A self-tuning regulator for multivariable systems

The controller described in this paper is designed for multivariable plants with constant, unknown parameters. The algorithm operates on-line with the a priori information about the time delay. The order of the system may be given a priori. In the case where the order of the system is unknown it can be determined by a generalized likelihood-ratio statistical test which is described in this paper. The multivariable self tuning regulator consists of the two tasks of estimation and regulation. Estimation of the input-output system model parameters is based on the least-squares principle. The control is computed to minimize the combined cost of output deviation and control energy. Asymptotic properties of the estimation are discussed. Usefulness and simplicity of this approach are illustrated by examples.     

A self-tuning regulator for distributed parameter systems

The control of distributed parameter systems with constant, but unknown parameters is considered. A weighted average of the distributed output on the spatial domain is defined as a new variable and is used to generate the control. The parameters of the model are estimated using recursive least squares estimation. The control is obtained using a minimum variance strategy based on the estimated parameters. Distributed disturbances and measurement noise are allowed to be present. Measurements at a finite number of points in the spatial domain are used in obtaining a discrete-time model. From the simulation of a one-sided heating diffusion process the self-tuning regulator is shown to have attractive characteristics and hence can be recommended for practical on-line control of distributed parameter systems.     

Modeling and self-tuning pressure regulator design for pneumatic-pressure–load systems

This paper presents a dynamic model and a design method for an accurate self-tuning pressure regulator for pneumatic-pressure–load systems that have some special characteristics such as being nonlinear and time-varying. A mathematical model is derived, which consists of a chamber continuity equation, an orifice flow equation and a force balance equation of the spool. Based on a theoretical analysis of the system dynamics, a three-order controlled auto-regressive moving average (CARMA) model is used to describe the practical pressure–load systems. Then a linear quadratic Gaussian self-tuning pressure regulator is designed to realize an adaptive control of pressure in the chamber. Because the system parameters are time-varying and the system states are difficult to detect, the recursive forgetting factor least-squares algorithm and the Kalman filtering method are adopted to estimate the system parameters and the system states. Experimental results show that the proposed self-tuning pressure regulator can be adapted to parameters which vary with such factors as the volume of the chamber and the setting pressure and that better dynamic and static performances can be obtained.     

Simple method for the design of Lyapunov functionals in distributed-parameter systems

The properties of Lyapunov functionals for systems of partial differential equations are expressed in local form. When written as an identity, the Lagrange multiplier forms the basis of a design method. This is applied to linear and non-linear examples. In the latter case, results are obtained more simply than previously. For ordinary differential equations, the method reduces to a standard one.     

A self-tuning regulator based on optimal output feedback theory

Self-tuning control of stochastic systems is considered. The underlying control problem is a parametric linear quadratic problem for fixed structure controllers. An explicit self-tuning regulator is described based on optimal output feedback theory. The proposed self-tuner is a generalization of state-space LQG self-tuners to parametric LQ problems. Two interesting application areas of parametric LQ self-tuners are autotuning of parameters of low-order regulators, such as PID-regulators, and adaptive decentralized control.     

Optimal scanning measurement problem for a stochastic distributed-parameter system

This paper deals with the problem of determining the optimal measurement scheduling for a stochastic distributed-parameter system (DPS) based on spatially continuous and discrete-scanning observations. These two types of measurement are realized by the optimal motion of spatially-movable sensors and the optimal selection of measurement data from spatially-fixed sensors, respectively. For the continuous scanning case, the existence of optimal solutions for the problem is proved and the N-modal approximation problem is established. For the discrete case, however, it is impossible to derive the existence conditions. Therefore, it is shown that there exist optimal relaxed solutions by introducing the relaxed control theory. A practical method for constructing an approximate solution for the relaxed problem is proposed. Necessary conditions for approximate-optimality are represented in the form of a matrix minimum principle, and a feasible algorithm is developed to determine the approximate solution. A numerical example is considered and the present two types of optimal measurement trajectory are compared.     

A reasoning method for a ship design expert system

Abstract: The ship design process is a highly data‐oriented, dynamic, iterative and multi‐stage algorithm. It utilizes multiple abstraction levels and concurrent engineering techniques. Specialized techniques for knowledge acquisition, knowledge representation and reasoning must be developed to solve these problems for a ship design expert system. Consequently, very few attempts have been made to model the ship design process using an expert system approach. The current work investigates a knowledge representation–reasoning technique for such a purpose. A knowledge‐based conceptual design was developed by utilizing a prototype approach and hierarchical decompositioning. An expert system program called ALDES (accommodation layout design expert system) was developed by using the CLIPS expert system shell and an object‐oriented user interface. The reasoning and knowledge representation methods of ALDES are explained in the paper. An application of the method is given for the general arrangement design of a containership.     

Comments on self-tuning regulator applied to a binary distillation column

Some comments are made on a paper describing an application of self-tuning control in the presence of step disturbances. It is demonstrated that the behaviour of the self-tuning regulator observed in the paper is a consequence of the slow convergence of the regulator parameters. By an increase of the convergence rate quite different results can be obtained. For example, no steady state error in the output is observed, contrary to the results in the paper in question.     

A new design method for regulator problems for singularly perturbedsystems with constrained control

A new design method for regulator problems for singularly perturbed systems is proposed in this paper. In contrast to previous papers using the Riccati equation approach and composite controls, a direct control without any Riccati equation is presented. It is applicable to solving a regulator problem for time invariant and time-varying systems with unconstrained controls as well as with constrained controls. The approach is based on a version of the Tikhonov theorem in L₂ for solutions of singularly perturbed systems to be proved in the paper and some techniques of sensitivity analysis for optimization with strictly convex objective functions     

A multivariable generalized self-tuning controller with decoupling design

This note describes a multivariable generalized self-tuning controller with decoupling which combines the classical pole-zero placement strategy with the optimal strategy for self-tuning control. The controller uses techniques of updating weighting polynomial matrices on line and eliminating the tracking errors without use of an integrator, and decouples the multivariable system.     

Robust implicit self-tuning regulator: Convergence and stability

A family of implicit self-tuning regulators (STR) is presented, based on Lyapunov analysis techniques for the control of a class of multi-input and multi-output (MIMO) dynamical systems. Linearity in the parameters is assumed to hold, but the ‘estimation error’ is considered to be nonzero; this allows control of a larger class of systems and also has the effect of producing a robust controller. Moreover, the certainty equivalence (CE) principle is not used in the STR design, overcoming a major problem in adaptive control. The structure of the STR is naturally derived using a tracking error/passivity approach. The role of persistency of excitation (PE) is explored, and is used to show the boundedness of parameter estimates when gradient-based parameter tuning of STR is performed in nonideal conditions. New on-line tuning algorithms for implicit STR are derived that are similar to the -modification approach for the case of continuous-time systems and that include a modification to the adaptation gain and a correction term to the standard gradient-based tuning. These improved parameter tuning algorithms guarantee tracking as well as bounded parameter estimates in nonideal situations, so that PE is not needed. The notions of a passive STR, a dissipative STR and a robust STR are introduced. Finally, this paper provides a comprehensive theory in the development of identification, prediction and adaptive control schemes for discrete-time systems based on Lyapunov analysis.     

LQ regulator design method for system with delay based on spectral decomposition of the hamiltonian

A design method to construct an LQ regulator for a system with time-delay is proposed. It is based on a new technique for composing the solution of an infinite-dimensional Riccati equation using the spectral decomposition of the hamiltonian. This design method has the feature that the closed-loop poles can be calculated a priori from the open-loop poles and one scalar design parameter, so that we can choose the parameter to obtain an optimal control law which results in the desired degree of exponential stability of the closed-loop system.     

Tide-tree: A self-tuning indexing scheme for hybrid storage system

Main memory index is built with the assumption that the RAM is sufficiently large to hold data. Due to the volatility and high unit price of main memory, indices under secondary memory such as SSD and HDD are widely used. However, the I/O operation with main memory is still the bottleneck for query efficiency. In this paper, we propose a self-tuning indexing scheme called Tide-tree for RAM/Disk-based hybrid storage system. Tide-tree aims to overcome the obstacles main memory and disk-based indices face, and performs like the tide to achieve a double-win in space and performance, which is self-adaptive with respect to the running environment. Particularly, Tide-tree delaminates the tree structure adaptively with high efficiency based on storage sense, and applies an effective self-tuning algorithm to dynamically load various nodes into main memory. We employ memory mapping technology to solve the persistent problem of main memory index, and improves the efficiency of data synchronism and pointer translation. To further enhance the independence of Tide-tree, we employ the index head and the level address table to manage the whole index. With the index head, three efficient operations are proposed, namely index rebuild, index load and range search. We have conducted extensive experiments to compare the Tide-tree with several state-of-the-art indices, and the results have validated the high efficiency, reusability and stability of Tide-tree.     

A self-tuning adaptive trend extraction method for process monitoring and diagnosis

Trend analysis is an efficient tool for process monitoring and diagnosis. However, the performances of a trend-based diagnosis system depend on the reliability of the trends extracted from the signals. One challenge in trend analysis is to design algorithms able to adapt themselves to the varying conditions of background noise and artefacts occurring non-deterministically on a same signal. Moreover, while long term trends such as decreasing/increasing have been extensively studied other subtle changes such as slow drifts and step-like transients have received little attention. In this paper, an adaptive on-line trend-extraction method is presented. It extends a former algorithm based on a linear segmentation to filter the signal and extract trends. In this version, the tuning parameters are not set to a fixed value for a given signal but can self-adapt on-line according to an estimation of the noise variance. An increasing or decreasing trend is detected if the variations on the signal are significantly higher than the level of the background noise. An initialisation phase is proposed to automatically set the initial values of the parameters, making the algorithm a self-tuned algorithm with minimal user intervention.The method was evaluated on a set of simulated data with various levels of background noise. It was also applied on real physiological data recorded from babies hospitalised in a Neonate Intensive Care Unit. It showed improved performances compared to the non adaptive algorithm, whatever the level of noise corrupting the data.     

Parametric state feedback design of linear distributed-parameter systems

The parametric approach for the design of state feedback controllers has been formulated so far only for linear lumped-parameter systems. It yields an explicit parametric expression for the state feedback gain given the closed-loop eigenvalues and the set of corresponding parameter vectors. This contribution presents a parameterisation of state feedback controllers for linear distributed-parameter systems with scalar state and distributed control. By introducing the closed-loop eigenvalues and the parameter vectors as design parameters, an explicit expression for the state feedback is obtained. In contrast to the pure eigenvalue assignment, the parameterisation allows the assignment not only of the closed-loop eigenvalues but also of the closed-loop eigenfunctions. The usefulness of the proposed parametric approach is demonstrated by decoupling the transfer behaviour of a MIMO diffusion system with respect to its dominant modes.     

Stability of finite element models for distributed-parameter optimization and topology design

We address a problem of numerical instability that is often encountered in finite element solutions of distributed-parameter optimization and variable-topology shape design problems. We show that the cause of this problem is numerical rather than physical in nature. We consider a two-field, distributed-parameter optimization problem involving a design field and a response field, and show that the optimization problem corresponds to a mixed variational problem. An improper selection of the discrete function spaces for these two fields leads to grid-scale anomalies in the numerical solutions to optimization problems, similar to those that are sometimes encountered in mixed formulations of the Stokes problem. We present a theoretical framework to explain the cause of these anomalies and present stability conditions for discrete models. The general theoretical framework is specialized to analyze the stability of specific optimization problems, and stability results for various mixed finite element models are presented. We propose patch tests that are useful in identifying unstable elements.     

阵风环境模拟用控制器设计与实验研究

针对环境模拟实验室风环境模拟的阵风特性,设计开发了一种迭代学习自校正PID控制器。该控制器将设定值序列进行迭代学习优化,系统内部采用PID自校正控制策略。仿真和实验结果表明,两种控制方式的结合,可改善系统设定点改变时的动态特性,实现了对期望风环境参数的有效控制。迭代学习控制的作用下,特别是对正弦风的模拟,随着时间推移实际值可跟踪定值,保证了环境模拟的控制精度。