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In this paper, a formation control for quadcopters connected by cables is considered. The quadcopters need to flight by avoiding the crush among each other and by keeping the distance constraint due to the cable. Moreover, the avoidance of contact against walls should be taken into account since the system is assumed to flight an indoor environment. To hold these several constraints, the model predictive based controller is proposed, and the effectiveness of the controller is verified via numerical simulation.

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 this paper, we propose an obstacle avoidance method considering both the shape and dynamics of robot and the motion of obstacles, using model predictive control (MPC) and fuzzy potential method (FPM). FPM can deal with the shape of robot, but it cannot treat the dynamics of robot. On the other hand, MPC can deal with constraints including the dynamics of both robot and obstacles. The predicted movable range of obstacles is used as a prohibited region for MPC. Thus, combining FPM and MPC, the proposed method achieves moving obstacle avoidance. The effectiveness of the proposed method is verified through numerical simulations.

This paper presents energy conserving vehicle motion control for electric vehicles equipped with different types of drive motors. The proposed controller, which minimizes total energy consumption in a vehicle, consists of guidance control and distribution control. In the guidance controller, optimized vehicle motion conserving energy consumption is generated using model predictive control. In the distribution controller, dissipation energy is minimized utilizing a redundant set of tire forces. By separating guidance and distribution control, the complexity of vehicle dynamics which is considered in nonlinear MPC is reduced, and computational load is also reduced. Effectiveness of the proposed energy conserving control is shown through the numerical simulation using a test course modeled an interchange; the energy conserving vehicle behavior is shown.

Localization is important to achieve safety motion of autonomous mobile robots. In this paper, we propose a model predictive control which prevents vehicles from falling into a singular localization environment with less features. We assume to use a laser range finder (LRF) as a sensor to obtain two dimensional point-cloud data of the surrounding environment and apply map-matching method for localization. We can calculate a covariance matrix of localization error using Fisher information matrix. The maximum eigenvalue of this covariance matrix is used as an index of uncertainty of localization. Then, we extract regions having large uncertainty for localization to construct prohibited regions. The robot avoids singular environments and regions with significantly low estimation accuracy by considering the prohibited regions represented as inequality constraints for model predictive control (MPC). We show the effectiveness of the proposed method through numerical simulations which simulate singular environments indicating a corridor or a large floor where only degenerated information is available.

This paper proposes a formation control method for multi quadcopters considering collision avoidance using model predictive control (MPC) in three dimensional space. To achieve the formation control, the term of consensus is included in MPC. Collision avoidance among control objects is considered by adding the term of repulsive force to index function of MPC. Thus this paper achieves formation control by combining consensus based control with collision avoidance control in MPC. In general, the repulsive force is described as a nonlinear function. It is difficult to add the nonlinear function to the term of index function because the computation time can increase. Hence it's not appropriate to implement to practical system. Thus the term of repulsive force is approximated as a linear function to achieve real-time control. In addition, the model of control object is assumed as linear system. The validity of proposed method is verified via experiment with three quadcopters in three dimensional space.

Leg/wheel robots has a hybrid movement mechanism which combines leg and wheel mechanism. In this paper, we propose a posture control for three-dimensional leg/wheel robots in which the distance between zero moment point and center of gravity are minimized using model predictive control. It is expected to prevent overturning of the robots based on prediction and considering zero moment point. The proposed method can be applied to the robots with redundant degrees of freedom. We show the superiority of the proposed method through simulations of multiple body dynamics.

When leg/wheel mobile robots are controlled by model predictive control (MPC), the optimization is too complex for an embedded CPU. Then, we proposed a method which reduces computational complexity by sequential optimization for the models partitioned into each leg. However, robustness of the proposed method was not evaluated enough in the disturbing environment. In this paper, we conduct simulations on the inclined plane to evaluate the robustness. Through the simulations, the robustness of the proposed method is improved by changing the control cycle and the predictive length.

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.

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.

There is a demand for autonomous driving control in front-wheel steering vehicles because it is expected to make driving safer and easier and also to reduce the driving workload. In order to perform safe driving with autonomous driving control, it is necessary to consider unexpected disturbances when the vehicle is moving and that tire forces have limitations. We propose autonomous driving control combining Model Predictive Control (MPC) and Sliding Mode Control (SMC). In this paper, we employ MPC in order to consider the maximum tire forces. SMC is employed to deal with unexpected disturbances that the model has not anticipated. Furthermore, we confirmed that path following control is possible by practical inspection using a small front-wheel steering vehicle that is susceptible to unexpected disturbances.

In this paper, we propose a model predictive obstacle avoidance control method for leg/wheel robots with 3 DOF SCARA legs. This robot is able to pass through a narrow space because it can extend and shrink the size between the wheels by changing the configuration of the legs. In this study, obstacle avoidance is achieved by two passage width constraints. Obstacle size and wheel positions are considered in those constraints. A distance between the robot and an obstacle is kept larger than a value specified by those constraints. Comparing with conventional artificial potential methods, the proposed method does not require tuning of obstacle parameters. We utilize two kinds of linear constraints. In the first constraint, we use the internal common tangent between the robot and the obstacle nearest to the robot. In the other constraint, we use the external common tangent to reduce getting stuck into the narrow space between obstacles. The optimal tracking control is realized using those constraints for the model predictive control. The efficacy of this method is verified via the numerical simulations.

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.

In this paper, we describe a model predictive control (MPC) for small unmanned aerial vehicles (UAVs) like the AR. Drone. By storing the disturbance information, we share it to other UAVs on the same path so that the other UAVs can know the disturbance in advance. Moreover, other UAVs are possible to use the disturbance information in the prediction steps. In addition, the prior disturbance information can be included in the model, and we use the model predictive control to perform the optimization calculation. As the result, the UAVs can prepare to the disturbance before it affects the UAVs, which make them possible to improve the stability of the flight. We verify this approach through the simulation and experiment.

In this paper, we cope with the JSAE-SICE benchmark study which demands recovering disadvantage of micro electric vehicles. To enhance the stability of micro cars, we apply the robust path tracking control combining Model Predictive Control and Sliding Mode Control. In this controller, the friction circles are considered using the approximated tire force usage rate to balance the path following performance and the robustness. The enhancement of the vehicle stability using the robust path tracking controller through the numerical simulations is shown. The controller are applied to the all scenarios of the JSAE-SICE benchmark study.

In this paper, we propose an optimal obstacle avoidance control method with translational and rotational motion for omni-directional mobile robots based on model predictive control (MPC) modifying fuzzy potential method (FPM). The reference path is generated by FPM considering the shape and the size of the robot, but it might vary steeply because the vehicle dynamics is not considered explicitly. To resolve this issue, the robot tracks the path while modifying it by optimization using MPC which computes an optimal input under prediction based on the dynamics until finite-time future in order to achieve smooth and efficient obstacle avoidance. The performance of the method is verified through comparative simulations.

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.

Recently, the increase of burden to operators and lack of skilled operators are the issue in the work of the hydraulic excavator. These problems are expected to be improved by autonomous control. In this paper, we present experimental results of hydraulic excavators using model predictive control (MPC) which incorporates servo mechanism. MPC optimizes digging operations by the optimal control input which is calculated by predicting the future states and satisfying the constraints. However, it is difficult for MPC to cope with the reaction force from soil when a hydraulic excavator performs excavation. Servo mechanism suppresses the influence of the constant disturbance using the error integration. However, the bucket tip deviates from a specified shape by the sudden change of the disturbance. We can expect that the tracking performance is improved by combining MPC and servo mechanism. Path-tracking controls of the bucket tip are performed using the optimal control input. We apply the proposed method to the Komatsu- made micro hydraulic excavator PC01 by experiments. We show the effectiveness of the proposed method through the experiment of digging soil by comparing servo mechanism and pure MPC with the proposed method.

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.