This paper proposes a cruise control for a two-wheeled mobile vehicle on a two-dimensional plane. The cruise control is executed using model predictive control (MPC), such that input and state constraints can be addressed explicitly. For real-time execution of MPC, we model the dynamics of the vehicle as a mixed logical dynamical system rather than an underlying nonlinear model. The cruise control is then reduced to a mixed integer quadratic programming problem, for which efficient solvers using the convexities are available. Simulations and experiments demonstrate the effectiveness of the proposed cruise control through a computation time comparison with the nonlinear MPC method.

This paper proposes a novel adaptive flux observer for the stator resistance identification of speed sensorless controlled induction motors (IM). Generally, to realize the stable parameter identification, the adaptive controller requires strictly positive-realness (SPR) property for the transfer function of the error system of observer. Since this error system does not have the SPR property in the low-speed region when operated in the regenerative mode, this paper proposes a new compensation method to realize the stable stator resistance identification. Moreover, this paper provides a stability analysis for the stator resistance identification system. This paper also demonstrates the feasibility of the proposed method through simulations.

This paper addresses the real-time control of transmission power for small cell base stations (SBSs) exploiting energy- harvesting sources. We employ model predictive control and optimize an objective function that contains the number of users with a given quality of experience and the average state of charge. We first determine the number of active SBSs in the viewpoint of energy efficiency and then approximate the objective function. Finally, we illustrate the proposed method through a numerical example, comparing it with a static method based on statistical information.

This paper addresses an optimal event generator synthesis for networked control systems (NCSs). The event generator is installed in a sensor, and sampled data packets are chosen for transmission through a communication network while desired performance is guaranteed to use network resources efficiently. We propose an optimal event generator synthesis method using a model predictive-based scheme. The effectiveness of the proposed method is illustrated with an example of a rotary inverted pendulum system.

In networked control systems, the data needs to be quantized for transmission over a limited bandwidth communication channel. We consider a dynamic quantizer in a quantized feedback control system. In this system, the quantizer parameters and input are optimized on-line by using the model predictive control to achieve the optimal control performance. In our method, the system constraints explicitly considered. The effectiveness of the proposed method is verified through simulation and experimental results.

Networked control systems (NCSs) have been receiving much attention in the field of remote robot operation, surgery and some operations. In the NCSs, data needs to be quantized since it is transmitted over a limited network channel. In earlier works, we considered an NCS with a variable discrete quantizer. In the system, both input and a parameter of the quantizer are optimized online with the help of model predictive control (MPC) so that the system is stabilized. However, the method is not applied to the tracking control, directly. This paper addresses an extension of the quantized feedback control system in order to consider the tracking control performance as well as the stabilization of the system. In the system, the center of the quantization is considered as a variable to be optimized. The optimization problem is reduced to a mixed integer quadratic programming so that the tracking control can be realized online. Experimental results are demonstrated to verify the effectiveness of the proposed method.

Data dropout in networked control systems (NCSs) is unavoidable. The desired performance may not be achieved if the data dropout is not considered. Therefore, a controller in the NCS needs to be designed which takes the data dropout into account. In this paper, the data dropout is assumed to follow a stochastic process. For the NCS, a model predictive control (MPC) method is proposed for the design of an optimal input sequence. The effectiveness of the proposed method is verified through simulations and experiments.

Model predictive instantaneous-current control (MPIC), which was proposed in our earlier works, enables us to achieve better instantaneous current control using mathematical models of an inverter and permanent magnet synchronous motors (PMSM). However, the dead-time to avoid the short breakdown in the inverter is the main reason in the modeling error. If the modeling error is not ignorable, it is not possible to predict the current evolution using the model. Thus, in this paper, we analyze the influence of the dead-time in MPIC and propose a compensation technique for the dead-time so that the error of the predicted current is reduced. The effectiveness of the proposed method is verified through simulation and experiments.

System identification covers a very wide range of techniques for obtaining a system model from its input-output data. In this paper, we address the identification problem of switched systems, by focusing our attention on hinging hyperplane autoregressive exogenous models (HHARX), which can be identified efficiently via mixed-integer programming. As an example, the problem of extracting operation modes in a driving system of a small toy vehicle and their switch conditions is formulated. The formulation of the identification problem of switched systems with unknown switch points is based on the hybrid dynamical system theory. The effectiveness of HHARX model approach in switched system identification is verified by simulation.

This paper studies the learning control problem of disassembly task sequence. The proposed learning control system has two sub systems. One of them is a supervisory control system which can realize the implicit specifications such as equipment-related or task-independent specifications. The other one is a learning automata system which can realize the explicit specifications such as task-dependent specifications. This approach allows the flexible design to accommodate frequently changing manufacturing requirements. Some simulation results to verify the effectiveness of the proposed system are shown.