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In recent years, to solve the problem of shortage of delivery staff caused by high demand in the delivery industry, it is expected to carry packages while a UAV tows them. However, when towing, the control performance of the aircraft deteriorates due to the influence of the weight and shaking of the towed object, so it is necessary to control the aircraft in consideration of the behavior of the towed object. To address this issue, an input design with hierarchical linearization has been proposed for the UAV that tows an object, but it has not been experimentally verified. In this paper, we conducted experiments and confirmed the existence of the modeling error. Thus, we built a model reflecting the actual structure of towing of UAV. We applied the redesigned controller to the simulation model and verified its performance.

Leg/wheel mobile robots have a hybrid mechanism combining legs and wheels. Those robots are expected as a transporter in an extraterrestrial environment because leg/wheel mechanisms have the potential to move on rough terrain efficiently. A controller is required adapting to the complex geometry of the ground. This paper presents a leg configuration control based on model predictive control considering load distribution using the state equation which includes steering. Numerical simulations are conducted to verify the advantage of the proposed method. The robot successfully moves on the slope with a bank angle while improving the load distribution.

In this paper, moving obstacle avoidance in narrow environment is studied. Recently, electric wheelchairs are widely used, which has independent driving wheels modeled as a two-wheeled nonholonomic vehicle model. One of the challenges on obstacle avoidance in real environment is that the obstacle position observed by sensors is usually fluctuated due to noise; it is difficult to pass through the obstacles safely. To overcome this difficulty, we apply time-state control form to divide the system into two subsystems: path dynamics and velocity dynamics so that we can separate the path planning from velocity control, which enables us to transform each subsystem into linear ones. The path tracking is achieved by linear control synthesis design, while running velocity can be tuned depending on the distance to the obstacles. Then we apply model predictive control (MPC) for each subsystem in order to realize obstacle avoidance and optimal velocity control. We impose constraints on MPC representing prohibited area for obstacle avoidance on path following control. In this paper, we show effectiveness of the proposed method by numerical simulations assuming a real electric wheelchair. In the neighborhood of obstacles, the vehicle decelerated in accordance with the distance to the obstacle. It is shown that the effectiveness of the proposed method is supported by numerical simulations.

In general, controller is designed with respect to one time scale. Time-state control form divides a system into virtual time control part and state control part, and the system is linearized based on two time scales; real and virtual time scales. This paper claims that handling of time scales for a control system should be more flexible, and introduces a multi time-scale transformation as a generalization of time scale transformation. As an example, a mechanical system is linearized under two virtual time scales.

In this paper, the change of the relative degree structure at the controllable equilibrium is discussed. In the neighborhood of the controllable equilibrium, all nonlinear systems are approximated to linear controllable system. The gap between the original nonlinear system and approximate linear system appears as a change of the relative degree structure. By comparison of these two structures, the necessary and sufficient condition is derived in order for the equilibrium not to be singular point via the input-output linearization. Based on this result, the selection of the output function is proposed. Moreover the output design method for linear stable zero dynamics is proposed.