Kazuma Sekiguchi

Model predictive trajectory tracking control for hydraulic excavator on digging operation

In order to increase work efficiency, alleviating burden of operators is important. An autonomous hydraulic excavator is expected to improve it. In this paper, an automatic control of a digging operation for the hydraulic excavator is studied. We propose a method for the trajectory tracking control using model predictive control (MPC) which incorporates servo mechanism. MPC can optimize motion and avoids rapid change of velocity using constraints. However, it is difficult to cope with unknown reaction forces caused by contacting with underground objects. Servo mechanism suppresses the disturbance by the integration of the tracking error. However, the error may be accumulated in the integration. Hence, the trajectory tracking may result in rapid response when the objects are removed. By combining MPC and servo mechanism, we can expect that servo mechanism works against the disturbance and the tracking performance is improved. We show effectiveness of the proposed method through simulations under the presence of the disturbance.

Experimental validation of repetitive disturbance estimation and model predictive control for multi UAVs

In this paper, we propose a method to estimate the disturbance information using repetitive technique based on a disturbance map. The disturbance map is shared among unmanned aerial vehicles (UAVs) during platoon flight. Shared map improves the estimated accuracy of disturbance observer via repetitive technique, referred as repetitive estimation. Using the estimated disturbance information, the disturbance map is updated on real-time. The disturbance information can be referred in model predictive control (MPC) as prior information. As the result, the disturbance influence will be suppressed effectively. The validity of the proposed method is verified via experiments using two UAVs.

Robust formation control applying model predictive control to multi agent system by sharing disturbance information with UAVs

A safety technology about unmanned aerial vehicles(UAVs) attracts a lot of attention. This paper presents a robust formation control method for multi agent system (MAS) to suppress the influence of disturbance and maintain the formation robustly by sharing the disturbance information. This paper estimates the disturbance by the disturbance observer. UAVs share these disturbance information using network of MAS. The inputs are calculated by model predictive control(MPC) that predicts the future motion of each UAV considering a shared disturbance information. UAVs share the disturbance information and the future motion of UAVs predicted by MPC using network of MAS. The proposed method achieves the robust formation control to consider these future information in MPC. This paper verifies the validity of proposed method via simulation.

A Hierarchical Model Predictive Tracking Control for Independent Four-Wheel Driving/Steering Vehicles with Coaxial Steering Mechanism

In this study, we apply a hierarchical model predictive control to omni-directional mobile vehicle, and improve the tracking performance. We deal with an independent four-wheel driving/steering vehicle (IFWDS) equipped with four coaxial steering mechanisms (CSM). The coaxial steering mechanism is a special one composed of two steering joints on the same axis. In our previous study with respect to IFWDS with ideal steering, we proposed a model predictive tracking control. However, this method did not consider constraints of the coaxial steering mechanism which causes delay of steering. We also proposed a model predictive steering control considering constraints of this mechanism. In this study, we propose a hierarchical system combining above two control methods for IFWDS. An upper controller, which deals with vehicle kinematics, runs a model predictive tracking control, and a lower controller, which considers constraints of coaxial steering mechanism, runs a model predictive steering control which tracks the predicted steering angle optimized an upper controller. We verify the superiority of this method by comparing this method with the previous method.

Verification of coverage control for multi-copter with local optimal solution avoidance and collision avoidance using random-walk and artificial potential method

This paper combines coverage control, random-walk and artificial potential method to deploy multi-copter type Unmanned Aerial Vehicles (UAVs). With the coverage control, it is able to deploy agents in the coverage area with arbitrary distribution based on the purpose of control. However, when initial position of agents concentrates on one location, it could be fallen into the local optimal solution (local optimal problem). While the local optimal problem, agents could not achieve the distribution based on the purpose of control. Meanwhile, random-walk takes vectorial angle randomly and move constant distance. By using this movement, it is possible to disperse the initial position of UAVs. Therefore, the local optimal problem could be solved by combining random-walk to coverage control. Moreover, using the artificial potential method as a repulsive force to avoid the collision between agents. This paper verifies the validity of proposed method by numerical simulation.

Interference suppression control for interaction of two quad copters by model predictive control using the disturbance map

This study presents the interaction suppression control of multi-copter using MPC that considers the disturbance map. The influence of wind by propellers to another quad copter is estimated beforehand and the disturbance map made from the information. The disturbance map stores the estimated disturbance value corresponding to a relative position. Also, the disturbance map is implemented in the control model for model predictive control. Hence, it is possible that the controller explicitly considers the future expected disturbance via a disturbance map. This paper performs the disturbance suppression control experiments with actual quad copters. and shows the effectiveness of the proposed method.

Model Predictive Control considering Reachable Range of Wheels for Leg / Wheel Mobile Robots

Obstacle avoidance is one of the important tasks for mobile robots. In this paper, we study obstacle avoidance control for mobile robots equipped with four legs comprised of three DoF SCARA leg/wheel mechanism, which enables the robot to change its shape adapting to environments. Our previous method achieves obstacle avoidance by model predictive control (MPC) considering obstacle size and lateral wheel positions. However, this method does not ensure existence of joint angles which achieves reference wheel positions calculated by MPC. In this study, we propose a model predictive control considering reachable mobile ranges of wheels positions by combining multiple linear constraints, where each reachable mobile range is approximated as a convex trapezoid. Thus, we achieve to formulate a MPC as a quadratic problem with linear constraints for nonlinear problem of longitudinal and lateral wheel position control. By optimization of MPC, the reference wheel positions are calculated, while each joint angle is determined by inverse kinematics. Considering reachable mobile ranges explicitly, the optimal joint angles are calculated, which enables wheels to reach the reference wheel positions. We verify its advantages by comparing the proposed method with the previous method through numerical simulations.

Model predictive parking control with on-line path generations and multiple switching motions

In this paper, we propose a parking control method with on-line path generations and multiple switching motions using path-following control for front steering vehicles. In the proposed method, the parking control is accomplished by following the reference path which is generated on-line adapting to the surrounding environment. Furthermore, we also realize the multiple switching motions to achieve the parking control in difficult situation. The performance of the proposed method is verified through experiments using a 1/10 scale vehicle. In addition, we show that the feasible area of the parking control is extended comparing with the conventional one.