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Heavy-duty commercial vehicles (HDCVs) have specific characteristics different from passenger cars. This paper deals with one of the characteristics of HDCV, propagation delay by air of an air-brake system. It is related to the braking performance of the vehicle. Thus, it is necessary to reflect it appropriately in the control model. In this paper, we propose the model predictive braking controller with the model of HDCV, including the air-brake system. The effectiveness of the proposed method is demonstrated via numerical simulations with the full dynamics vehicle model and the realistic air-brake model.

In this paper, it is demonstrated that the proposed robust path following controller enhances maneuverability and stability of small electric vehicles (EVs) equipped with in-wheel motors through the JSAE-SICE benchmark problem No. 3. In order to achieve high maneuverability, maximum tire force is explicitly considered using octagonal approximation. Verifications are conducted using full vehicle model so that the practical vehicle performance is evaluated. In addition to evaluate the total vehicle performance, we apply the controller to all benchmark scenarios and show its maneuverability and stability of small EV are enhanced.

In this paper, we propose an attitude control law for underactuated two-wheel spacecraft under non-zero total angular momentum. Attitude control with non-zero total angular momentum is complicated in the case that the number of reaction wheels equipped on a spacecraft is two. For a spacecraft in this situation, an attitude control law has been proposed based on a kinematics model by Katsuyama et al. [Y. Katsuyama, SICE Annual Conference, pp. 3421-3426, 2013]. However, a dynamics controller is more desirable for a practical system. Thus, in this paper, we expand the controller to a dynamics model. Nevertheless, in the case of dynamics model, the expansion is not straightforward because of the singularity of input transformation. Therefore, we propose to apply the hierarchical linearization technique which separates a system into several subsystems and linearizes the subsystems step by step. Using this method, the input transformation becomes well-defined, and the system is linearized partially. Additionally, the dimension of linearizable state increases compared with the ordinary input-output linearization. Numerical simulation is conducted to show the validity of the proposed controller.

Leg/Wheel mobile robots can achieve adaptive motions for various environments using legs or wheels. However, computational complexity of optimization like model predictive control (MPC) is increased with the number of degree of freedom on joints and wheels. In this research, we propose to partition a model of a leg/wheel mobile robot to decrease computational complexity. A model of leg/wheel mobile robot is represented by plural symmetric model for a leg composed of joints and a wheel. The robot is controlled by optimizing each leg sequentially using partitioned model at every control cycle. Partitioned models are connected by constraints to maintain body shape. While the configuration of a single leg is optimized, the robot can achieve concerted motion using predicted state of the other legs in the constraint. In this paper, we conduct tracking trajectory control of wheel position using partitioned model. We choose a difficult trajectory for the robot which includes rotational and translational motion to verify the performance of tracking trajectory. We confirm that the tracking performance is not deteriorated by experiment verification using an actual robot, while the concerted motion is achieved even if the behavior of each leg is optimized sequentially. Moreover, we verify that the computational complexity is decreased by the proposed method.

This paper focuses on a real-time obstacle avoidance control method for vehicles using model predictive control (MPC). MPC can optimize the motion of the vehicle over a finite time horizon while satisfying various constraints such as vehicle dynamics, the road width and the steering range. However, the computational cost is too large for conducting real-time control. In this paper, a collision avoidance is realized by MPC with constraints for avoiding prohibited regions represented as circles. We approximate this region into a half plane separated by the tangent of the prohibited region. By handling approximated regions as constraints of the road width of MPC, we can implement the collision avoidance algorithm into the controller without increasing the computational cost. Moreover, in order to reduce the computational effort, we transform the nonlinear vehicle dynamics into reduced order and linearizable subsystems called time-state control form (TSCF). The effectiveness of the proposed method is proved by comparative simulations with conventional method where artificial potential method is applied to MPC. In addition, we conduct two experiments using a 1/10 scale vehicle which is equipped with a laser range finder to execute obstacle detection and localization. We show that real-time control can be realized even if we use an on-board embedded CPU which runs at the frequency of 500MHz.