Single-input And Single-output Research Articles

A sliding mode control using an extended Kalman filter as an observer for stimulus-responsive polymer fibres as actuator

This paper presents a single-input and single-output (SISO) adaptive sliding mode control (SMC) combined with an extended Kalman filter (EKF), which is used as an observer to control stimulus-responsive polymer fibres as an actuator. Conductive metal-polymer fibres are the fundamental core for wearable technology and an integral part of smart textiles. To control this actuator a SMC is combined with an EKF and used as an observer to estimate the velocity. Despite the particular simplified model of the considered actuator, the EKF presents a nonlinear Jacobian matrix. The parameter settings of the system and measurement covariance matrix, together with their initial values, are done heuristically. Because of the slow velocity of the fibre, the EKF produces poor estimation results. Therefore, a derivative approximation structure is proposed to estimate the velocity though the measurement of the position. Both estimations are analysed based on simulations. The simulation results indicate that the proposed algorithm is effective and robust.

Synthesis of mechanisms integrated with motion and force transformation

The synthesis of mechanisms has an important effect in the product development. A mechanism has two basic kinds of functions, i.e. to transform motion and to transform force. However, current methods always focus on either motion synthesis or force synthesis. The paper proposed a dynamic programming-based synthesis of multi-input or multi-output mechanisms with the simultaneous consideration of motion and force transmission. A general function model is proposed with a result of function matrix of mechanisms. After the solving process of single input and single output (SISO) system using dynamic programming algorithm is proposed, the single input and multiple outputs (SIMO) system, and the multiple inputs and single output system (MISO) are also discussed in detail, together with design evaluation. The synthesis of mechanisms for jacking up system in offshore platform is taken as an example, which demonstrates that the methodology is helpful to produce valuable mechanisms.

Scaling-based least squares methods with implemented Kalman filter approach for nano-parameters identification

A single-input and single-output (SISO) controlled autoregressive moving average system with scaled input-output data is considered here. Recursive least squares (RLSs) methods were used to estimate the nanosized parameters of a SISO linear model using input-output scaling factors. Thus, a general identification technique, through scaling data, was produced. Different variations of the RLS method were tested and compared. The first RLS method used a forgetting factor and the second method integrated a Kalman filter covariance. Using the described method, in order to estimate the resistance, time constant and inductance, the latter two lying within the nano range, the input signal must have both a high frequency and a high sampling rate, in relation to the time constant. The method developed here can be used to identify the nano parameters characterising the linear model, while allowing for a broader sampling rate and an input signal with lower frequency. Simulation results indicate that the proposed algorithm is both effective and robust at estimating the nano range parameters. The most powerful contribution contained here is the provision of a scaled identification bandwidth and sampling rate for the detecting signal in the identification process.

Spectral H2optimal correction of additive fault estimation observer

This study is devoted to design of the fault estimation adaptive observer. Initial observer is modified with correction filter in order to improve robustness against external disturbance with known spectral structure. Computation of correctors parameters is based on special spectral algorithm of SISO (Single Input and Single Output) H2 optimisation in frequency domain. Novel algorithm is proposed for practical implementation. Its effectiveness is demonstrated with example of successive additive fault estimation during marine ship yaw motion process.

Parameter Estimation and Model Order Identification of LTI Systems

In this paper, a sparsity seeking optimization method for estimating the parameters along with the order of output error models of single-input and single-output (SISO), linear time invariant (LTI) system is proposed. It is demonstrated with the help of simulations that the proposed algorithm gives accurate parameter and order estimates on a variety of systems.

Control design methods with anti-windup mechanism for disturbance rejection and reference following

There are many methods for designing feedback control systems with the objectives of disturbance rejection and reference following. Two well-known methods are to directly embed a disturbance model in the controller or to compensate for the effect of disturbance using its estimation from an observer. It is demonstrated in this paper that the first method has inherited a two-degree-of-freedom control system configuration, but has an integrator windup problem in the presence of actuator saturation, whilst the second approach has embedded an anti-windup mechanism, but suffers from poor performance in reference following. This paper examines these two mainstream control design methods for their applications in the presence of actuator saturation. It is shown with a simple example that the classical control system by directly embedding a disturbance model leads to integrator windup, and an anti-windup mechanism is hence proposed to overcome this drawback. The frequency response analysis presented in this paper leads to the conclusion that both methods offer similar closed-loop performance in terms of disturbance rejection and noise attenuation, however, the first approach that embeds a disturbance model in the controller provides a two-degree-of-freedom control system configuration, and hence gives a better performance in terms of reference following. Comparative simulation studies of both Single-Input and Single-Output (SISO) and Multi-input and Multi-Output (MIMO) systems are used to support the conclusions.

Robust adaptive asymptotic tracking control of a class of nonlinear systems with unknown input dead-zone

This paper considers the tracking control for a class of uncertain single-input and single-output (SISO) nonlinear strict-feedback systems with unknown input dead-zone nonlinearity, parametric uncertainties and unknown bounded disturbances. By constructing a smooth dead-zone inverse and applying the backstepping recursive design technique, a robust adaptive backstepping controller is proposed, in which adaptive control law is synthesized to handle parametric uncertainties and a novel robust control law to attenuate disturbances. The robust control law is developed by integrating a sufficiently smooth positive integral function at each step of the backstepping design procedure. In addition, a smooth projection mapping is used and assumptions are made that the prior knowledge of the extents of parametric uncertainties and the variation ranges of the bounds of disturbances is known to facilitate the backstepping recursive design. However, the exact bounds of disturbances are not required. The major feature of the proposed controller is that it can theoretically guarantee asymptotic output tracking performance, in spite of the presence of unknown input dead-zone nonlinearity, various parametric uncertainties and unknown bounded disturbances via Lyapunov stability analysis. Comparative simulation results are obtained to illustrate the effectiveness of the proposed control strategy.

Ziv–Zakai Bound for Joint Parameter Estimation in MIMO Radar Systems

Local bounds, such as the Cramer-Rao bound (CRB), provide inaccurate predictions under low signal-to-noise ratio (SNR) conditions. Global bounds are capable of providing more accurate predictions of the performance of estimators over the full range of SNR. In this paper, we derive the Ziv-Zakai bound (ZZB) for joint location and velocity estimation of a target illuminated by a non-coherent multiple-input multiple-output (MIMO) radar employing orthogonal waveforms and widely spaced antennas. The setup captures the multistatic nature of the target gains, where each pair of transmit-receive elements experience independent gains governed by the Swerling type 1 model. The target returns are observed in the presence of spatially and temporally independent Gaussian clutter-plus-noise. The ZZB for joint delay and Doppler estimation for single-input and single-output (SISO) radar is also developed. We show that the ZZB is a comprehensive metric that captures the effect of the SNR, the ambiguity function (AF) and other parameters of the radar systems. The effects of different system configurations are explored through numerical studies. The results are useful for the analysis of both active and passive radars.

Fuzzy adaptive output feedback DSC design for SISO nonlinear stochastic systems with unknown control directions and dead-zones

In this paper, the adaptive fuzzy output feedback control problem is investigated for a class of single-input and single-output (SISO) stochastic nonlinear systems unknown dead-zones and unknown control direction. In the design, by using a linear state transformation, the unknown control coefficient and the unknown slope characteristic of the dead-zone are lumped together, and the original system is transformed to a new system. Fuzzy logic systems are employed to identified the uncertain nonlinear systems, a nonlinear fuzzy state observer is constructed to solve the problem of unmeasured states, and a special Nussbaum function is introduced into the control design to solve the unknown control direction problem, respectively. To address the problem of “explosion of complexity”, the dynamic surface control (DSC) technique is employed, a stable adaptive fuzzy output-feedback tracking control scheme is developed. The stability is proven based on Lyapnov theory, and it can guarantee that all the variables of closed-loop system are bounded in probability, and tracking error converges to a small neighborhood of zero. A numerical example is provided to verify the effectiveness of the proposed method.

Friction coefficient estimation in servo systems using neural dynamic programming inspired particle swarm search

Parameter estimation of static friction torques in servo control systems is of great significance to their robust control. Many researchers are devoted to pursuing the solutions to estimating the coefficients of the static friction torques. In order to tackle the troublesome matter more effectively, in this paper, we address a neural dynamic programming inspired particle swarm search algorithm. We call the algorithm direct BP neural dynamic programming inspired PSO (NDPSO) since we incorporate direct back propagation (BP) and neural dynamic programming (NDP) into particle swarm optimization (PSO). In NDPSO, critic BP neural network is trained to balance the Bellman equation while action BP neural network is used to train the inertia weight, the cognitive coefficient, and the social coefficient of the PSO algorithm. The training target is to enable the critic BP neural network output to approach the ultimately successful objective. Successively, NDPSO, together with standard PSO (SPSO) and genetic algorithm (GA), is applied to the parameter identification of the static friction torque in a servo control system with single input and single output (SISO). The experimental results clearly demonstrate that NDPSO is effective and outperforms SPSO and GA in identifying the parameters of the static friction torque in the servo control system.

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems.

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.

Adaptive fuzzy backstepping control design for a class of pure-feedback switched nonlinear systems

In this paper, an adaptive fuzzy output tracking control approach is proposed for a class of single input and single output (SISO) uncertain pure-feedback switched nonlinear systems under arbitrary switchings. Fuzzy logic systems are used to identify the unknown nonlinear system. Under the framework of the backstepping control design and fuzzy adaptive control, a new adaptive fuzzy output tracking control method is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error remains an adjustable neighborhood of the origin. A numerical example is provided to illustrate the effectiveness of the proposed approach.

Experimental Assessment of a New Fuzzy Controller Applied to a Flexible Rotor Supported by Active Magnetic Bearings

The aim of this study was to develop and implement a new control approach dedicated to turbomachinery. The new, fuzzy based controller utilizes inputs expressed in polar coordinates. Its originality is that it manages two significant physical quantities, namely, tangential and radial velocities, associated with steady-state and transient behaviors, respectively. Three controllers are compared for the control of a flexible rotor supported by active magnetic bearings (AMBs): proportional-integral-derivative (PID), single-input and single-output (SISO) fuzzy and the new controller. The assessment was performed using an academic test rig and the results obtained with the new controller show that performances were enhanced with equivalent levels of stability and robustness.

Data-Driven Gradient Descent Direct Adaptive Control for Discrete-Time Nonlinear SISO Systems

A novel data-driven gradient descent (GD) adaptive controller, for discrete-time single-input and single output (SISO) systems, is presented. The controller operates as the least mean squares (LMS) algorithm, applied to a nonlinear system with feedback. Normalization of the learning rate parameter provides robustness of the overall system to modeling errors and environment nonstationarity. Convergence analysis reveals that the controller forces tracking error to zero, with bounded control signal and the controller weights. The experiments on the benchmark systems support the analysis.

Design of Multiloop Controller for Three Tank Process Using Cdm Techniques

In this study the controller forthree tank multi loop system is designed using coefficient Diagram method. Coefficient Diagram Method is one of the polynomial methods in control design. The controller designed by using CDM technique is based on the coefficients of the characteristics polynomial of the closed loop system according to the convenient performance such as equivalent time constant, stability indices and stability limit. Contro ller is designed for the threetank process by using CDM Techniques;the simulation results show that the proposed control strategies have good set point tracking and better responsecapability. Most of the processes industry in power plants, refinery process, aircrafts and chemical industries are multivariable or multi-input multi-output (MIMO) and control of these MIMO processes are more complicated than SISO processes. The strategies are used to design a controller for the S ingle input and single output (SISO) process cannot be applied for Multi input and multi output (MI MO) process because of the more interaction between the two loops. The different methods have been presented in the literature for control (1) of MIMO process. Proportional -integral-derivative (PID) or Proportional-Integral (PI) based controllers are used very commonly to control three tank systems. Generalized synthesis method used to tuning the controller parameters. Generally the two types of control schemes are available to control the MIMO processes. The first control scheme or multiloop control scheme, where single loop controller are used to in the form of diagonal matrix is one.. The second scheme is a (3) multivariable controller known as the centralized controller. Matrix is not a diagonal one. In this paper, the Coefficient Diagram Method (CDM) can be used to design a controller for MIMO process. Now the CDM is well established approach to design a controller, which provides an outstanding time domain characteristics of closed loop systems. Basically the CDM is based on pole assignment, which can locate of the closed-loop system are used to obtained the predetermined values. Although it has been used to demonstrate the design based on CDM. It has some robustness and is possible to see that of the smooth response without oscillation in change in the model of the process exists.

Estimation of the Dynamic Properties of Non-linear Packaging Materials Using a Reverse Multiple Input/Single Output Based Approach

During the distribution phase, packaged consignments are exposed to a variety of environmental hazards (such as vibrations) that, if excessively severe, may cause damage to or even destroy the product. Structural deterioration can be tracked by monitoring variations in the packaging system's modal parameters, particularly its natural frequency (stiffness). Natural frequency estimates are often extracted using a least-squares regression curve fit, applied to an estimate of the system's frequency response function (FRF). FRF estimates are generally obtained using the Fourier transform with a single input and single output (SISO). This approach is suitable for many applications; however, as the non-linearity of the system under analysis increases, the ability of this technique to accurately monitor changes in the system will decrease. In addition, when the excitation to the non-linear system is varied (increased or decreased amplitude), a SISO-based approach may indicate a shift in natural frequency (as a result of the varied input) even though no change in the condition of the system has occurred. This paper discusses an approach that is designed to separate the linear component of the system's FRF using a reverse multiple input/single output (RMISO) algorithm. Such separation will allow traditional modal parameter extraction (curve fitting) techniques to be used to monitor the condition of non-linear systems. The paper presents the results of experiments in which expanded polystyrene samples were subjected to broad-band random base excitation with a free-moving load placed atop the cushion sample. Continuous acceleration measurements of the vibration table and the free-moving load were used to compute the FRFs of the cushions, and the differences between a conventional (SISO) approach and the proposed RMISO-based parameter extraction technique were evaluated. Copyright © 2014 John Wiley & Sons, Ltd.

Adaptive Fuzzy Fault-Tolerant Output Feedback Tracking Control of Uncertain Stochastic Nonlinear Systems with Unknown Time-Delay and Tracking Error Constrained

The problem of tracking error constrained adaptive fuzzy output feedback control is investigated for a class of single-input and single-output (SISO) stochastic nonlinear systems with actuator faults, unknown time-delay, and unmeasured states. The considered faults are modeled as both loss of effectiveness and lock-in-place. The fuzzy logic systems are used to approximate the unknown nonlinear functions, and a fuzzy adaptive observer is designed for estimating the unmeasured states. By transforming the tracking errors into new virtual error variables and based on backstepping recursive design technique, a new fuzzy adaptive output feedback control method is developed. It is shown that all the signals of the resulting closed-loop system are bounded in probability and the tracking error remains an adjustable neighborhood of the origin within the prescribed bounds. The simulation results are provided to show the effectiveness of the proposed approach.

Developing Models of Lithuanian Speech Vowels and Semivowels

Lithuanian vowel and semivowel phoneme modelling framework is proposed. Using this framework, the phoneme signal is described as the output of a linear multiple-input and single-output (MISO) system. The MISO system is a parallel connection of single-input and single-output (SISO) systems whose input impulse amplitudes vary in time. Within this framework two synthesis methods are proposed: harmonic and formant. The synthesized sounds obtained by the harmonic synthesis method are compared with those obtained by the formant method. Application of this modelling framework to all of Lithuanian vowel and semivowel synthesis gives naturally sounding result.

Adaptive output feedback control for a class of nonlinear systems with full-state constraints

This paper studies an adaptive output-feedback control for a class of nonlinear single-input and single-output (SISO) systems with the full-state constraints. A state observer is designed to estimate those unmeasured states. At present, all the results in the output-feedback area ignore the effects of the full-state constraints. The presence of these constraints results in a complicated procedure and the major difficulties in the design. The barrier Lyapunov function (BLF) and a novel design procedure are given to overcome these difficulties. The adaptation law and the controllers are obtained based on the backstepping design procedure. In addition, only one adjustable parameter needs to be updated, and thus, the online computation burden is alleviated. The stability of the closed-loop system is proven by using the Lyapunov theorem. A simulation example is given to verify the effectiveness of the approach.

Ergodic capacity analysis of spatially modulated systems

Spatial Modulation (SM) is a newly introduced wireless transmission scheme, and Space Shift Keying (SSK) is its simplified version. Their capacity performance has not been well examined to date. Starting from basic definitions of information theory, through theoretical analyses and numerical calculations, this paper evaluates the capacity of SM systems in various channels. Our conclusion is that under Rayleigh fading channels, the capacity of the SSK system is limited by the Signal-to-Noise Ratio (SNR) or the number of transmit antennas, whichever one is smaller. From the capacity point of view, the SM system is better than the Single-Input and Single-Output (SISO) system, but worse than the Multiple-Input and Single-Output (MISO) system. The correlation between transmit antennas will degrade the performance of the SSK system. Line-of-Sight (LOS) may cause performance degradation for SSK, but capacity increases for SM.