Akira Yanou

An Extension of Two Degree-of-Freedom of Generalized Predictive Control for M-input M-output Systems Based on State Space Approach

This paper proposes an extension method of two degree- of-freedom generalized predictive control (GPC) for m-input m-output systems by using coprime factorization approach. The extended controller has new design parameter which can change the controller poles without changing the closed-loop poles. And the proposed method reveals the effect of the integral compensation only if there exists modelling error and/or disturbance. Therefore, performance degradation due to an integral compensation, such as a slow response or an excessive control effort, can be avoided when the controlled system has no perturbation.

An Extension of Self-Tuning Two Degree-of-Freedom GPC Based on Polynomial Approach with Computational Savings

This paper proposes an extension of self-tuning two degree-of-freedom generalized predictive control based on polynomial approach with computational savings. When the identified plant parameters converge on true values, the proposed method reveals the effect of the integral compensation only if there exists modeling error or disturbance. And a new design parameter is introduced, which is chosen to poles of the controller without changing closed-loop poles.

An Extension of Two Degree-of-Freedom of Self-Tuning GPC Based on State-space Approach Using Coprime Factorization Approach

This paper proposes an extension of two degree-of-freedom of self-tuning generalized predictive control (GPC) based on state-space approach by using coprime factorization approach. The extended controller has new design parameter which can change controller poles without changing closed-loop poles. When the identified plant parameters converge on true values and the control system has no perturbation, performance degradation due to an integral compensation, such as an excessive control effort, can be avoided.

Feature Extraction and Recognition for Road Sign Using Dynamic Image Processing

In the research field of the intelligent transportation system (ITS) and the roving robot, the driver-support system auto-recognizing road signs and the robot control system auto-recognizing behavior-indication signs are variously suggested. Both systems use the dynamic image processing to acquire features such as the shape and design of signs. Artificial signs valid for the machine vision are sometimes used for these researches. However, in this study, the general road signs adopted in public roads are used, because machines such as robots and vehicles are supposed to live together in real environments. In this paper, nine kinds of signs one third in size such as STOP, NO ENTRY and NO PASSAGE are prepared, and then some experiments were carried out to recognize these signs under various kinds of measurement condition.

Two degree-of-freedom of self-tuning Generalized Predictive Control based on state space approach using a genetic algorithm

Generalized Predictive Control (GPC) achieves a robust tracking for step-type reference signal by including an integrator in advance. Although author has proposed a design scheme of two degree-of-freedom GPC system which reveals an effect of integral compensation only if there exists modeling error or disturbance, a gain for integral compensation must be selected by trial and error. In this paper, a new scheme of two degree-of-freedom of self-tuning GPC system is obtained by using a genetic algorithm for selection of integral gain.

Two degree-of-freedom of Generalized Predictive Control based on polynomial approach using a genetic algorithm

Generalized Predictive Control (GPC) achieves a robust tracking for step-type reference signal by including an integrator in advance. Although author has proposed a design scheme of two degree-of-freedom GPC system which reveals an effect of integral compensation only if there exists modeling error or disturbance, a gain for integral compensation must be selected by trial and error. In this paper, a new scheme of two degree-of-freedom of GPC system based on polynomial approach is obtained by using a genetic algorithm for selection of integral gain.

Two degree-of-freedom generalized predictive control with integral compensation in a multirate system

This paper deals with a multirate control system, where the sampling interval of the plant output is an integer multiple of the hold interval of the control input. In the multirate system, intersample output may oscillate even if a sampled output converges to a reference input. Therefore, this paper proposes a new design method to suppress intersample ripple in the multirate system, the numerical examples demonstrate its effectiveness.

Navigation and path search for roving robot using reinforcement learning

The robot technology is rapidly developing in recent years. In connection with this technology, a robot activity is expected in various places or various environments. Therefore, this study describes 1) how the location of the destination of the robot in real world is measured based on the image obtained by one camera and 2) how the robot is navigated to the destination where a user points out on the display, on which the forward scene is imaged. The cases where there are some obstacles on the way to the destination are considered. The roving robot tries to find the shortest way to the destination based on the information on the locations of the obstacles and the destination by using the reinforcement learning, which is a hopeful candidate in the autonomous control technique. In addition, the measurement method for the indicated location based on the image is described, the simulation result for the path search by using the reinforcement leaning is shown, and the experiment result of navigation in real field is shown. Finally, the main conclusions are summarized.

An extension of constrained receding-horizon predictive control by using coprime factorization approach

This paper proposes an extension of the scheme of constrained receding-horizon predictive control by using the coprime factorization approach. By selecting a newly introduced design parameters, the proposed method has features that not only the controlled system is stable, but also the controller itself has no unstable pole, that is, a strongly stable system is obtained.