Hiroshi Tokutake

Real-time identification method of driver model with steering manipulation

This study proposes a method for real-time identification of a driver model. The proposed method requires only the yaw rate sensor, the steering angle sensor, and velocity sensors that are usually installed in the production car. The identification algorithm involves the division of the recorded data, prefiltering of the divided data, estimation of the drivers desired response, and identification. The prefilter extracts the drivers involuntary response that can be modelled in a simple form. The ideal car response that the driver attempts to track is estimated from the recorded data, and this response is provided to the identification algorithm of the feedback driver model for error tracking. These newly developed methods enable real-time identification under actual driving conditions. The driving simulator experiments and the actual driving tests were performed, and the proposed method was validated. The results show that the time history of the variation in the drivers characteristics can be realised in real time using the proposed method.

Robust Flight Controller Design That Takes into Account Handling Quality

Ar obust flight controller design method, which satisfies handling quality requirements, is shown. The expanded control anticipation parameter is introduced as a new handling quality criterion to treat high-order dynamics. The relations to the C∗ criterion and equivalent systems are shown. The proposed criterion is formulated as a linear matrix inequality, and a robust flight controller that satisfies handling-quality requirements can be designed numerically. In addition, a cost index with a time-weighting function that evaluates several time response features is introduced into controller design. An example that illustrates flight controller design of Multi-Purpose Aviation Laboratory airplane is presented, and flight testing is performed.

Robust Flight Control Design With Eigenstructure Assignment

Abstract Although the H ∞ control method is a powerful tool to design robust controller, it is difficult to attain the performances in time domain. A new design method of H ∞ state feedback controller is proposed which guarantees the robust stability and assigns eigenstructure in the desired regions. Conditions of robust stability and closed loop eigenstructure assignment are solved in LMI form with one nonlinear constraint. This method is validated with numerical example of flight control design.

Handling qualities evaluation method based on actual driver characteristics

The present study proposes an objective handling qualities evaluation method using driver-in-the-loop analysis. The driving simulator experiments were performed for various driving conditions, drivers and vehicle dynamics. The response characteristics of the driver model and the closed-loop system were analyzed. The analysis revealed the driving strategies clearly, indicating the importance of closed-loop analysis. Using the identified driver model and its strategies, a cost function of the handling qualities was constructed. The cost function can be used to estimate the handling qualities analytically from the vehicle dynamics. The proposed method was validated by comparison with the handling qualities evaluation rated by the drivers comments.

Controller Design Using Standard Operator Model

Manual tracking experiments were conducted to investigate aircraft operator responses. In these experiments, the operators attempted to track a random reference input. Afterward, the operators specified the workload required for the task. Using this information, operator models were identified and an operator control philosophy was derived. This philosophy was used to develop a controller design method with the standard operator model to reduce the workload. A standard operator model and controller were developed to allow the operator to control the plant easily. This design method was applied to a plant, and a reduction in workload was confirmed.

Pitch, Roll, and Yaw Damping of a Flapping Wing

a = lift slope C = damping coefficient Cd = drag coefficient c = chord length of the wing c = mean chord length F = tension of the thread f = flapping frequency g = gravitational acceleration h = distance between the tip path plane and the center of gravity I = moment of inertia of the total system (i.e., the flapping-wing aircraft, suspension bar, and counterbalancing weights) K = a constant in Eq. (2) L = rolling moment Lp = roll damping of the flapping-wing aircraft l = distance between the two threads used to suspend a bar attached to the X-flapping-wing aircraft ls = length of each of two threads used to suspend a bar attached to the flapping-wing aircraft M = pitching moment Mq = pitch damping of the flapping-wing aircraft m = total mass of the flapping-wing aircraft, suspension bar, and counterbalancing weight ma = mass of the flapping-wing aircraft mw = mass of one wing N = yawing moment N r = yaw damping of the flapping-wing aircraft P = cycle period of rotation p = roll rate q = pitch rate R = wing length r = spanwise position r = yaw rate Sw = area of one wing t = time T = thrust vi = induced velocity = flapping angle 0 = mean flapping angle = amplitude of flapping motion = rotational angle 0 = initial rotational angle = amplitude of the rotational motion = feathering angle = amplitude of feathering motion = density of air = angle of the thread measured from the vertical direction of the gravitational force

Lateral-Directional Controller Design Using a Pilot Model and Flight Simulator Experiments

A new controller design method of lateral-directional dynamics is proposed. This method is based on the formulated pilot model, and the controller is designed so that the pilot-airplane system attains the desired requirements. Robust stabilities and handling qualities can be taken into account. The proposed method was applied to B-747 dynamics, and flight simulator experiments were performed. As a result, decreased tracking error and improved handling qualities were confirmed for the designed controller.

Flow Control with Pitching Motion of UAV using MEMS Flow Sensors

This paper presents separation control using a MEMS flow sensor. The MEMS flow sensor measures flow field characteristics from which the separation position is estimated. An airplane’s attitude is then controlled by the elevons so that separation occurs at the desired position, resulting in a maximum lift coefficient. The fluid dynamics around the wing is modeled by dynamic wind tunnel experiments. The dynamic fluid model and direct measurements help to maintain separation control. A controller is designed using the H∞ control method with sufficient robust stability. Numerical simulations and actual flight tests were conducted, and the performance of the flow control was investigated.

Flight controller design taking account of control anticipation parameter

The controller design method taking account of flying quality is proposed. It is assumed that pilot determines flying quality by control anticipation parameter (CAP). CAP condition is formulated in LMI forms and they are introduced into controller design. When the LMIs have solutions, flying quality of closed-loop system is guaranteed. Flying quality condition is combined with robust stability condition and verified by numerical example.

H∞ Control of a Flying Vehicle in Ground Effect

Abstract The purpose of this study is to design a stabilizing controller of the longitudinal motion of a flying vehicle in ground effect and validate its properties by experiments. The aerodynamic coefficients are nonlinear to height, which results in a substantial change of the dynamic characteristics. Nonlinear simulations and a stabilizing experiment in ground effect are carried out. These results demonstrate that H∞ controller based on the normalized coprime factorization is effective to control vehicles in ground effect.