Yuji Wakasa

Online Controller Tuning via FRIT and Recursive Least-Squares

Abstract This paper proposes an online type of controller parameter tuning method by modifying the standard fictitious reference iterative tuning method and by utilizing the so-called recursive least-squares (RLS) algorithm, which can cope with variation of plant characteristics adaptively. As used in many applications, the RLS algorithm with a forgetting factor is also applied to give more weight to more recent data, which is appropriate for adaptive controller tuning. Moreover, we extend the proposed method to online tuning of the feed-forward controller of a two-degree-of-freedom control system. Finally, numerical examples are provided to illustrate the effectiveness of the proposed method.

Stochastic Asymptotic Stabilizers for Deterministic Input-Affine Systems Based on Stochastic Control Lyapunov Functions

In this paper, a stochastic asymptotic stabilization method is proposed for deterministic input-affine control systems, which are randomized by including Gaussian white noises in control inputs. The sufficient condition is derived for the diffucion coefficients so that there exist stochastic control Lyapunov functions for the systems. To illustrate the usefulness of the sufficient condition, the authors propose the stochastic continuous feedback law, which makes the origin of the Brockett integrator become globally asymptotically stable in probability.

Eye interface for physically impaired people by Genetic Eye Tracking

Purpose of our works is development of an interface by eye movement for physically impaired people due to spinal cord injury and neurological disease. Eye appearance is changed by eye blinking, head motion, eye movement, and individual difference. Moreover, the system must track the eye and acquire its information simultaneously. For these problems, we use genetic eye tracking method with an artificial iris template. In this paper, we propose an application of the genetic eye tracking, and the effectiveness is evaluated by simulations. As the result, this proposed system achieves high accuracy.

Control-theoretic analysis of exploitation and exploration of the PSO algorithm

The behavior of the particle swarm optimization (PSO) algorithm is analyzed by regarding its dynamics as a system with multiplicative noise and applying control-theoretic analysis methods. In order to evaluate the convergence and diversity of the PSO algorithm, two measures related to the decay rate and l2 gain of the PSO dynamics are used. These measures are characterized by linear matrix inequalities (LMIs) and are therefore efficiently computed by convex optimization tools. As for the measure related to the decay rate, moreover, an alternative condition in terms of nonlinear scalar inequalities is presented which can be checked more shortly and accurately than the LMI. Numerical experiments suggest that the measures are effective enough to evaluate the convergence and diversity of the PSO algorithm, which results in better understanding of the PSO algorithm from the viewpoints of exploitation and exploration abilities.

Intelligent control for pneumatic servo system

This paper presents a novel model reference adaptive control (MRAC) incorporating neural network (NN) for the pneumatic servo system. In the proposed method, the input of the NN are different from which in the conventional method. And there is no need to use the inner parameters of the NN during the learning of the NN. The effectiveness of the proposed scheme is confirmed by experiments using the existent pneumatic servo system.

Restroom Human Detection Using One-Dimensional Brightness Distribution Sensor

As aging society problem goes serious; systems to confirm safety of elders in daily life are expected. In this paper, a sensor, which detects person localization without privacy offending, applying brightness distribution is realized. In the proposed design, the sensor is constructed with a line sensor and cylindrical lens to obtain one-dimensional brightness distribution. Comparing with conventional line sensors, CMOS area sensors are with low cost, and high sensitivity. Therefore, in the proposed sensor, the CMOS area sensor is applied as covered in certain areas physically, so that it behaves as a line sensor. The proposed sensor is able to obtain one-dimensional horizontal brightness distribution that is approximately equal to integration value of each vertical pixel line of two-dimensional image. By employing this method, the information of the target person’s position and movement status can be obtained without using two-dimensional detail texture image.

Optimal error feedback filters for uniform quantizers at remote sensors

Optimal error feedback filters for A/D converters of remote sensors in networked control systems are studied. It is shown that if the transfer function from the quantization error to the output of interest has a minimum phase, then its inverse is the optimal error feedback filter in terms of any kind of norms of errors. For general LTI systems, the design of error feedback filters is formulated as an optimization problem, which can be numerically solved. A design example is provided to demonstrate the effectiveness of our proposed method.

Intelligent IMC-PID control for ultrasonic motor

Ultrasonic motor (USM) causes serious characteristic changes during operation and contains non-linearity caused by frictions. To compensate the characteristics, the internal model control - proportional integral differential (IMC-PID) control which requires only one parameter setting beforehand is widely applied to the control of ultrasonic motor. However, there are limitations of control performance on the conventional fixed-gain type IMC-PID control. So, in this paper we propose an improved method of variable gain type IMC-PID control combined with neural network (NN). In this proposed method, the identification method is adopted to overcome serious characteristic changes and neural network is used to compensate non-linearity. And, the effectiveness of the proposed method has been confirmed by experiments.

Almost sure asymptotic stabilizability for deterministic systems with wiener processes

In this paper, we discuss almost sure asymptotic stability for deterministic systems, which are randomized by adding one-dimensional standard Wiener processes. First, we clarify the difference between the deterministic Lyapunov stability theory and the stochastic stability theory proposed by Hasminskii, and show that local asymptotic stability in probability causes trouble in randomization problems. Second, we describe the Lyapunov stability theory based on almost sure stability, as proposed by Bardi and Cesaroni, and show that the stability is “almost the same” as the deterministic Lyapunov stability. Third, we survey randomization problems briefly, and show that Stratonovich integrals are valid for such problems based on one-dimensional Wiener processes. Finally, we derive the necessary conditions for stochastic Lyapunov functions to ensure almost sure stabilities and discuss the difference between Hasminskiis stabilities and those of Bardi and Cesaroni via linear stochastic systems with numerical simulations.

Stabilization problems of nonlinear systems using feedback laws with Wiener processes

This paper proposes a method to stabilize the origins of deterministic dynamical systems by state feedback control laws with Wiener processes. First, we obtain Ito-type stochastic dynamical systems by randomizing ordinary differential equation systems. Second, we design the diffusion coefficients. Then, we obtain Sontag-type global asymptotically stabilizers based on the stochastic control Lyapunov functions. Moreover, we show the usefulness of our results by some examples, i.e., systems with noncontractible state spaces and nonholonomic systems.