Hiroaki Fukushima

Brief paper: Adaptive model predictive control for a class of constrained linear systems based on the comparison model

This paper proposes an adaptive model predictive control (MPC) algorithm for a class of constrained linear systems, which estimates system parameters on-line and produces the control input satisfying input/state constraints for possible parameter estimation errors. The key idea is to combine the robust MPC method based on the comparison model with an adaptive parameter estimation method suitable for MPC. To this end, first, a new parameter update method based on the moving horizon estimation is proposed, which allows to predict an estimation error bound over the prediction horizon. Second, an adaptive MPC algorithm is developed by combining the on-line parameter estimation with an MPC method based on the comparison model, suitably modified to cope with the time-varying case. This method guarantees feasibility and stability of the closed-loop system in the presence of state/input constraints. A numerical example is given to demonstrate its effectiveness.

Robust constrained predictive control using comparison model

This paper proposes a quadratic programming (QP) approach to robust model predictive control (MPC) for constrained linear systems having both model uncertainties and bounded disturbances. To this end, we construct an additional comparison model for worst-case analysis based on a robust control Lyapunov function (RCLF) for the unconstrained system (not necessarily an RCLF in the presence of constraints). This comparison model enables us to transform the given robust MPC problem into a nominal one without uncertain terms. Based on a terminal constraint obtained from the comparison model, we derive a condition for initial states under which the ultimate boundedness of the closed loop is guaranteed without violating state and control constraints. Since this terminal condition is described by linear constraints, the control optimization can be reduced to a QP problem.

Model Predictive Formation Control Using Branch-and-Bound Compatible With Collision Avoidance Problems

This paper presents a model predictive control (MPC) approach for multivehicle formation taking into account collision avoidance and velocity limitation with reduced computational burden. The first part of the paper constructs a formation control law using feedback linearization with MPC in order to reduce the optimal control problem to a mixed-integer quadratic programming problem for a group of unicycles. The second part constructs a new branch-and-bound (B& B) -based algorithm for collision-avoidance problems. Numerical examples and experiments show that the proposed method significantly reduces computation time.

Distributed Model Predictive Control for Multi-Vehicle Formation with Collision Avoidance Constraints

This paper presents a distributed model predictive control (MPC) method for unicycles information with collision avoidance constraints. The proposed method first stabilizes the system by using a feedback linearization, and then a collision avoidance method based on MPC is applied to the linearized system. One of the features of the proposed method is that each vehicle sequentially solves its optimal control problem at different time step. Unlike other MPC collision avoidance methods in which all vehicles solve optimal control problems at every time step, only one vehicle can solve its optimization problem at one time step. We derive a condition for the proposed method to ensure the feasibility of the optimization method and stability of the closed-loop system. The effectiveness of the method is also investigated by experiments.

Modeling and Control of a Snake-Like Robot Using the Screw-Drive Mechanism

In this paper, we develop a new type of snake-like robot using screw-drive units that are connected by active joints. The screw-drive units enable the robot to generate propulsion on any side of the body in contact with environments. Another feature of this robot is the omnidirectional mobility by combinations of screws angular velocities. We also derive a kinematic model and apply it to trajectory tracking control. Furthermore, we design a front-unit-following controller, which is suitable for manual operations. In this control system, operators are required to command only one unit in the front; then, commands for the rest of the units are automatically calculated to track the path of the preceding units. Asymptotic convergence of the tracking error of the front-unit-following controller is analyzed based on a Lyapunov approach for the case of constant curvature. The effectiveness of the control method is demonstrated by numerical examples and experiments.

Sliding-Mode Control for Transformation to an Inverted Pendulum Mode of a Mobile Robot With Wheel-Arms

This paper proposes a control method for locomotion mode transformation of a mobile robot with wheel-arms. The proposed method aims at transformation from a four-wheeled mode for high-speed mobility to an inverted pendulum mode, which has advantages of high viewing position and small turning radius. Since the initial state of the system is far away from the target equilibrium point of the wheeled inverted pendulum system, we use a nonlinear controller based on sliding-mode control. While the previous transformation methods cannot control the robot velocity until the robot body is lifted up, the proposed method can take into account the robot velocity from the beginning of the transformation, which enables us to complete the transformation in a smaller space. To analyze the asymptotic stability of the control system on the sliding surface, we derive an invariant set in which the system state converges to the origin without going out. Furthermore, the effectiveness of the proposed method is demonstrated in both simulations and real robot experiments.

Control of a Group of Mobile Robots Based on Formation Abstraction and Decentralized Locational Optimization

In this paper, we propose a new method of controlling a group of mobile robots based on formation abstraction. The shape of a formation is represented by a deformable polygon, which is constructed by bending a rectangle, to go through narrow spaces without colliding with obstacles. If the trajectory of the front end point, as well as the width and the length of the formation, are given, the formation automatically reshapes itself to fit the area through which the front part of the group has already safely passed. Furthermore, the robots continuously try to optimize their positions to decrease the risk of collisions by integrating a decentralized locational optimization algorithm into the formation control. We show that the objective function, taking into account the distance between robots, does not decrease for fixed and nonconvex polygonal formation shapes if the zero-order hold control is applied for a sufficiently short sampling period. We also analyze the influence of the decentralized locational optimization algorithm on the objective function in the case of variable formations. The effectiveness of the proposed method is demonstrated in both simulations and real robot experiments.

Model predictive control of an autonomous blimp with input and output constraints

This paper focuses on how to design autonomous flight control systems taking into account input constraints due to actuator saturations and output limitations from the viewpoint of security. Model predictive control (MPC) is one of the most systematic ways to handle such constraints. To implement MPC, we first construct a simple linear model connected to a deadzone nonlinearity based on experimental data. Then, MPC controllers are derived offline as piecewise affine state feedback laws based on a robust MPC approach to take into account additive uncertainties. Indoor experiments are performed to investigate the effectiveness of the MPC controllers obtained based on the simple model.

Transformation Control to an Inverted Pendulum for a Mobile Robot With Wheel-Arms Using Partial Linearization and Polytopic Model Set

This paper presents a shape transformation control method of a mobile robot with wheel-arms. The proposed method aims at transformation from a four-wheeled mode for high-speed mobility to an inverted pendulum mode, which has advantages of high viewing position and small turning radius. The transformation starts with lifting up the wheel-arms to raise the center of gravity of the whole robot including the main body and arms. From such initial states, the body is lifted up and controlled to the target angle by partial linearization, while returning the arms to the initial angle. Then, the robot position is controlled by manipulating the target body angle. Unlike existing methods, we take into account the effects of the body angular velocity and the tracking error of the body angle by constructing a model set, which is composed of a single nominal model and its polytopic uncertainty for the system matrices. In order to derive the model set, we assume that the target body angle is constrained to a prescribed range. Therefore, the target body angle is manipulated using a model predictive control method, such that the closed-loop system is asymptotically stabilized, while the given constraint is satisfied, for all systems in the model set. The effectiveness of the proposed method is demonstrated in both simulations and real robot experiments.

Development of Robot Teleoperation System in Bad Viewing Condition

In robot teleoperation there is a mounted camera on a robot and the operation is usually performed from a remote site using captured images by the mounted camera. Even though color cameras provide many useful information of a remote site, robot teleoperation using color cameras are highly effected by environmental conditions such as lighting, colors, smoke, etc. and there might be a case that cameras might become useless during an operation such as in dark places. In this paper we have developed a robot teleoperation system which does not fully rely on color camera images and works well in bad viewing condition for operating a robot and searching for a victim. A laser rangefinder is used for sensing surroundings of a robot and a thermal camera is used for victim detection. An operator can control a robot in a remote site only using environmental contour figure information around a robot without color camera images. The augmented image interface consists of color and thermal camera images are used for victim detection. We have executed a victim searching task in different lighting conditions with different sensor configurations to show the effectiveness of developed system.