Virtual Power Plants (VPPs) have been increasingly being used to achieve carbon neutrality in energy systems and to improve resilience. This study handled a control scheme for small-scale community-operated VPPs, which has attracted much attention in recent years. In such community-operated VPPs, operations are not limited to maximizing economic value, but are also focused on community preferences. In this study we proposed Model Predictive Allocation Control (MPAC), which enables VPPs to operate in a way that appropriately reflects community preferences. The MPAC formulates the energy resource allocation control by Model Predictive Control (MPC) and modifies the VPP operation by tuning the weight parameters of the MPC. Furthermore, the weight parameters can be tuned by a preference learning-based optimization algorithm to easily reflect the community’s decisions. We also compensate for the operational stability of the VPPs by using frequency stabilizing control in combination. The effectiveness of the proposed method was verified by experiments using numerical simulations.

This study examines how to properly control each autonomous machine at a construction site where human-driven construction machines, an autonomous excavator, and an autonomous truck are coexisting. We proposed to utilize Model Predictive Control (MPC) to control two different control targets, an excavator and a truck, using the same method in a unified manner. By taking advantage of the MPC's ability to handle constraint conditions explicitly, we proposed a control method that allows each moving object to operate safely without contact. In order to control each autonomous machine with MPC, the future behaviors of machines other than itself are needed. In this study, a simple but effective model was used to make predictions for human-operated construction machinery. On the other hand, for autonomous machines, we proposed a method to achieve efficient cooperative behavior by utilizing the calculation results of the MPC of other machine. The effectiveness of the proposed method was confirmed by numerical simulations.

With the development of the electronic commerce business, the need for automation of warehouse transport is increasing, and there are high expectations for the improvement of warehouse efficiency with automated transport robots. Conventionally, such systems have often used path planning for each robot based on a predefined graph-based map, and guiding the robots to their destinations by preventing collisions through area exclusive control or other means. However, conventional methods require precise map information of the warehouse for path planning with high degree of freedom. This makes it time-consuming to update the map information when the layout of the warehouse is changed. In this study, path planning with a high degree of freedom for multiple robots in a warehouse is achieved through model predictive control using map information which is easy to maintain. The proposed method uses simple global map and obstacle map to achieve efficient path planning that takes collision avoidance into account for the robots. Numerical simulations are conducted to verify the effectiveness of the proposed method for improving travel efficiency.

With the development of the Electronic Commerce (EC) business, the need for automation of warehouse transport is increasing, and transport robots are being introduced in various warehouses. This study addresses the problem of generating a path for multiple robots operating in a warehouse to move from arbitrary initial positions to target positions. To optimize the efficiency of all robots’ movements without collisions in a warehouse, we propose a path generation method using Model Predictive Control (MPC). In actual operation, it is important to generate paths that take into account the uncertainty of each robot’s self-position measurement and consider safety margins so that robots do not collide with each other. In this study, we devise two methods for considering safety margins in MPC: penalty functions and constraints, and evaluate the advantages and disadvantages of these methods.

This study presents a whole-body model predictive control (MPC) of robotic systems with rigid contacts, under a given contact sequence using online switching time optimization (STO). We treat robot dynamics with rigid contacts as a switched system and formulate an optimal control problem of switched systems to implement the MPC. We utilize an efficient solution algorithm for the MPC problem that optimizes the switching times and trajectory simultaneously. The present efficient algorithm, unlike inefficient existing methods, enables online optimization as well as switching times. The proposed MPC with online STO is compared over the conventional MPC with fixed switching times, through numerical simulations of dynamic jumping motions of a quadruped robot. In the simulation comparison, the proposed MPC successfully controls the dynamic jumping motions in twice as many cases as the conventional MPC, which indicates that the proposed method extends the ability of the whole-body MPC. We further conduct hardware experiments on the quadrupedal robot Unitree A1 and prove that the proposed method achieves dynamic motions on the real robot.

We propose a novel and efficient lifting approach for the optimal control of rigid-body systems with contacts to improve the convergence properties of Newton-type methods. To relax the high nonlinearity, we consider the state, acceleration, contact forces, and control input torques, as optimization variables and the inverse dynamics and acceleration constraints on the contact frames as equality constraints. We eliminate the update of the acceleration, contact forces, and their dual variables from the linear equation to be solved in each Newton-type iteration in an efficient manner. As a result, the computational cost per Newton-type iteration is almost identical to that of the conventional non-lifted Newton-type iteration that embeds contact dynamics in the state equation. We conducted numerical experiments on the whole-body optimal control of various quadrupedal gaits subject to the friction cone constraints considered in interior-point methods and demonstrated that the proposed method can significantly increase the convergence speed to more than twice that of the conventional non-lifted approach.

We propose an efficient way of solving optimal control problems for rigid-body systems on the basis of inverse dynamics and the multiple-shooting method. We treat all variables, including the state, acceleration, and control input torques, as optimization variables and treat the inverse dynamics as an equality constraint. We eliminate the update of the control input torques from the linear equation of Newton’s method by applying condensing for inverse dynamics. The size of the resultant linear equation is the same as that of the multiple-shooting method based on forward dynamics except for the variables related to the passive joints and contacts. Compared with the conventional methods based on forward dynamics, the proposed method reduces the computational cost of the dynamics and their sensitivities by utilizing the recursive Newton-Euler algorithm (RNEA) and its partial derivatives. In addition, it increases the sparsity of the Hessian of the Karush–Kuhn–Tucker conditions, which reduces the computational cost, e.g., of Riccati recursion. Numerical experiments show that the proposed method outperforms state-of-the-art implementations of differential dynamic programming based on forward dynamics in terms of computational time and numerical robustness.

This paper presents a simple method to generate a gait trajectory of a compass type biped walking model. The method relies on the symmetric characteristics in the dynamics of the model. The motion generated by this method resembles that of passive dynamic walking phenomenon, as the motion consists of a phase of a ballistic leg swing and a foot collision taking place one after another. The two differs in the point that the method is constructed against a level surface, while passive dynamic walking occurs on a shallow slope. We constructed a compass type biped robot to experimentally confirm the effectiveness of our method. Preliminary results that partially validate our method are shown.