Tadanao Zanma

Dead-time compensation in model predictive instantaneous-current control

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.

Instantaneous-current control of PMSM using MPC: Frequency analysis based on sinusoidal correlation

We have proposed a novel approach for permanent magnet synchronous motors based on model predictive control. This paper describes a frequency analysis based on a single sinusoidal correlation in order to clarify the control performance of the model predictive instantaneous current control (MPIC). For MPIC, the proposed analysis method allows us to analyze the gain and phase characteristics which has not been discussed explicitly so far in other works.

Optimal quantization feedback control with variable discrete quantizer

Networked control systems (NCSs) have been receiving much attention in order to improve control performance in the field of in remote robot operation, surgery and some operations. In the NCSs, it is important to quantize necessary signals for control over a limited network channel for the sake of prevention from transmitting a large amount of data. This paper addresses a quantized feedback control system 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). In our approach, constraints on input/output, the parameter of the quantizer and other physical and/or logical constraints can be explicitly taken into account while guaranteeing optimality. The optimization problem is reduced to a mixed integer quadratic programming. Experimental results are demonstrated to verify the effectiveness of the proposed method.

Optimal Input and Quantization Interval for Quantized Feedback System With Variable Quantizer

Networked control systems (NCSs) have received much attention in the field of robot teleoperation, telesurgery, and other applications. In an NCS, it is important to compress data transmitted over a limited communication network while preserving the data required for control. We address a quantized feedback system in which the output of a plant is quantized with a variable quantization interval. For such a system, we present a novel optimal quantized feedback control. The proposed approach derives not only an optimal input, but also an optimal quantization interval in the quantizer simultaneously using the model predictive control. The approach also satisfies constraints on the state, input, and quantization interval. The effectiveness of the proposed approach is validated through cart-positioning experiments.

State estimation in quantized feedback control system

Recently, by development of communication techniques, control via communication is expected to take important part in networked control systems (NCSs) and so on. The main problem of the NCSs is that data are transmitted via a communication channel which has a constraint of capacity by converting a continuous signal into a discrete signal by using quantizer to compress the amount of information. Therefore, the discrete signal has a margin of error called quantization error. In the quantized feedback control systems, we use an observer to estimate a state if the state is not available, and use the estimated state in feedback control. In such cases, the system may be unstable and degraded the performance since the states estimation is not performed properly due to the quantization error. Hence, an state estimator must be designed with taking into account the effect of quantization error. In this paper, we propose a designing state estimator method in discrete-time systems with the quantized output. The effectiveness of the proposed method is illustrated with experiments.

Compensation of networked control systems with time-delay

In recent years, networked control system (NCS) has been receiving much attention due to shared networkc channels with improved communication device. In NCS, data-dropout and delay are unavoidable. In practical systems, some constraints have to be considered independent of communication. A desired performance in NCS may not be achieved if the closed-loop is not designed without taking consideration of the data-dropout and delay. Therefore, the delay and/or the data-dropout in the network have to be considered in designing NCS. This paper proposes a model predictive control (MPC) based approach for NCS. The delay is described as stochastic models in order to taking them into account for generating input to actuator. The effectiveness of the proposed approach is verified by applying to an inverted pendulum.

Optimal control for networked control systems with stochastic data dropout

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.

Estimation of Network Traffic Status for Networked Control Systems with Data Dropout and Its Control

We address networked control systems (NCSs) with data dropout. The data dropout is assumed to obey a Markovian process model. For the data dropout, a controller synthesis is reviewed. When the network traffic varies, it is necessary to select an adequate controller to guarantee the control performance. Since the controller is designed according to the Markovian process model of the data dropout, it is necessary to obtain the network traffic status for the selection. For the purpose, we propose an estimation of the network traffic status. The network traffic status is modeled as several Markovian process models. It is estimated by comparing the probability matrices of the Markovian process models with the matrix that is obtained the data-dropout history in the network. The effectiveness of the proposed method is verified through simulations and experiments.

Optimal plant data transmission in networked control systems

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.

Optimal dynamic quantizer and input in quantized feedback control 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.