Presence Of Inputs Research Articles

Input-output theory of the unconventional photon blockade

We study the unconventional photon blockade, recently proposed for a coupled-cavity system, in presence of input and output quantum fields. Mixing of the input or output channels still allows strong photon antibunching of the output field, but for optimal values of the system parameters that differ substantially from those that maximize antibunching of the intracavity field. This result shows that the specific input-output geometry in a photonic system determines the optimal design in view of single-photon device operation. We provide a compact analytical formula that allows finding the optimal parameters for each specific system geometry.

Analysis and design of robust estimation filter for a class of continuous-time nonlinear systems

This article addresses the design of a multiconstrained robust estimation filter (MREF) for a class of continuous-time nonlinear systems in the presence of input and output disturbances. By constructing an augmented system, the MREF achieves the estimation of both states and faults, under existence conditions that are less restrictive than those associated with existing adaptive fault diagnosis observer (AFDO) and sliding mode observer design. Moreover, a detailed discussion on and comparison with the AFDO design are given. Furthermore, by introducing slack variables, improved results on MREF design are obtained such that different Lyapunov functions can be separately designed for multiple constraints. Simulation results are presented to illustrate our contributions.

A 10-b 100-kS/s 1-mW General-Purpose ADC for Cellular Telephones

An on-demand general-purpose analog-to-digital converter (GPADC), which achieves a 10-b accuracy of up to 100-kS/s request rate, is presented. The GPADC can process single-ended signals with selectable input ranges up to the battery voltage. The range selection accuracy is 1 LSB. The DAC architecture presents the benefits of a differential approach while sampling single-ended input signals. Built-in reference buffer and oscillator are included. An external clock source is not needed; only a conversion request signal is necessary. Dedicated low-power design solutions have been introduced to reduce power consumption at the maximum conversion request speed to about 1 mW at a 2.0-V supply voltage. New design solutions have been introduced to comply with system-level requirements, such as scaling and shifting of the battery voltage, buffering low-frequency input signals with high source impedance, and to cope with the presence of input signals with voltage levels higher than the GPADC supply voltage when the battery is discharged. The active area is about 0.58 mm2 in a 0.35-μm single-poly 5-metal CMOS technology with a MIM capacitor option.

Global Tracking Control of Underactuated Ships With Input and Velocity Constraints Using Dynamic Surface Control Method

This paper proposes a global tracking control method for underactuated ships with input and velocity constraints using the dynamic surface control (DSC) method, where the control structure is formed in a modular way that cascaded kinematic and dynamic linearizations can be achieved similarly as in the backstepping method. First, the first step linearization of the kinematics determines the pseudo (or auxiliary) surge velocity and yaw angle, which are used as the commands for the second-step linearization. Then, in the second-step linearization of dynamics, the actual torque inputs are designed to make the actual surge velocity and yaw angle follow these pseudo commands to achieve the position and yaw angle tracking. By employing the dynamic surface control method in the design of each kinematic and dynamic linearization law, we can obtain a control structure that is much simpler than the previous backstepping-based controllers such that it is beneficial from the practical application viewpoint. In addition, it is possible to track general reference trajectories, i.e., the reference yaw velocity need not be persistently exciting and there is no restriction on the initial yaw tracking error. In particular, global tracking control is achieved even in the presence of input and velocity constraints, unlike the DSC method which introduces the several filters in the backstepping design procedure to avoid the model differentiation and make it easier to be implemented and usually has semiglobal tracking performance. Finally, the stability analysis and numerical simulations are performed to confirm the effectiveness of the proposed scheme.

Model predictive control of Kuramoto–Sivashinsky equation with state and input constraints

This work focuses on the model predictive control design methodology that successfully accounts for the state and input constraints applied in the context of highly dissipative Kuramoto–Sivashinsky (KS) partial differential equation (PDE) describing stability of a thin film thickness in the two-phase annular flow in vertical pipes. The evolution of a linear dissipative KSE PDE state is given by an abstract evolution equation in an appropriate functional space. The proposed constrained predictive control law utilizes a low order modal representation in the optimization functional, while higher modes are included only in the PDE state constraints. Simulation results demonstrate a successful application of the proposed predictive control technique that achieves optimal stabilization of a spatially-uniform unstable steady state of Kuramoto–Sivashinsky equation in the presence of input and state constraints.

Hybrid fault diagnosis for telerobotics system

Fault detection and isolation ( FDI) of a class of networked control systems ( NCS), applied for telerobotics system is studied in this paper. The considered NCS application is related to telerobotics system, where it is modelled with a hybrid manner, by including the continuous, discrete, uncertain, and stochastic aspects of all the system components. The main considered components of the NCS namely the network system and controlled system are completely decoupled according to their operation characteristics. The network part is taken as a discrete and stochastic system in presence of non-structured uncertainties and external faults, while the controlled part is considered as a continuous system in presence of input and output faults. Two model based fault diagnosis approaches are proposed in this paper. The first concerns a discrete and stochastic observer applied to the network system in order to detect and isolate system faults in presence of induced delay on the network part. The second is based on the analytical redundancy relations ( ARR) allows detecting and isolating the input and output system’ faults. Experimental results applied on telerobotics system, show the performance and the limit of the proposed fault diagnosis approach.

Discussion on: “Identification of ARX and ARARX Models in the Presence of Input and Output Noises”

Discussion on: “Identification of ARX and ARARX Models in the Presence of Input and Output Noises”

Identification of ARX and ARARX Models in the Presence of Input and Output Noises

ARX (AutoRegressive models with eXogenous variables) are the simplest models within the equation error family but are endowed with many practical advantages concerning both their estimation and their predictive use since their optimal predictors are always stable. Similar considerations can be repeated for ARARX models where the equation error is described by an AR process instead of a white noise. The ARX and ARARX schemes can be enhanced by introducing the assumption of the presence of additive white noise on the input and output observations. These schemes, that will be denoted as “ARX + noise” and “ARARX + noise”, can be seen as errors-in-variables models where both measurement errors and process disturbances are taken into account. This paper analyzes the problem of identifying ARX + noise and ARARX + noise models. The proposed identification algorithms are derived on the basis of the procedures developed for the solution of the dynamic Frisch scheme. The paper reports also Monte Carlo simulations that confirm the effectiveness of the proposed procedures.

Noisy FIR Identification as a Quadratic Eigenvalue Problem

This correspondence describes a method for identifying FIR models in the presence of input and output noise. The proposed algorithm takes advantage of both the bias compensation principle and the instrumental variable method. It is based on a nonlinear system of equations whose unknowns are the FIR coefficients and the input noise variance. This system allows mapping the noisy FIR identification problem into a quadratic eigenvalue problem. The identification problem is thus solved without requiring the use of iterative least-squares algorithms. The performance of the proposed approach has been tested and compared with that of other identification methods by means of Monte Carlo simulations.

Boundary model predictive control of Kuramoto–Sivashinsky equation with input and state constraints

In this work an asymptotic stabilization of highly dissipative Kuramoto–Sivashinsky equation (KSE) by means of boundary model modal predictive control (MMPC) in the presence of input and state constraints is demonstrated. The KS equation is initially defined in an appropriate functional space setting and an exact transformation is used to reformulate the original boundary control problem as an abstract boundary control problem of the KSE partial differential equation (PDE). An appropriate discrete infinite-dimensional representation of the abstract boundary control problem is used for synthesis of low dimensional model modal predictive controller (MMPC) incorporating both the pointwise enforced KSE state constraints and input constraints. The proposed control problem formulation and the performance of the closed-loop system in the full state feedback controller realization have been evaluated through simulations.

Absolute stability and integral control for infinite-dimensional discrete-time systems

We derive absolute stability results of Popov and circle-criterion types for infinite-dimensional discrete-time systems in an input–output setting. Our results apply to feedback systems in which the linear part is the series interconnection of an l 2 -stable linear system and an integrator and the nonlinearity satisfies a sector condition which allows for saturation and deadzone effects. The absolute stability theory is then used to prove tracking and disturbance rejection results for integral control schemes in the presence of input and output nonlinearities. Applications of the input–output theory to state-space systems are also provided.

Periodic use of time-varying state feedbacks for the receding horizon control of bilinear systems

This paper provides a receding horizon control method for SISO bilinear systems in the presence input constraints. Periodically-invariant sets are derived for a bilinear system with respect to a series of time-varying state feedback gains. The dual-mode control strategy is adopted and the periodically-invariant sets are used as target invariant sets. The state feedback gains used to define the target invariant sets are also used to render degrees of freedom to steer the current state into the target set. The region of attraction for the proposed algorithm is enlarged significantly with an extension of the horizon of periodicity while the on-line.

Constructive Methods for Initialization and Handling Mixed State-Input Constraints in Optimal Control

Newmethodsarepresented toaddresstwoissuesin indirectoptimalcontrol:the calculationofastarting pointfor the numerical solution and the consideration of mixed state-input constraints. In the first method, an auxiliary optimal control problem is constructed from a given initial trajectory of the system. Its adjoint variables are simply zero. This auxiliary problem is then used within a homotopy approach to eventually reach the original optimal control problem and the desired optimal solution. The second method concerns the incorporation of mixed stateinput constraints into the dynamics of the considered optimal control problem. It uses saturation functions which strictly satisfy the constraints. In this way, the original constrained optimal control problem is transformed into an unconstrained one with an additional regularization term. The two approaches are derived within a general framework. For sake of illustration, they are applied to the space shuttle reentry problem, which represents a challenging benchmark due to its high numerical sensitivity and the presence of input and heating constraints. The reentryproblemissolvedwithacollocationmethodanddemonstratestheapplicabilityandaccuracyoftheproposed constructive methods.

Corrective control of asynchronous sequential machines in the presence of adversarial input

The problem of controlling asynchronous sequential machines in the presence of input disturbances is presented, which may be also regarded as an adversary in a game theoretic setting. The adversarial input is supposed to be measurable but uncontrollable by outer control units. The main objective is to develop state-feedback controllers that eliminate the effect of the adversarial input, while controlling the machine so as to match a prescribed model. Necessary and sufficient conditions for the existence of appropriate controllers are derived in terms of inequality relation between two numerical matrices. Whenever controllers exist, algorithms for their design are outlined and a case study is provided.

Total least squares in fuzzy system identification: An application to an industrial engine

Takagi–Sugeno fuzzy models have proved to be a powerful tool for the identification of nonlinear dynamic systems. Their generic nonlinear model representation is particularly useful if information about the structure of the nonlinearity is available. In view of a practical applicability in industrial applications two important issues are addressed. First, the problem of unbiased estimation of local model parameters in the presence of input and output noise is considered. For that purpose the concept of total least squares for parameter estimation is reviewed and a related partitioning algorithm based on statistical criteria is presented. Second, the steady-state accuracy of dynamic models is addressed. A concept of constrained TLS parameter optimisation is introduced which enforces the adherence of the model to selected steady-state operating points and thus significantly improves the model accuracy during steady-state phases. Results from a simulation model and from an industrial gas engine power plant demonstrate the capabilities of the proposed concepts.

Control of a planar system with quantized and saturated input/output

The stabilization problem for a simple (unstable) planar system in the presence of input and output quantization and saturation is addressed. It is shown that global stability to a terminal set, the size of which can be arbitrarily reduced, is achieved by means of a hybrid output feedback control law, which reads out the plant only three values and delivers a control action composed of three values. Directions to extend the proposed results to a class of -dimensional systems are also provided. Simulation results complete the work.

Adaptive receding horizon predictive control for constrained discrete-time linear systems with parameter uncertainties

This paper proposes a discrete-time model predictive control (MPC) scheme combined with an adaptive mechanism. To this end, first, an adaptive parameter estimation algorithm suitable for MPC is proposed, which uses the available input and output signals to estimate the unknown system parameters. It enables the prediction of a monotonically decreasing worst-case estimation error bound over the prediction horizon of MPC. These distinctive features allow for future model improvement to be explicitly considered in MPC. Thus, a less conservative adaptive-type MPC controller can be developed based on the proposed estimation method. Second, we show how the discrete-time adaptive-type state-feedback MPC controller is constructed by combining the on-line parameter estimation scheme with a modified robust MPC method based on the comparison model. The developed MPC controller guarantees feasibility and stability of the closed-loop system theoretically in the presence of input and state constraints. A numerical example is given to demonstrate its effectiveness.

On robustness of constrained discrete-time systems to state measurement errors

In this note we show that robustness with respect to additive disturbances implies robustness with respect to state measurement errors and additive disturbances for a class of discrete-time closed-loop nonlinear systems. The main result is formulated in terms of input-to-state stability and includes the possible presence of input and state constraints. Moreover, the state feedback controllers are allowed to be discontinuous and set-valued and thus the result also applies to model predictive control laws.

Bilinear equation method for unbiased identification of linear FIR systems in the presence of input and output noises

The presence of contaminating noises in both the input and output of an FIR system usually results in a biased least squares (LS) parameter estimate. The total least squares (TLS) methods are known to be efficient in achieving unbiased estimation, if the ratio of the input noise variance to the output noise variance (NNR) is known. However, when the NNR is unknown, a simple analysis shows that the classical LS and TLS estimation methods usually have such insufficient degree of freedom that they can achieve the unbiased solution. In this paper, it is shown by analyzing the algebraic structure of the correlation matrix that the unbiased estimate of FIR parameters can be obtained by solving a special bilinear equation. Then we develop a bilinear equation method (BEM) for solving the bilinear equation associated with the unbiased solution of the FIR system or filtering under the unknown NNR. Unlike the available unbiased estimators, the main advantage is that the proposed method exploits much sufficiently the special structure of the correlation matrix and obtains much accurate estimation for FIR filtering in the presence of input and output noises. Two simulation examples are presented that show the good performance of the proposed method, including its superiority over the classical LS and TLS approaches, and the instrumental variable methods.

Nonlinear Fault Detection and Isolation in a Three-Tank Heating System

We consider the fault detection and isolation (FDI) problem for a nonlinear dynamic plant (the IFATIS Heating System Benchmark) affected by actuator and/or sensor faults. A general procedure is proposed for modeling faults of sensors that measure the state of a nonlinear system, so that each sensor fault is typically associated to a set of (always concurrent) fault inputs and the resulting dynamic equations are affine in the introduced fault inputs. This allows the application of recently developed nonlinear FDI techniques, slightly extended to cover the considered model structure. For the presented case study, assuming nonconcurrency of the faults in the hardware components, we describe in detail the modeling procedure, the synthesis of residual generators, and the design of a combinatorial logics that recovers perfect isolation. Simulation results are reported in the presence of input and measurement noise