances in Mechanical Engineering

2021

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.

Section: Control Lawmentioning

confidence: 99%

Section: Control Lawmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Advances in Mechanical Engineering

2021

“…Therefore, studies on model-free control and data-driven control (DDC), wherein controller parameters are directly tuned using the input/output data of a controlled object, are attracting attention. 2–21 Model-free control is effective when applied to time-varying systems because optimal parameters can be obtained via online calculations. However, some difficulties are associated with its application to mass products from the viewpoint of stability and CPU performance.…”

Section: Introductionmentioning

confidence: 99%

Direct tuning of the data-driven controller considering closed-loop stability based on a fictitious reference signal

Measurement and Control

2021

The direct tuning of controller parameters, which is based on data-driven control, has been attracting considerable attention because of the ease of its control system design. In practical use, it is important to consider the stability of the closed-loop system and model matching with few design parameters. In this study, we propose a direct tuning method based on a fictitious reference signal that considers the bounded-input bounded-output (BIBO) and model matching without repeating experiments. The proposed method includes two steps. In the first step, the BIBO stability is satisfied. The pole information is lost in the cost function of the conventional method using a fictitious reference signal. Then, we derive a new cost function that can prevent the loss of the pole information. This provides controller parameters that can stabilize the closed-loop system. The model matching between the reference model and the closed-loop system is considered in the second step. When model matching is achieved, the characteristics of the reference model almost match those of the closed-loop system, including the gain and phase margins. The parameters of the reference model are automatically tuned to realize model matching. Using the two-step method, we can obtain parameters considering BIBO stability and the model matching. In addition, there are no design parameters apart from the dealing noise. Two simulations and an experiment were performed on a system with dead time to verify the effectiveness of the proposed two-step method. The results showed that the proposed method provides BIBO stability and model-matched control parameters from the measured data through a one-time experiment without trial and error.

“…The datadriven control, such as Virtual Reference Feedback Tuning (VRFT) [9] and fictitious reference feedback tuning (FRIT) [10,11], is drawing a lot of attention because the controller parameters can be obtained off-line from a set of input/output data without repeated experiments. As model-free control methods for sequentially changing controller parameters online, the adaptive PID control with simultaneous perturbation stochastic approximation algorithm (SPSA) [12], adaptive PID [13,14], and FRIT-RLS [15,16], which is an online version of the FRIT, and so on, have been proposed. It is noteworthy that these model-free controls can be applied to time-varying systems because the control parameters can be adapted in real time.…”

Section: Introductionmentioning

confidence: 99%

Direct tuning method of gain‐scheduled controllers with the sparse polynomials function

Asian Journal of Control

2021

In industry, gain‐scheduled proportional‐integral‐derivative (PID) control is performed for nonlinear systems using a look‐up table (LUT) that is easy to understand. Compared with the fixed PID, there are many more parameters of the scheduler, and it takes a lot of time to tune them. Also, the ROM storage area increases. To address these problems, in this paper, we propose a gain‐scheduled control law using the sparse polynomial functions and a direct parameter tuning method without system identification. The polynomial functions are used instead of LUT to reduce the ROM area. For direct tuning, data‐driven control is formulated so that it can be applied to the gain‐scheduled control, and the optimal parameters are obtained by the LASSO regression, with which the small contributing parameters of the scheduler become zero, and a sparse controller is obtained. The effectiveness of this method was examined by simulation for two types of nonlinear systems. As a result, it was revealed that a sparse controller with a low calculation cost and a reduced ROM area can be directly obtained without knowing the characteristics of the controlled object for a large number of control parameters of the gain scheduler.