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In this paper, we study a consensus problem of continuous time multi-agent system. Multi-agent system has communication network. Each agent gets information of neighbors to achieve consensus. It is assumed that there are delays in communication network. The communication delays are different every communication channels and time-dependent. Furthermore, it is assumed that there are switching topologies and feed-back gains are time variable. Under these assumptions, we show that the entire system is stable. The stability is shown by Lyapunov theorem.

In recent years, integrated vehicle control system has been designed based on the information of vehicle torque. Therefore, engine is treated as one of actuators generating torque and a necessity of realizing engine torque demand from the system is increasing. This paper propose an adaptive torque demand control of SI (Spark Ignition) engine with continuous variable valve lift. The adaptation mechanism is based on STC(Self Tuning Control) algorithms.

In this paper, we propose robust design of Immersion and Invariance (I&I) - model reference adaptive control system (MRACS) for an SISO linear time-invariant system with arbitrary relative degree in the presence of deterministic disturbance. In this method, we introduce fixed compensator and prove uniformly asymptotic stability of the tracking error and the boundedness of all signals in the closed system.

In this paper we provide a novel model-based control system for nonlinear process for the purpose of minimizing an objective function, followed by similar research areas such as dynamic optimization and model predictive control. Their target processes have large nonlinearity, multi manipulated variables and multi control variables. We then provide our control system scheme and an optimization algorithm. We also illustrate the results obtained from application of a depropanizer distillation column using a rigorous plant-wide dynamic simulator.

In this paper, a new method is proposed to achieve both clean effluent quality and low operational cost simultaneously. This is accomplished by minimizing the certified cost function directly through an extremum seeking approach. The effectiveness of the proposed method is tested on biological wastewater treatment simulator SIMBA.

In this paper, the application of sensitivity analysis in order to determine the key parameters of wastewater treatment systems, along with identification method based on this result has been proposed. The effectiveness of the proposed method has been verified by using real influent data provided by a pilot plant.

Fully adaptive feedforward control algorithm is proposed for general multichannel active noise control (ANC) when all the noise transmission channels are uncertain. To reduce the actual canceling error, two kinds of virtual errors are introduced and are forced into zero by adjusting three adaptive FIR filter matrices in an on-line manner, which can result in the canceling at the objective points. Unlike other conventional approaches, the proposed algorithm does not need exact identification of the secondary paths, and so requires neither any dither signals nor the PE property of the source noises, which is a great advantage of the proposed adaptive approach.

We suggest a method to design supervisor which guarantees robust stability for linear time-invariant systems in the presence of the uncertainty via output feedback. The system is a composite of continuous-time uncertain plant and switched controller. The controller is defined by a collection of given controllers whose state is shared. Furthermore the proposed supervisor controller determines the mode of the controller to guarantee robust stability of the total closed-loop system. A sufficient condition of robust stability is given in terms of an algebraic Riccati equation and bilinear matrix inequalities(BMI).

A new auto-tuning algorithm for motor velocity control is presented to adjust the parameters in the feedforward and feedback PI controllers, according to identified physical parameters such as a motor time constant, viscous friction terms proportional to motor velocity and squared velocity, and Coulomb friction. The physical parameters are identified by the proposed adaptive algorithm which appropriately set up a reference model and use its state variables to adjust the feedforward controller parameters. The proposed scheme is robust to disturbance torque, compared to ordinary closed-loop identification using the motor output directly. Its effectiveness is investigated in velocity control experiment using an AC synchronous servo motor.

New adaptive identification algorithms are proposed for a time-varying linear system in which the system parameters change as piecewise polynomial function of time. By appropriately incorporating the internal model corresponding to the polynomial degree into the parameter adjustment dynamics of the new algorithm, its BIBO stability can be assured by the aid of the small gain theorem and modified passivity theorem. Its effectiveness of the proposed LMS and RLS types of adaptive algorithms is validated by comparing to ordinary approaches in adaptive direction-of-arrival (DOA) estimation for fast moving targets.

This paper is concerned with a new adaptive predistortion method for compensation of linear and nonlinear distortions of a high power amplifier (HPA), which is frequently used in the orthogonal frequency division multiplexing (OFDM) communication scheme and mobile communication systems. The proposed nonlinear method is based on the support vector machine (SVM), which can construct an inverse system of the nonlinear amplifier through learning using a given number of training samples. An on-line adaptive algorithm for updating the parameters of SVM is also investigated to reduce its computational burden of the batch-processed optimization. By comparing with a case without predistortion and ordinary table-lookup scheme, it is shown that the proposed data-based approach can improve the suppression of the power spectrum of the amplifier output outside the input frequency band caused by the nonlinear distortions.

A class of identification algorithms for the closed-loop identification problem is discussed based on the output inter-sampling scheme. It is illustrated that the inter-sampled linear plant has a special single-input multiple-output model structure with common control input, and the inter-sampled output has cyclostationary property. Thus it can be clarified that the identifiability of the algorithm can be guaranteed by the multiple model structure in the time domain, and the cyclostationary property can be used to eliminate the correlation between the plant input and output noise in the frequency domain even though no persistently exciting reference signal is available. Some simulation and experiment results demonstrate the effectiveness of the proposed algorithms.

A new deterrninistic adaptive identification and equalization approach is investigated based on an internal model principle to take account of time-varying dynamics of the channel parameters. The proposed adaptive algorithm incorporates an internal model for compensating the dynamics of the time-varying parameters of the FIR channel model. The stability of the adaptation can be assured by designing the parameter adjusting dynamics in the actaptive algorithm by the help of the small gain theorem and passivity theorem. Its effectiveness of the proposed algorithms is examined and compared to ordinary LMS approaches in numerical simulation of adaptive identification and equalization of a fading channel with parameters changing with a first-order function of time.

A new discrete-time closed-loop identification method is investigated based on an output inter-sampling scheme to construct a plant model when the plant is directed by a generalized predictive controller (GPC). The ordinary identifiability condition requires that the order of the controller is larger than that of the plant, however, this is hardly satisfied in practical cases. This paper proposes a new direct method using data set of the input and inter-sampled output of the plant and clarifies that the ordinary restrictive identifiability condition can be removed by using the single input multiple output model description, and then the consistency of the parameter estimate is assured even without using reference or external test signals holding the PE condition.

A new adaptive controller for rejecting periodic disturbances is proposed. The controller is constructed in a plug-in manner, i.e., it can be added to an existed feedback control system without altering the original closed-loop configuration. It is shown that the controller can reject disturbances at selected frequencies independently. Furthermore the proposed design method of the plug-in adaptive controller is based on linear control theory although the closed loop system includes the adaptive algorithm as nonlinear operator.

Multirate control has potential advantages over single-rate control if sampling periods are chosen adequately. This paper proposes a multiobjective approach to multirate control design taking account of various kinds of composing systems and disturbances with different characteristic frequencies. For that kind of multirate systems, performance evaluation in discrete time does not make sense and only continuous-time evaluation of performances gives a fair judgment of signals with different frequencies. The paper shows how to deal with multiple continuous-time performance objectives of multirate sampled-data systems. An algorithm to design multiobjective controllers is developed.

A multi-channel adaptive active noise control system is studied in this paper, which involves a multiple number of primary noise sources, noise reference microphones, secondary control sources and cancelling error detection microphones. First, a general structure of the multi-channel active noise control system is clarified and a perfect cancellation condition by a feedforward controller is presented. Secondly, a new LMS type of robust adaptive algorithm for updating the feedforward controllers is proposed, which can assure a stability of the adaptation and keep the each cancelling error within a tight bound by efficiently using prior information on each upper bound of the uncertainty terms. In experimental studies using an air duct, the proposed algorithm is compared with an ordinary filtered-x algorithm which does not always possess the property of stable convergence.

This paper presents a decentralized robust model reference adaptive control design for continuous time linear time invariant interconnected large-systems in the presence of" disturbances and/or unmodeled dynamics. The scheme of adaptation is based on the concept of dynamic certainty equivalence (DyCE) and incorporates the design of a decentralized fixed(non-adaptive) compensator in the error feedback loop. The fixed compensator is used to attain the strictly properness of an error transfer function matrix against the cross-coupling and unmodeled dynamics effects, furthermore it can realize the high performance of a tracking. The key features of our control design are: (1) use of H∞minimization design for the decentralized fixed compensator, and (2) use of a robust high-order estimator with a-modification for the effects of cross-coupling.

A new adaptive controller for rejecting periodic disturbances is proposed. The controller is constructed in a plug-in manner, i.e., it can be added to an existed feedback control system without altering the original closed-loop configuration. It is shown that the controller can reject disturbances at selected frequencies independently. Furthermore the proposed design method of the plug-in adaptive controller is based on linear control theory although the closed loop system includes the adaptive algorithm as nonlinear operator.