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In this paper, a new fault diagnosis methodology based on nonlinear system modelling and frequency analysis is developed. The method is derived via resolving several fundamental issues associated with conducting system fault diagnosis using the NARMAX (Nonlinear Auto-Regressive Moving Average with eXougenous inputs) modelling and NOFRFs (Nonlinear Output Frequency Response Functions) based frequency analysis, which was recently proposed and extends the well-known linear system modelling and FRF (Frequency Response Function) based frequency analysis to the nonlinear case. Simulation studies verify the effectiveness of the new method and demonstrate the performance of the method when applied to address a mechanical structural system fault diagnosis problem. The new methodology has the potential to resolve a wide range of input output data based engineering system fault diagnosis problems

Variants of the S -procedure provide an important tool in robust stability and robust performance design, mainly in the state space framework based on linear matrix inequalities. The aim of this paper is to formulate three classical results, the basic S -lemma, the Finsler's lemma and a variant of the full-block S -procedure, in the input output framework, i.e., in the infinite dimensional setting. While the presented results widen the scope of the applicability of these fundamental tools, they also provide a link to the theory of indefinite spaces, as an efficient framework to handle robust control problems.

A new type of Chen-Fliess series is first introduced which depends on the input and delayed versions of the input. It is then shown how a class of analytic differential delay systems with a single delay and constant initial conditions has input-output maps representable in terms of this new functional series. As in the classical case, the coefficients can be computed by iterated Lie derivatives, but here the method is applied to an infinite dimensional embedding of the original state space system. Finally, the more technical issue of series convergence is addressed. Sufficient conditions are produced to guarantee convergence in both a local and global sense.

This paper studies the multi-agent consensus problem of complex networks consisting of general homogeneous continuous linear time-invariant systems with controls under a time-invariant directional communication topology. A new approach based on relative-input-output is proposed to solve the problem. In contrast to a popular method where each agent is equipped with an observer, and hence involving heavy observer dynamics, the proposed approach is static and utilizes relative-input and relative-output eliminating the observers aforementioned, therefore is practically much simpler and easier to implement significantly reducing computational and communicational burdens and costs. Extension of the framework is then made to tackle a dual problem: consensus of distributed filtering without controls. Two numerical examples are provided to validate the proposed approach.

A method is proposed to detect if there is no coupling between an input and an output in systems operating in open-loop. The proposed technique is applicable to multiple input multiple output (MIMO) systems, i.e., the intent is to detect zeros in a transfer matrix. Traditional approaches to input/output (IO) selection are usually performed after the plant model is identified. The proposed approach is applied during the pre-identification stage and it is based on cross-correlation and fuzzy logic analysis. A study case involving identification of a 20 × 10 real system is discussed, as well as the advantages of detecting no-model IO pairs in the identification process.

Baker's yeast fermentation is influenced by the relative concentration of glucose and dissolved oxygen (DO) in the reactor. The process is sensitive to the oxidative capacity of the cells and exhibits a range of metabolic regimes depending on available glucose and oxygen. The time profiles of cell mass, glucose, ethanol and DO concentrations possess strong nonlinear characteristics. A decoupled input-output linearizing controller (DIOLC), which exhibited satisfactory performance in simulation experiments, was implemented online for validation. Our results showed that the DIOLC was capable of negating the interaction between glucose and dissolved oxygen, and executed satisfactory control action in experiments carried out in a 5 liter bioreactor. The performance of DIOLC was better compared to that of a PID controller implemented under identical test conditions.

The problem of finding an input-output representation of a nonlinear state space system, usually referred to as the state elimination, plays an important role in certain control problems. Though, it has been shown that such a representation, at least locally, always exists for both the systems with and without delays, it might be a neutral input-output differential equation in the latter case, even when one starts with a retarded system. In this paper the state elimination is therefore extended further to nonlinear neutral state-space systems, and it is shown that also in such a case an input-output representation, at least locally, always exists. In general, it represents a neutral system again.

This paper is concerned with the identification of nonlinear processes which are parameterized as linear parameter varying (LPV) models. The LPV models are represented as blended linear models by using ADAptive LINear Element (ADALINE) structure. Firstly, ADALINE is employed to represent local models describing the system's behaviour at each fixed working point. Then, a novel method is proposed to estimate parameters of weight functions such that the global LPV model can be built by interpolating the local ones. The adaptation algorithm is discussed and extensive simulation studies are conducted to verify the effectiveness of this method.

This paper deals with a design problem of a performance-driven adaptive output feedback control with a parallel feedforward compensator (PFC). A PFC design method which using an input/output experimental data set will be proposed in order to design a performance-driven adaptive output feedback control system. Furthermore, the effectiveness of the proposed PFC design and an adaptive output feedback control method will be confirmed through numerical simulations.

The main issue in controlling safety-critical systems such as nuclear power reactors or railway signalization systems where risk ratio is at the highest level because small errors might result in hazardous accidents (e.g. death or injury of several people) is to satisfy safety-related standards and provide high-safety with high-reliability. In order to improve reliability and safety of such systems to the required level by decreasing common-cause failures at the same time, diverse programming (or N-version programming) technique where N-different independently designed modules run in parallel on the same input output specifications can be used. In this study, a new bitwise voting strategy based on safe-states of variables is proposed and possible critical races between modules those run in a parallel manner are determined. Solutions for critical races are also explained.

This paper presents a new control design approach for discrete-event systems described by Input/Output automata. A formal design method guarantees the fulfillment of the specifications for the closed-loop system including the system safety. Necessary and sufficient conditions for the well-posedness of the control loop and the controllability of the plant with respect to the specification are proved. The control of a batch process is used to illustrate the results.

Hysteresis is a nonlinear behavior encountered in a wide variety of processes in which the input-output dynamic relations between variables involve memory effects. In Ikhouane (2010) a framework aimed to characterize these systems is proposed. The systems that are considered are seen as operators that map an input signal and initial condition to an output signal, all of them belonging to some specified sets. A definition of the quasi-static regime is motivated and proposed. The aim of the present paper is to generalize the concepts introduced in Ikhouane (2010).

International audience; Identification of real life systems is often applied in closed-loop due to stability, performance or safety constraints. However, in case of Linear Parameter-Varying (LPV) systems, closed-loop identification is not well-established despite the recent advances in prediction-error identification. Building on the available results, the paper proposes the closed-loop generalization of a recently introduced instrumental variable scheme for the identification of LPV-IO models with Box-Jenkins type of noise models. Estimation under closed-loop conditions is analyzed from the stochastic point of view with the proposed approach and the performance of the method is demonstrated through a representative simulation example.

Recently, a generalized passivity concept for linear multivariable systems was obtained which allows circumventing the restrictiveness of the usual passivity concept. The latter is associated with the classical SPR (Strictly Positive Real) condition whereas the new concept of passivity is associated with the so called WSPR condition and its advantage in multivariable systems is that it does not require a restrictive symmetry condition of SPR systems. As a result, it allows the design of multivariable adaptive control that, unlike some existing factorization approaches, does not imply in additional overparameterization of the adaptive controller. In this paper, we complete a previously established WSPR sufficient condition and prove that it is also necessary. We also propose some methods of passification by either premultiplying the system output tracking error vector or the system input vector by an adequate passifying matrix multiplier, so that the resulting input/output transfer function becomes WSPR. The efficiency of our proposals are illustrated by simulation utilizing a well known robotics adaptive visual servoing problem.

This paper proposes a new event-based control method for nonlinear systems that are input-output linearizable. The control input generator uses a copy of a continuous reference system to generate an exponential control input that keeps the state of the disturbed plant in a bounded surrounding of the setpoint. An upper bound for the deviation of the event-based control loop to the reference system is derived, which depends on the event threshold of the event generator. Hence, by appropriately choosing the event threshold, the event-based control can be made to mimic the continuous control with arbitrary accuracy. The proposed event-based control method is applied to a cooling process.

This paper presents an extension of the well known linear time invariant identification theory to Linear, Periodically Time-Varying (LPTV) systems. The considered class of systems is described by ordinary differential equations with coefficients that vary periodically over time, making use of multisines both for excitations as well as for the time-varying system parameters. To solve the model equation, an efficient frequency domain simulator is built and is compared with the classically time integration solvers. Further, a frequency domain identification algorithm is proposed within an errors-in-variables stochastic framework. This approach determines a parametric model for the LPTV-system from noisy input-output data. The developed estimation theory is also verified on a simulation example.

This paper presents a new approach for modeling a discrete-event systems under the presence of faults. Input/Output automata are used to represent the nominal and faulty behavior of a plant for fault detection and identification. Assuming that a model of the nominal behavior of a plant is available, it is usually a nontrivial task to derive a model of the plant subject to actuator, sensor and system internal faults. This task often require some expertise or a large amount of data on the faulty behavior of the plant. The key aspect of the paper is the introduction of suitable functions enabling to reduce the amount of information needed to model a faulty behavior. The method presented here fits in the nondeterministic Input/Ouput automata framework where actuator, sensor and internal system faults are considered. The proposed approach is applied to a chemical plant for demonstration.

In this paper, the model predictive control strategy based on input and output data sets for partial differential equation (PDE) unknown spatially-distributed system (SDS) is proposed. The control aim is that the outputs of low-dimensional temporal model reach the set points. Thus, it makes the control design easily and reduces the computational burden. The low-dimensional model is obtained by principal component analysis (PCA) method, and the state of the low-dimensional model is estimated based on spatially-distributed output. The terminal constraints are used to transform the cost function along an infinite prediction horizon into finite prediction horizon. The simulations demonstrated show the accuracy and efficiency of the proposed method.

In this paper, we propose the parameterization of all robust stabilizing simple multi-period repetitive controllers with the specified input-output characteristic. Recently, Yamada et al. proposed the parameterization of all robust stabilizing simple multi-period repetitive controllers for the plant with uncertainty such that the controller works as a robust stabilizing multi-period repetitive controller and transfer functions from the periodic reference input to the output and from the disturbance to the output have finite numbers of poles. However, using the method by Yamada et al., it is complex to specify the low-pass filters in the internal model for the periodic reference input of which the role is to specify the input-output characteristic. Because, the low-pass filters are related to three kinds of free parameters in the parameterization proposed by Yamada et al. Our proposed parameterization in this paper overcome this problem by specifying the low-pass filters in the internal model for the periodic reference input beforehand. That is, using our parameterization, we can design a robust stabilizing simple multi-period repetitive control system which has desirable input-output characteristic more easily than the method proposed by Yamada et al.

In this paper, the incremental-dissipativity is used to investigate synchronization of a class of dynamical networks from an input-output point of view. The network consists of subsystems described by input-output operators. Firstly, the concept of incremental-passivity is extended to incremental-dissipativity, and it shows that the incremental-dissipativity of the subsystems leads to the dissipativity of a group of virtual systems each of which is related to an individual subsystem. Then the synchronization problem is transformed into the stability problem of a system interconnected by the virtual systems. By utilizing the stability theory of large-scale systems, a sufficient condition is obtained which ensures the input-output synchronization of the network. The counterparts of the main result and the discussion on conditions of the incremental-dissipativity for the subsystems with state space representations are also presented. Finally, an example is given to show the effectiveness of the proposed result.

We prove the global controllability for a class of nonlinear MIMO Volterra systems of the triangular form as well as for their bounded perturbations. In contrast to the related preceeding work [12], we replace the condition of C 1 smoothness, which was essentially used before, with that of local Lipschitzness. Furthermore, we remove the assumption of the invertibility of the input-output interconnections, which was also essential in these preceeding results. In order to solve the problem, we revise the backstepping procedure proposed in these works, and combine it with another method of constructing discontinuous feedbacks proposed for the so-called “generalized triangular form” in the case of ODE [17, 13, 19].

This paper proposes an FRIT method for mainly improving the feedback property. While the conventional FRIT tunes the control parameters so that the plant output follows the reference model output, the proposed one tunes the control parameters so that the disturbance response follows the disturbance reference model output, which represents a feedback property. Furthermore, the paper shows that the FRIT can tune the control parameters from input-output data caused by the disturbance response if precise estimated disturbance signals are obtained. The paper also derives the analytical pre-filter for the one-shot experimental input-output data in the proposed FRIT which makes the controller, determined in the proposed FRIT, a good approximation to the controller optimizing the original performance index, which evaluates the deviation of the disturbance response from the disturbance reference model output.

This paper deals with nonlinear retarded time-delay systems that surprisingly admit an input-output representation of neutral type. It is shown that such an unexpected behaviour represents a strictly nonlinear phenomenon, for it cannot happen in the linear time-delay case where retarded systems always admit an input-output representation of retarded type. A necessary and sufficient condition under which the nonlinear systems admit a neutral input-output representation is given and strategies for finding an inputoutput representation of retarded type are briefly outlined. Some open problems that arise, like minimality and system transformations, are discussed as well.

The disturbance decoupling and the simulteneous disturbance and input – output decoupling problems for singular systems are considered in the context of the matrix fraction description (MFD) of the system. Solvability conditions are obtained in terms of the composite matrix of a column reduced MFD of the system, a characterisation of the fixed poles of both problems is given and it is shown that the remaining poles can be arbitrarily assigned.

Control reconfigurability, from the fault tolerance point of view, defines the capacity of the control system to allow restoration of performance in the presence of faults. For its evaluation it is required to calculate the controllability gramian. This calculation is usually carried on before control system be implemented, i.e., off-line. Instead, the on-line evaluation of the control reconfigurability from input/output data could be useful for the fault tolerance redesign. The contribution of the paper is to propose a way of calculating on-line the controllability gramian from input/output data in order to evaluate an index based on the control reconfigurability. The paper aims to estimate the reconfigurability for a linear system under partial loss of control effectiveness. The controllability gramian could be computed in direct form from the initial condition response. For practical aspects, an identification algorithm is proposed in order to compute the system controllability gramian, independently of initial conditions. An example is presented to illustrate the method.

This work aims at evaluating the use of TS MIMO model for the Detection and Isolation of faults of sensors on a nonlinear process. TS model involves a set of local linear models whose contribution on the output signals is expressed by a weighting function. In a previous work, an identification scheme was proposed for TS MIMO model. It doesn't need for the outputs of the process to be separable, as it is usually the case for the identification methods based on MISO model. A model-based method, for faults detection is proposed from the identified model. This new approach differs from the observers and parity space based approaches, as it only uses a partial knowledge of the model. Outputs are projected into a sub-space that is orthogonal to the sequences of input-output weighted signals to provide a residuals set. The process of fault isolation exploits the structure of the residuals set in order to recognize the acting fault. The sensitivity of the residuals to the occurrence of faults is proved while the incidence matrix of the residuals is deduced from simulation. It shows the ability of the generated set of residuals to isolate the fault of sensors.

Polynomial input–output recursive models are widely used in nonlinear model identification for their flexibility and representation capabilities. Several identification algorithms are available in the literature dealing both with model selection and parameter estimation, based on various criteria. Previous works have shown the limits of the classical prediction error minimization approach, and suggested the use of a simulation error minimization approach for better model selection. The present paper goes a step further by integrating the model selection procedure with a simulation oriented parameter estimation algorithm. Notwithstanding the algorithmic and computational complexity of the proposed method, it is shown that it can achieve significant performance improvements with respect to previously proposed approaches.

This paper addresses suboptimal control of nonlinear systems which can be feedback-linearized from input to output. The case of input-to-state linearizable systems is also covered as a special case. The method is thus applicable to all nonlinear systems which can be partially linearized using the method of output-feedback linearization while having a stable internal (or zero) dynamics. The well-known LQR technique applied to the linearized system does not guarantee the suboptimality of the nonlinear system. This paper uses output feedback linearization technique to partially linearize the system and then designs an output-feedback for the feedback-linearized system in such a way that it ensures suboptimal performance of the original nonlinear system. The proposed method can optimize any arbitrary smooth function of states and input. The proposed controller is, however, suboptimal due to the facts that (1) the form of the controller is a linear static feedback of the linearized state, (2) the search algorithm may fall into a local extremum rather than a global, and (3) the calculated controller depends on the initial conditions. The method is successfully applied to control design of the longitudinal subsystem of a laboratory double-rotor helicopter and the results are discussed and compared with those of the LQR method.

In this work we develop an input-output recurrent neurofuzzy network in discrete-time for identification and control of nonlinear systems. The structure is linear in the consequent parameters and nonlinear in the antecedent ones. The training of the antecedent parameters is achieved by linearizing them around a suboptimal value, assuming that the only known data are input-output signals obtained directly from measurements of the system, as well as some information about its structure (local stability and time delays). The training algorithm is based on a Kalman filter, stable under certain assumptions. It is also presented a theorem to check the stability of the resulting network in the Lyapunov sense, and a predictive control design. The performance of the network is shown by the identification and control of a nonlinear benchmark system.

A new approach for component-oriented modeling of asynchronous discrete-event systems is presented where input-output (I/O) automata are used for representing the components. Coupling signals are introduced to describe the interactions among the components. The resulting network of I/O-automata has a direct correspondence to the block diagram. By using the parallel composition rule known from standard automata modeling as an example, it is shown that the new model is applicable for at least the same class of asynchronous discrete-event systems as the known modeling formalisms.

The paper studies the nonlinear control systems on time scales that serve as models of time and unify the continuous and discrete time. The realization problem for single-input single-output nonlinear delta differential systems is studied through the language of differential forms. Necessary and sufficient conditions are given for the existence of a state-space realization of the nonlinear input-output delta differential equation, i.e. a higher order delta differential equation relating the input, the output and a finite number of their delta-derivatives. The sufficiency part of the proof gives a constructive procedure (up to the integration of one-forms) for obtaining an observable state-space description.

This paper studies the realizability property of continuous-time quadratic input-output (i/o) equations in the classical state space form. Constraints on the parameters of the quadratic i/o model are suggested that lead to realizable models. The complete list of 2nd and 3rd order realizable i/o quadratic models is given and two subclasses of the nth order realizable i/o quadratic systems are suggested. Our conditions rely basically upon the property that certain combinations of coefficients of the i/o equations are zero or not zero. We provide explicit state equations for realizable 2nd order quadratic i/o equations, and for one realizable subclass of quadratic i/o equations of arbitrary order.

Actuators and Sensors, Implemented Solutions. The frequency response is undoubtly one of the basic methods of measure and characterization of systems. The knowledge of this technique and its application is essential in subjects such as Automatic and Electronic. In this paper a low cost circuit that allows to obtain, automatic or semiautomaticaly, the frequency response of a linear system is presented. This circuit, that responds to a specific design of the authors, will be called “bodemeter” due to its obvious application for drawing the Bode plot. The input and output of the system to study are directly connected to the bodemeter. This can be used with conventional instrumentation or it can be connected to a personal computer through a data acquisition card. The computer does not require any special characteristic as for speed, and it is solely recommendable to use a graphic output to visualize the plots; this characteristic is broadly overcome at present. The data acquisition card does not need special features either and any conventional low cost card can be used.

The paper suggests constraints on the parameters a i , b i , c ij of the bilinear continuous-time input-output model that yield generalized state equations with input derivative order lower than that in the input-output equations. In the limiting case when one removes the input derivatives altogether, these conditions provide a solution of the realizability problem.

Intra- and inter-patient variability poses a challenging task to control blood glucose concentration in diabetic patients. A data based model predictive control with state and disturbance estimation has been developed to control the blood glucose concentration in the type-I diabetic patients in the presence of meal disturbances under patient-model mismatch. Simulation studies were performed on three distinct patient models generated as a result of sensitivity analysis, which revealed that the proposed control strategy is able to control the blood glucose concentration well within the acceptable limits and also able to compensate for the slow parametric drifts.

A new tool for selecting the right pairing between inputs and outputs in a multiloop system is introduced. Similar in structure to the classical RGA the new array, called a Relative Omega Array, is based on the characteristic frequencies in open and closed loop under perfect control, as eventually detected by a classical relay test. This way the dynamic properties of the system are simply taken into account. Some examples show that the new tool is effective, giving the correct pairing also when the RGA approach fails.

A reactive system is an entity which takes as inputs signals from a certain set, and transforms them to produce as outputs signals in some further set, where a signal is modeled as a function from a time domain to a value space, and time domains are linearly ordered sets. Building on our previous work which generalizes Aubin's Evolutionary Systems model, we develop a general formulation of non-deterministic input-output reactive systems that is uniform for discrete, continuous and hybrid time, and which extends Willems’ Behavioural Systems input-output model by allowing hybrid time, and by allowing different time domains for input and output signals. However, our approach differs from these existing frameworks in that instead of taking as primitive signals over infinite-length time domains, we work with finite-length paths and utilize their algebraic and partial-order structure. We illustrate our framework with a generic model of event-driven A/D conversion, transforming a continuous-time input signal into a discrete-time output signal via an intermediate hybrid-time signal.

System identification, or modelling from data, is used to develop dynamic models for control design, performance prediction, and system analysis. Some system identification algorithms are sensitive to the relative scaling of different input and output channels. Ideally, the identified model would be insensitive to arbitrary scaling of the input and output channels. For system identification algorithms that are scale-sensitive, re-scaling the input and output data can alleviate poor identification results due to initial inappropriate relative scaling of the data. In this paper we describe several methods for choosing weighting matrices for system identification with application to a laboratory experiment.

The objective of this contribution is to discuss three basic control design methods for disturbance rejection. The key idea is to use a simple fixed continuous time noise model as a design variable to specify the desirable closed loop properties, e.g., the closed loop bandwidth. We will study three closely related controller design methods based on the noise model, namely minimum variance control, weighted minimum variance control and a loop shaping design procedure proposed by Skogestad and Postlethwaite. All these methods lead to simple controller structures, where the transfer function of the input/output relation is the only unknown part. The main motivation of this study has been tuning of controllers for disturbance rejection by means of iterative feedback tuning (IFT). Here, the controller parameters are found by minimizing a specified cost function using gradient search techniques. The gradients are estimated using data from iterative experiments. This is a rather difficult problem if no external input excitation is allowed. Import factors for good results are the controller parametrization and the choice of cost function. We propose an IFT cost function based on a simple output signal weighting tailored to the fixed noise model controller structure. This leads to robust procedure for iterative feedback tuning of a controller for disturbance rejection. The approach is illustrated on a simple example. QC 20120104. Sponsors: 16th Triennial World Congress of International; Federation of Automatic Control, IFAC 2005

The paper presents a methodology of designing control logic that is implemented by industrial programmable logic controllers. A two stage approach is proposed. In the first stage a set of interlock supervisors is designed based on discrete-event model of the plant and a set of interlock specification models. Supervisory control theory is used to test the controllability of the specifications and to derive a finite automaton representation of the admissible behaviour of the system. In the second stage the model of admissible behaviour is adopted as a plant model and used for the verification of the sequential specification model in a form of a Petri net. The basic property of interest is the absence of blocking. To study the interaction of the two models an extension of Place/Transition nets is used, which includes external inputs and outputs, i.e., the Real-time Petri nets (RTPN). A new kind of reachability analysis is applied, which considers all possible changes of the controller input and output signals. This enables to verify the nonblocking operation of the sequential controller.

An LQ control problem for constrained discrete-time systems is discussed based on model predictive control with reduced order quadratic programming (QP) problem. By introducing a singular value decomposition for linear systems, where zero-order holder is embedded in the input signals, an approximation method of model predictive control is derived. The feature of resulting control system is illustrated with numerical examples.

In this paper, a new bias-compensated least-squares (BCLS) based algorithm is proposed for identification of noisy input-output system. It is well known that BCLS method is based on compensation of asymptotic bias on the least-squares (LS) estimates by making use of noise variances estimates. The main feature of the proposed algorithm is to introduce a generalized least-squares type estimator in order to obtain the good estimates of noise variances. The results of a simulated example indicate that the proposed algorithm provides good estimates.

A system is frequently represented by transfer functions in an input-output characterization. However, such a system (under mild assumptions) can also be represented by transfer functions in a port characterization, frequently referred to as a chain-scattering representation. Due to its cascade properties, the chain-scattering representation is used throughout many fields of engineering. This paper studies the relationship between poles and zeros of input-output and chain-scattering representations of the same plant.

In this paper, a new correlation analysis based algorithm is proposed for the identification of a class of nonlinear systems which can be described by the NARX (Nonlinear AutoRegressive with eXogeneous input) model with input nonlinearities. Without any assumptions about the structure of an approximating function for the system nonlineariy, the algorithm recovers the functional values of the nonlinearity over a discrete point set associated with the levels of the applied input and estimates the model parameters from the system input output data. An optimal approximating polynomial can then be determined from the nonlinear functional values to produce an optimal estimate for the system nonlinearity. Simulation studies are included to demonstrate the effectiveness of the new method.

The paper presents support solutions for designing, reconfiguration and testing embedded system applications with microcontrollers. The proposed method allows the reconfiguration of an industrial application, in different versions, by following the next steps: designing the core software independent to the hardware input and output signal pins; defining a table of pins – signals correspondence, for each particular configuration; adding particular software, involving usually just number of signals, input – output functions, etc. The proposed development system offers the needed functions for redesigning the application, without changing the core software, and even without knowing the assembler details of the application.

The paper deals with ways of implementation of simulation controlled systems by using QNX and RT Linux operating systems. The laboratory station consisted of PC computer and PLC was built. Station's elements were connected through voltage interface. PC computer was equipped with input output card. Controlled system's simulation was main PC's function. PC computer was also involved in data visualization. Exemplary simulation's program of electroheating controlled system using RT Linux is included. PLC was programmed to perform controller with Smith's predictor function. PLC's program is presented.

In this paper, we provide one of the powerful iterative tuning methods for the parameters of the controller of a closed loop system. The method we provide here requires only one-shot experiment and then an iterative non-linear optimization for obtaining the optimum parameter of the controller can be performed off-line by using the fictitious reference signal computed by the first experimental data (from this reason, we refer to our method as “fictitious reference iterative tuning”, which is abbreviated to “FRIT”). The main concept and the algorithm of FRIT are provided as our main result of this paper. Moreover, we give a control experiment of the cart-system in order to show the validity of FRIT.

Optimal control is considered for a linear time-invariant plant in the input-output data space. The proposed control strategy does not employ any traditional mathematical model such as a transfer function or a state-space equation. Instead, the plant dynamics is represented as a set of basis vectors whose elements are input-output data of the plant. Using this system representation, two optimal control problems are solved. One is to find the control input which minimizes a quadratic performance index. The other is to find the control input which minimizes a quadratic performance index subject to achieving dead-beat tracking.

Block Relative Gain (BRG) is a useful method for screening alternatives for block decentralized control at the design stage. In this paper, we establish the connection between BRG and closed loop properties like stability, input output controllability, block diagonal dominance and interactions. Based on these results, simple rules for pairing selection for block decentralized control are proposed. Published version

Autonomy of an underwater vehicle is in the main attributable to the design of a suitable guidance and control system. Generally, the control system is developed and tested first in simulation studies using a model of the vessel to gain confidence in the approach being adopted. Therefore, plant identification is imperative to gain insight about the system. This paper is concerned with a practical system identification (SI) method in order to obtain a model of an autonomous underwater vehicle (AUY) using input output data as opposed to painstaking mathematical modelling techniques. A model of the AUV is developed using SI and tested in simulations using a linear quadratic Gaussian (LQG) controller. Modelling and simulation results are presented.