On-line Estimation Algorithm Research Articles

Nonlinear identification of mooring lines in dynamic operation of floating structures

An adaptive algorithm for on-line estimation of physical coefficients of cables in viscous environment is presented. The procedure is useful for obtaining cable characteristics, which are needed in stability analysis and control system design for moored floating structures. It uses measurements of position and forces from on-board instrumentation. It is also able to track changes in the depth and to test for parameter consistency in order to confer the estimation robustness with respect to dynamic perturbations. It is based on nonlinear solvers, which can cope with transcendental functions of the model structure. The proof of asymptotic convergence is presented. Finally, three basic case studies are analyzed.

Estimation of HIV/AIDS parameters

This paper shows how well-established control system techniques can be introduced to formulate guidelines for clinical testing and monitoring of HIV/AIDS disease and the estimation of HIV/AIDS parameters. It is assumed that the viral load and healthy CD4+T cell in plasma are measured. The objective is to estimate all parameters in the basic three-dimensional HIV/AIDS model. For this purpose, through an analysis of basic system properties, the minimal number of measurement samples for the CD4+T cell and the viral load counts is first obtained. The paper determines then the HIV progression stages when an estimation of all parameters is impossible. Outside these stages, the paper proposes two on-line estimation algorithms for all HIV parameters based on the well-known techniques of adaptive identifiers and adaptive observers. Conditions for parameter convergence are discussed. Simulation results are demonstrated for the parameter estimation using adaptive observers.

Estimating membership in a multicast session

We propose two novel on-line estimation algorithms to determine the size of a dynamic multicast group. We first use a Wiener filter to derive an optimal estimator for the membership size of the session in case the join process is Poisson and the lifetime of participants is distributed exponentially. We next develop the best first-order linear filter from which we derive an estimator that holds for any lifetime distribution. We apply this approach to the case where the lifetime distribution is hyperexponential. Both estimators hold under any traffic regime. Applying both estimators on real traces corresponding to video sessions, we find that both schemes behave well, one of which performs slightly better than the other in some cases. We further provide guidelines on how to tune the parameters involved in both schemes in order to achieve high quality estimation while simultaneously avoiding feedback implosion.

Planar Translational Cable‐Direct‐Driven Robots

AbstractWe present the simulated dynamics and control of a planar, translational cable‐directdriven robot (CDDR). The motivation behind this work is to improve the serious cable interference problem with existing CDDRs and to avoid configurations where negative cable tensions are required to exert general forces and moments on the environment and during dynamic motions. Generally for CDDRs the commanded rotations are more demanding than commanded translations in terms of slack cable conditions. Therefore we propose a translational CDDR whose end‐effector may be fitted with a traditional serial wrist mechanism to provide rotational freedom (assuming proper design to resist the moments). Only the translational CDDR is considered in this article, including kinematics and statics modeling, statics workspace (wherein all possible Cartesian forces and moments may be exerted with only positive cable tensions), plus a dynamics model and simulated control for planar CDDRs. Here we focus only on planar CDDRs, to clearly demonstrate our dynamics and control work; we will extend this work to spatial CDDRs in the future. Examples are presented to demonstrate simulated control including feedback linearization of the four‐cable CDDR (with one degree of actuation redundancy) performing a Cartesian task. An on‐line dynamic minimum torque estimation algorithm is introduced to ensure all cable tensions remain positive for all motion; otherwise slack cables can result from CDDR dynamics and control is lost. © 2003 Wiley Periodicals, Inc.

Translational Planar Cable-Direct-Driven Robots

A planar cable-direct-driven robot (CDDR) architecture is introduced with only translational freedoms. The motivation behind this work is to improve the serious cable interference problem with existing CDDRs and to avoid configurations where negative cable tensions are required to exert general forces on the environment and during dynamic motions. These problems generally arise for rotational CDDR motions. Thus, we propose a class of purely translational CDDRs; of course, these are not general but may only perform tasks where no rotational motion or resistance of moments is required at the end-effector. This article includes kinematics and statics modeling, determination of the statics workspace (the space wherein all possible Cartesian forces may be exerted with only positive cable tensions), plus a dynamics model and simulated control for planar translational CDDRs. Examples are presented to demonstrate simulated control including feedback linearization of the 4-cable CDDR (with two degrees of actuation redundancy) performing a Cartesian task. We introduce an on-line dynamic minimum torque estimation algorithm to ensure all cable tensions remain positive for all motion; otherwise slack cables result from the CDDR dynamics and control is lost.

ESTIMATION OF HIV/AIDS PARAMETERS

This paper shows how well-established control system techniques can be introduced to formulate guidelines for clinical testing and measurement of the HIV/AIDS disease for the estimation of HIV/AIDS parameters. It is assumed that the viral load and CD4+ T cell count in plasma blood are measured. The objective is to estimate all parameters in the basic three dimensional HIV/AIDS model. For this purpose, through an observability analysis, the minimal number of measurement samples for the CD4+ T cell and the viral load counts is first obtained. The paper determines then the HIV progression stages when an estimation of all parameters is impossible. Outside these stages, the paper proposes two on-line estimation algorithms for all HIV parameters based on the well-known techniques of adaptive identifers and adaptive observers. Conditions for parameter convergence are discussed. Simulation results are demonstrated for the parameter estimation using adaptive observers.

Adaptive predictive control of combustor NO x emissions

The design and implementation for the active control of oxides of nitrogen (NO x ) emissions from gas turbine combustors is considered in this paper. Predictive control techniques with both fixed and adaptive parameters are employed. An on-line parameter estimation algorithm provides good modelling of the nonlinear characteristics of the combustor NO x process. To obtain a comparison with other control techniques, an optimal PID controller is detailed. The actuator that controls the gas flow to the combustor is an electromagnetic shaker. Gain-scheduling control is applied to provide accurate position tracking of the shaker whose characteristics vary between different operating points. These control strategies are implemented using the SIMULINK/dSPACE controller development environment. Their performance is evaluated on an atmospheric test rig fitted with a commercial combustor.

Adaptive Time Delay Controller

A time delay controller is proposed in this paper. The controller uses the delay element in positive feedback with a feed-forward reference model. The proposed controller posses attractive properties such as requiring only one tuning parameter. Furthermore, it can be applied to higher-order systems as well as systems with dominant delay. The time delay controller gives better performance and smooth control action as compared with classical PID controller. The basic structure is integrated with an on-line estimation algorithm to form an Adaptive Time Delay Controller (ATDC). Simulation and experimental studies are included in the paper to demonstrate the performance of the proposed adaptive time delay controller.

Online estimation of the stator parameters in an induction motor using only voltage and current measurements

This paper proposes a set of algorithms for stator parameter online estimation (stator resistance, stator inductance, and leakage inductance) of an induction motor using neither the rotor speed nor the rotor resistance. The estimation procedure is only based on stator current and voltage measurements. The algorithm performance is tested using simulation and experimental data from a commercial 15 kW induction motor.

Adaptive time-delay controller

A time-delay controller is proposed in this letter. This controller possesses attractive properties, such as requiring only one tuning parameter. Furthermore, it can be applied to higher order systems as well as systems with dominant delay. The basic structure is integrated with an online estimation algorithm to form an adaptive time-delay controller. An experimental study is included to demonstrate its performance and merits compared with two similar controllers.

Online estimation of hidden Markov models

We present a novel and simple online estimation algorithm for hidden Markov models, with memory requirements independent of the data length. The transition matrices and the state distribution are obtained at any instant as contractions of tensorial quantities, which are iteratively reestimated.

Adaptive Predictive Control of Combustor NOx Emissions

The design and implementation of an adaptive predictive controller for oxides of nitrogen (NOx) emissions from gas turbine combustors is considered in this paper. Predictive control techniques with both fixed and adaptive parameters are introduced. An on-line parameter estimation algorithm is used to model the nonlinear characteristics of die combustor NOx process. The predictive control strategies are implemented using the MATLAB/dSPACE controller development environment. Their performance is evaluated on an atmospheric test rig fitted with a commercial combustor and also compared with a PID controller.

Detect and Diagnose Unexpected Changes in the Output Probability Density Functions for Dynamic Stochastic Systems: An Identification Based Approach

In parallel to the recently developed observer based fault detection algorithm (Wang, 1998), this paper presents an identification based fault detection and diagnosis approach for the output probability density function for unknown time-invariant stochastic systems. The fault is assumed to be the unexpected changes of the system physical parameters which affect the shape of the output probability density functions of the system. In this context, the measured control input directly affects the distribution of the system output in some probability sense. An on-line parameter estimation algorithm is constructed using the measured output probability density functions of the system. Based upon the same type of dynamic model proposed by Wang (1997, 1998), a functional weighted integration of the output probability density function is employed in the evaluation of the residual signals for both fault detection and diagnosis purposes. An applicability study of the proposedalgorithm to the detection of the unexpected changes of the solid flocculation in paper making is included, where a simulated example is employed to illustrate the use of the developed algorithm and encourage results have been obtained.

Designs of Self-tuning Control Systems for Ships

This paper treats of the applications of the pole assignment self-tuning control theory to the identification and design of ship's auto-pilot systems. An on-line estimation algorithm known as the Recursive Least Squares Algorithm is used to identify parameters of the control system. Two typical types of the pole assignment self-tuning control algorithms that can be linked to the on-line estimation algorithm to form self-tuning control systems for ships are introduced. The authors discuss how to choose the control parameters by the rules of thumb in the actual sea trials. The pole assignment self-tuning controllers designed to steer ships are verified by experiments aboard the training ship Shioji Maru of Tokyo University of Mercantile Marine. The aboard experiments result in the confirmation that it is possible to keep and to change the ship's course as desired by the designed controllers.

Cross-coupled fuzzy logic control for multiaxis machine tools

This paper presents a design and implementation case study that focuses on contour control of a biaxial CNC machine tools. Since, it is difficult to obtain an accurate nonlinear mathematical model of cross-coupled multiaxis machine tools, here we investigate an alternative to conventional approaches where we employ crosscoupled fuzzy logic controllers for improving the contouring accuracy of multiaxis CNC machine tools. A new fuzzy rule-generated method which is based on a performance index of the contour error obtained from an on-line estimation algorithm is proposed. An adapted output scale factor is adopted to improve the system performance. Experimental results have shown that the desired contouring accuracy can be achieved, and the proposed approach outperforms over uncoupled approaches. In conventional control, increasing contour feedrate for productivity may result in larger contour errors. However, the experimental results have shown that the performance of the proposed approach is still quite good with increasing contour feedrate.

Decomposition algorithms for on-line estimation with nonlinear DAE models

In a previous paper we presented an algorithm for solving the large nonlinear problems that arise in dynamic state and parameter estimation. These problems are described by a set of Ordinary Differential Equations (ODE) subject to initial values, discretized and solved under a simultaneous solution strategy within an Error-in-all-Variable-Measurements (EVM) framework. In this paper, we extend this work to systems described by Differential and Algebraic Equations (DAE), for both initial value (IVP) and boundary value (BVP) problems. The resulting discretized NLP problems are usually large both in the number of variables and the number of degrees-of-freedom, and the size of the problem grows linearly with the number of data samples or the number of discretizations, making them difficult to solve with general purpose NLP solvers. We discretize the DAE model with an implicit Runge-Kutta scheme (IRK) so that higher index and stiff problems can be solved, and use the SQP method to solve the resulting NLP. Under the assumption that the measurement errors are independently distributed, the optimality conditions for each data set at the QP subproblem level are decoupled so that the first order conditions in the state variables, input variables and the stage derivatives can be solved recursively and expressed as functions of the optimality conditions in the parameters, thus reducing the size of the QP subproblem and turning the solution effort linear with both the number of sample points and the number of discretization points. We use two decoupling procedures depending on whether the DAE system is expressed as an IVP or a BVP. For the IVP we extend the technique used in our previous paper and use an affine transform which expresses the directions in the variables and the multipliers as functions of the steps in the parameters. For the BVP we use a selected variable elimination procedure so that the optimality conditions in the state and input variables, stage derivatives and multipliers of the QP subproblem will be transformed into an almost block diagonal system of linear equations. We then solve this system using SOLVEBLOCK, a solution method linear with the number of blocks to reduce the size of the QP subproblem to the dimension of the parameters. As seen in our examples, these approaches are therefore much faster than general purpose NLP solvers. In this paper we also present a case where an IVP is unstable and difficult to solve, but stable and well behaved when rewritten as a BVP.

On-line Structure Detection and Parameter Estimation with Exponential Windowing for Nonlinear Systems

A new recursive orthogonal estimation algorithm is derived which updates both the model structure and the parameters of nonlinear models on-line. Techniques developed for on-line modification of the structure of linear models are not generally suitable when nonlinear systems are considered, because the vector-shift properties no longer hold for nonlinear models and the demands of real-time processing on data storage make on-line structure detection much more difficult. In the present study a new on-line orthogonal estimation algorithm based on the polynomial NARMAX model is derived by extending the family of orthogonal QR decomposition algorithms to include on-line model structure selection. The new algorithm which includes exponential data windowing based on a stable Givens routine minimises the loss function at every selection step by selecting significant regression variables, computes the parameter estimates and maintains the orthogonality of the vector space for continuous computation. Simulated examples are included to demonstrate the performance of the new algorithm.

Autoregressive and adaptive estimation with an application to hurricane track prediction

The key result of this paper is that following a change in a parameter of AR (p), an autoregressive process of order p, the innovations sequence of the Kalman filter parameters will follow an autoregressive moving-average model in addition to a transient function. Furthermore, it is also shown that the first p values of the innovations' sample autocorrelation can be used to form a sufficient statistic to detect if at least one of the parameters in the AR (p) model did change at an unknown point in time. Following a parameter change detection process, improved estimates and noise statistics can be determined and implemented to modify the Kalman filter. The revised model will thus be more consistent with the most recent process behaviour. To motivate the reader, a simulation exercise was conducted to validate the on-line change detector and adaptive estimation algorithm. The proposed algorithm was used to predict hurricane movements with real data provided by the National Hurricane Center.

Decomposition algorithms for on-line estimation with nonlinear models

Dynamic state and parameter estimation for nonlinear systems usually leads to large nonlinear problems when the governing differential constraints are discretized under a simultaneous solution strategy, as the optimality conditions for each data set are coupled to the conditions of other data sets. This NUP problem grows not only in the number of variables, but also in the number of degrees of freedom if an error in all variables method (EVM) approach is used. In this paper wc discretize the set of ODEs using a one-step integration method, and use the SQP method to solve the resulting NLP. The optimality conditions for each data set at the OP subproblem level are decoupled using an affine transform, so that the first-order conditions in the state and input variables can be solved recursively and expressed as functions of the optimality conditions in the parameters, thus reducing the size of the problem and turning the effort of solving it linear with the number of data sets As seen in our examples, this approach is therefore over tv/o orders of magnitude faster than general purpose N LP solvers. This technique can also be viewed as an extension of a decomposition strategy that has been successfully applied to optimal control problems.

Review of the damped least-squares inverse kinematics with experiments on an industrial robot manipulator

The goal of this paper is to present experimental results on the implementation of the damped least-squares method for the six-joint ABB IRb2000 industrial robot manipulator. A number of inverse kinematics schemes are reviewed which allow robot control through kinematic singularities. The basic scheme adopts a damped least-squares inverse of the manipulator Jacobian with a varying damping factor acting in the neighborhood of singularities. The effect of a weighted damped least-squares solution is investigated to provide user-defined accuracy capabilities along prescribed end-effector space directions. An online estimation algorithm is employed to measure closeness to singular configurations. A feedback correction error term is introduced to ensure algorithm tracking convergence and its effect on the joint velocity solution is discussed. Computational aspects are discussed in view of real-time implementation of the proposed schemes. Experimental case studies are developed to investigate manipulator performance in the case of critical end-effector trajectories passing through and near the shoulder and wrist singularities of the structure.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>