In this paper, we discuss practical applicability of the data-driven designed dynamic quantizer (D4Q), proposed recently by Fujimoto and Minami. In many industrial control applications, the target systems only accept discrete-valued control inputs, mainly to suppress manufacturing cost, system complexity and network bandwidth. Quantizers, which converts control-valued inputs to discrete-valued ones, are thus required in those applications. One of a promising algorithm for this purpose is the so-called “optimal dynamic quantizer (ODQ)”; it minimizes the discrepancy between the outputs with and without discretization, based on the precise linear model of the target system. Then Fujimoto and Minami proposed a novel approach to design a dynamic quantizer, not from the system model, but directly from the input-output data. In this paper, we apply the D4Q design method to an electro-mechanical control system, possibly containing elements that are difficult to model, such as friction. Through the experimental results, we show that the D4Q is effective compared to the existing quantization methods, and is more robust against unmodelled element than the conventional ODQ.

This paper discusses data-driven parameter tuning of feedfoward controller. In particular, the goal of this paper is to propose a method whose cost function becomes convex for any controller structure. By focusing on Estimated Response Iterative Tuning (ERIT) and equation-error form identification, this paper proposes a method whose cost function becomes a quadratic function. This paper also proposes an online tuning method of feedforward controller. This online tuning method employs Recursive Least Squares (RLS) algorithm to solve the quadratic function. The effectiveness of the proposed method is demonstrated through practical experiments.

This paper discusses Iterative Learning Control methods which iteratively update the input sequence to track the desired output. In particular, this paper focuses on Quadratically optimal Iterative Inversion Control (Q-IIC) which updates the inverse model of the system. Although the monotonic convergence of the inverse model with Q-IIC has been proven, it does not mean the monotonic decrease of tracking error. From the practical viewpoint, a method which decreases the tracking error monotonically is preferable. Based on these, this paper proposes a modified version of Q-IIC whose monotonic decrease of tracking error is guaranteed. The effectiveness of the proposed method is shown through a practical experiment with motor.

In this paper, we derive a feedback control law that stops a floating target with initial momentum and initial angular momentum. First, the system is formulated as a nonlinear discrete-time system in which the momentum and angular momentum of the target are the states, and the control input is given by the magnitude and position of an impulsive force. Next, a state feedback control law is derived based on Lyapunov's second method, and the behavior of the system is analyzed as an autonomous system under the state feedback control law to prove the global asymptotic stability of the origin. In addition, the stability conditions are presented when there is modeling error in the friction coefficient. Finally, numerical examples also demonstrate its validity.

This note proposes a design method of quasi Sliding Mode Control (SMC) for Linear Parameter-Varying (LPV) systems as the combination of Linear State-Feedback Control (LSFC) and additional internal saturation by extending an LSFC gain parametrization for LTI systems in the literature. A numerical example well illustrates the advantage of our controller.