In this paper, we consider fuel economy optimization problem for mild HEV (Hybrid Electric Vehicle) via hierarchical model predictive control. In our algorithm, we deal with two problems; one is eco-driving, and the other is torque distribution. In the eco-driving problem, we control vehicle speed. Considering the reduction of fuel consumption and NOx emission, we calculate the required torque to follow target speed. Next, in the torque distribution problem, we calculate the distribution between the engine and motor torque. In that phase, engine characteristics are considered. These problems differ in terms of time scales, hence we propose hierarchical model predictive control. Finally, numerical simulation results show the effectiveness of this research.

Recently, the use of small mobile robots in smart agriculture has been explored. Small robots have the advantage of being less invasive to the environment and crops, but also have the problem of being prone to stranding due to collisions with the geological features. Many studies have been conducted on the collision between robots and obstacles. However, most of them focus on avoiding collision or stopping the robot quickly after collision in order to protect robots and workpieces. On the other hand, since obstacles to field robots are assumed to be soft and deformable obstacles such as soil and grass, there are other options for dealing with obstacles besides the above: push off. In order to take such an action, one has to determine the scale of the obstacle and decide whether to push or pull. However, there are not many studies that use the force received from a deforming obstacle as an indicator of behavioral decisions. In response to the above problem, this study proposes a method for estimating the time and magnitude of a robot's collision with an obstacle and determining the possibility of stranding due to continued action. The target robot is a small weeding robot for paddy fields that has balance floats in front and behind its body and runs on two wheels. The proposed method models the acceleration by a differential equation with an unknown collision, and the collision term is estimated by an extended Kalman filter. The effectiveness of the proposed method is confirmed by numerical examples and experiments.

Ensuring safety for the wearer is of utmost importance for wearable robots. For safe assistance of complex body parts such as the torso, it is important to measure and control the contact force: the force applied to the human body from the robot. We have been studying the robot enabling to measure contact force distribution from tactile sensors on the robot surface as pressure distribution. In order to take safe assistance, the robot is controlled to reduce superfluous force by direct feedback control of the contact force in wearable robots. In this paper, a new control method is proposed, in which predictive control of contact force distribution is applied. The method predicts the contact force distribution of the next step from the current link positions and measured contact force distribution. The robot controls his actuators to minimize the predictive errors with the optimum contact force distribution. The proposed method is compared with the conventional control based on inverse kinematics, being verified the effectiveness.

In this paper, we proposed to design a null-space compensation control system for linear time invariant systems with multi-input redundant channels. In the input redundant systems, control input vector may have null-space component of the plant parameter vector. The null-space component does not contribute to the generation of the control force which drives the plant. If a control input that does not include the null-space component can be generated, efficient control is achieved in the sense of minimizing the norm of the control input. In the proposed method, a model reference adaptive control (MRAC) method was used to design the control system that compensates for the null-space component even if the plant parameters are unknown. The effectiveness of the proposed null-space compensation control system was shown by numerical examples.

This paper proposes an optimal trading algorithm for a decentralized energy trading system among multiple building energy management systems (BEMS). Each building generates solar power and has a battery. As a decentralized energy trading algorithm, we first solve the proposed optimization problem to determine the optimal trading volume that minimizes the total purchase price of electricity while satisfying the battery capacity constraint under the consideration of the solar power generation error and the time-varying electricity price. To take into account the foregoing, model predictive control and a scenario-based robust optimization are applied. After the information on the desired amount of electricity transactions is communicated between buildings, the electricity sales price is updated. These steps are iterated until the termination condition is satisfied, and the final trading volume is determined. The convergence of the proposed algorithm is analyzed and the sufficient conditions for the convergence of the price update parameters are shown. Finally, the total price cost and computational cost are compared with the centralized one, and the effectiveness and the superiority of the decentralized control system are shown by several numerical simulations.

Measurements using piezoelectric sensors are suitable for the long-term monitoring of structures. This paper describes the results of evaluating the characteristics of the voltage output and displacement measurement of piezoelectric joint sensors using the sensor measurement robot SALLY.