Takami Matsuo

Nominal performance recovery by PID+Q controller and its application to antisway control of crane lifter with visual feedback

We have recently suggested the PID+Q controller that consists of a PID controller, a stable free-parameter, and an internal model of plants. In this brief paper, we point out that the free parameter can be designed to improve the robust stability and the robust performance of the control system, and present a nominal-performance recovery problem to design the free parameter with better robust performance. Moreover, we apply the PID+Q controller to the robust antisway control of crane lifter in order to compensate the time delay caused by the vision-based measurement. After modeling the lifter system with the visual sensor as a pendulum control system with a time delay, we design a robust PD+Q controller and a PID+Q controller using the MATLAB LMI Toolbox. Finally, we implement the designed controller on a table-size lifter kit with the visual sensor to demonstrate the proposed PD PD+Q controller.

A design of robust adaptive control system with a relaxed strictly-positive-realness condition

In this paper, we extend the exact model matching by observer-controllers to that by all two-parameter compensators and give an adaptive control scheme with free parameter Q using coprime factorization. The parameter adjustment law using a Riccati equation which guarantees the boundedness of the dynamic error system is proposed. Moreover, we construct the adaptive control system for a plant with output disturbances or unmodelled dynamics by designing the transfer function of error system and Q parameter to meet some desired robust performance properties.

Frequency estimation with an LMI-based adaptive update law

In this paper, we propose a frequency estimator using an adaptive observer. An LMI-based update law is applied to the adaptive observer and guarantees the stability of the error system with bounded disturbances. Moreover, we introduce the averaging factors to the frequency estimator in order to attenuate an error fluctuation arising due to noises in measurements. Finally, the performance of the proposed estimator is investigated by MATLAB simulations.

Non-model-based velocity and acceleration estimators for a suspension system with parallel connection of a hydraulic actuator

The design of an ideal differentiator is a difficult and a challenging task. In this paper, we propose two types of differentiators to estimate the time-derivative of an output signal. To begin with, a velocity estimator is proposed based on the adaptive identification method and compared with the exact differentiator proposed by Levant. Then, an acceleration estimator is proposed with the non-passifiable adaptive schemes. Finally, the proposed differentiators are applied to the estimation of an active suspension system. The simulation results show that the proposed differentiators are robust to additive noises.

ZEROS AND RELATIVE DEGREE ASSIGNMENTS OF ADAPTIVE CHAOTIC COMMUNICATION SYSTEMS

A general methodology for designing chaotic and hyperchaotic cryptosystems has been developed using the control systems theory. Grassi et al. proposed a nonlinear-observer-based decrypter for the state of an encrypter. If we can design the decrypter without the knowledge of the parameters of the encrypter, the chaos-based secure communication systems are not secure. In this paper, we have designed an observer-based chaotic communication system, which allows us to assign the relative degree and the zeros of its encrypter system. Moreover, under some conditions, we have designed an adaptive decrypter using the adaptive parameter adjustment law based on a Riccati equation when the transfer function of the encrypter is of minimal-phase type. The simulations via MATLAB/Simulink suggest that the encrypter dynamics should be designed such that its relative degree is more than 2 and its zeros are unstable so as to fail to synchronize the cryptosystem for the intruders.

Adaptive Estimation of Friction Forces with Fuzzy Basis Function Expansion

In this note, we design adaptive observers to estimate the nonlinear friction model when the output is the velocity and the position, respectively. First, two adaptive observer are designed to estimate the viscous friction force in the velocity measurement and in the position measurement, respectively. Secondly, the kinetic friction force and the dynamic friction force are estimated by the fuzzy adaptive observers in the position measurement. Finally, simulation results for the proposed observers are presented

Adaptive observer-based P-controller for biological clock of crassulacean acid metabolism

The nonlinear dynamical behavior of the crassu-lacean acid metabolism is discussed from the control theoretical viewpoint. The state-variables of the nonlinear dynamic equations are an internal CO2 concentration, a malate concentration in the cytoplasm, a malate concentration in the vacuole, and an order of the tonoplast membrane. The input variables are an external CO2 concentration, a light intensity and a temperature. The order of the tonoplast membrane cannot be measured. We have recently presented a dynamic estimator of the tonoplast order and a fuzzy identifier of the nonlinear function in the dynamics of the tonoplast order. In this paper, we propose two types of the feedback controllers of the CAM model using the estimate of the adaptive controller.

Firing Pattern Estimation of Synaptically Coupled Hindmarsh-Rose Neurons by Adaptive Observer

In this paper, we present adaptive observers for synaptically coupled Hindmarsh-Rose(HR) neurons with the membrane potential measurement under the assumption that some of parameters in an individual HR neuron are known. Using the adaptive observers for a single HR neuron, we propose a two-stage merging procedure to identify the firing pattern of a model of synaptically coupled HR neurons. The procedure allows us to recover the internal states and to distinguish the firing patterns of the synaptically coupled HR neurons, with early-time dynamic behaviors.

Firing Pattern Estimation of Biological Neuron Models by Adaptive Observer

In this paper, we present three adaptive observers with the membrane potential measurement under the assumption that some of parameters in HR neuron are known. Using the Strictly Positive Realness and Yus stability criterion, we can show the asymptotic stability of the error systems. The estimators allow us to recover the internal states and to distinguish the firing patterns with early-time dynamic behaviors.

Adaptive Observer for Biological Clock of Crassulacean Acid Metabolism with Partial States

The mechanism of endogenous circadian photosynthesis oscillations of plants performing crassulacean acid metabolism (CAM) is investigated in terms of a nonlinear theoretical model. Blasius et al. used throughout continuous time differential equations which mode adequately reflect the CAM dynamics. They showed that the membrane effectively acts as a hysteresis switch regulating the oscillations. In this paper, we discuss the nonlinear dynamical model of CAM from the control theoretical viewpoint. In particular, we present an adaptive observer to estimate the states and the nonlinear function in the dynamics of the tonoplast order assuming that the available signal is only the internal CO2 concentration. We adopt a nonlinear error function in the error feedback term.