Adaptive Control Methodology Research Articles

Adaptive predictive congestion control of high-speed ATM networks

This paper proposes an auto regressive moving average (ARMAX)-based adaptive control methodology to prevent congestion in high-speed asynchronous transfer mode (ATM) networks. An adaptive controller is developed to control traffic where sources adjust their transmission rates in response to the feedback information from the network switches. Specifically, the buffer dynamics at a given switch is modeled as a nonlinear discrete-time system and an ARMAX controller is designed so as to predict the explicit values of the transmission rates of the sources so as to prevent congestion. Tuning methods are provided for the unknown coefficients of the ARMAX model to estimate the unpredictable and statistically fluctuating network traffic. Mathematical analysis is given to demonstrate the stability of the closed-loop system so that a desired quality of service (QoS) can be guaranteed. The QoS is defined in terms of cell loss ratio (CLR), transmission delay and buffer utilization. We derive design rules mathematically for selecting the parameters of the ARMAX algorithm such that the desired performance is guaranteed during congestion and potential tradeoffs are shown. Simulation results are provided to justify the theoretical conclusions for multiple source/single switch scenarios using both ON/OFF and MPEG data. The performance of the proposed congestion control scheme is also evaluated in the presence of feedback delays for robustness considerations.

An adaptive control scheme for biomass-based diesel–wind system

Control of autonomous diesel–wind systems is difficult because of time-varying dynamical properties, most of them as a result of plant non-stationary, non-linearity and random disturbances. In this paper, the system is composed of a pitch controlled wind turbine with an induction generator connected to an ac bus bar in parallel with a diesel generator set having a synchronous generator. A power plant can generate electric power using biomass from the olive tree in Spain. To have optimal response under disturbances, adaptive schemes appear to be a particularly attractive choice. The recursive least squares method and minimum-variance control algorithm is implemented to design the controller. This paper considers the application of an adaptive control methodology that allows the controller to automatically adjust to changing process variables and thereby provide uniform response over a wide range of operating conditions.

ADAPTIVE NARROW BAND DISTURBANCE REJECTION IN ACTIVE VIBRATION CONTROL

This paper presents a methodology for feedback adaptive control of active vibration attenuation systems in the presence of unknown narrow band disturbances. The proposed methodology consists in two algorithms based on the Internal Model Principle. The first one deals with an indirect adaptive control, the second one with a direct adaptive control. The feasibility of the two algorithms is illustrated in real-time on an active suspension system.

APPLICATION OF NEURO-FUZZY IDENTIFIER IN THE PREDICTIVE CONTROL OF A POWER PLANT

An adaptive predictive control methodology is applied for a fossil fuel boiler control. The control algorithm takes advantage of a neuro-fuzzy identifier system for prediction of the boiler response in a future time window. An optimizer algorithm based on evolutionary programming technique (EP) uses the identifier-predicted outputs and determines input sequence in a time window. The present optimized input is applied to the plant, and the prediction time window shifts for another phase of plant output and input estimation. The neuro-fuzzy identifier is trained to provide a good estimation of boiler outputs. Neuro-fuzzy rules and membership parameters are trained based on the data log, applying genetic algorithm and back-propagation, respectively. The obtained intelligent control system is highly structural and applicable on different boiler systems.

Predictive congestion control of ATM networks: multiple sources/single buffer scenario

This paper proposes a neural network (NN)-based adaptive control methodology to prevent congestion in high-speed asynchronous transfer mode (ATM) networks. The buffer dynamics at the switch is modeled as a nonlinear discrete-time system and a NN-based predictive controller is designed to predict the explicit values of the transmission rates of the sources so as to prevent congestion. Tuning methods are provided for the NN weights to estimate the unpredictable and statistically fluctuating network traffic. Mathematical analysis is given to demonstrate the stability of the closed-loop system so that a desired quality of service (QoS) can be guaranteed. The QoS is defined in terms of cell loss ratio (CLR) and latency. We derive design rules mathematically for selecting the NN tuning algorithm such that the desired performance is guaranteed during congestion and potential tradeoffs are shown. Simulation results are provided to justify the theoretical conclusions for single source/single switch scenario using ON/OFF data. Finally, comparison studies are also included to show the effectiveness of the proposed method over conventional rate-based and thresholding techniques during simulated congestion.

Comparing the adaptive control methodology (ACM) to the financial planning practice of a large international group

The present paper is a contribution to the validation of the newly developed methodological framework called “adaptive control methodology” (ACM) introduced in a previous paper. ACM is a tool for planning and on-line control of complex socio-economic processes. It combines the techniques of adaptive control theory with system dynamics (SD) and group multicriteria decision aid (GMCDA). In the following, the usefulness and practical use of this framework are set in perspective by comparing it with recurrent planning and control of financial processes in the BSN/DANONE group. As a result of this comparison, some lessons drawn on the way of practical implementation of the theoretical framework can be eased. As by-product recommendations are made on how to influence in a constructive way common planning processes in the real world.

Improved adaptive model control

The Adaptive Model Control methodology is based on obtaining the model of a plant adaptively by using a FIR (Finite Impulse Response) filter, and inverting this model algebraically for use as the controller. This will practically cancel the plant dynamics when connected in cascade with the plant. The Adaptive Inverse Control methodology has the same aim but a different means for obtaining the plant inverse; the inverse is obtained by a second adaptive process. Adaptive Inverse Control systems have been studied with the “disturbance cancelation” and the “reference model” features added, while the algebraically inverting Adaptive Model Control systems could also support the same advanced features with a simpler and faster adapting structure. In this work, a scheme for adding the above features to an adaptive Model Control system is proposed, and using the A-4D aircraft as a test platform in simulations, the performance of this system is evaluated and compared to that of the Adaptive Inverse Control under the same operating conditions.

Decentralised control of chemical reactors based on delta model representation - a case study

The paper presents a methodology for adaptive control of multi input - multi output (MIMO) continuous-time industrial processes. The external model of a controlled plant is considered in a delta model representation. The proposed control strategy consists of local and global control loops. The local controllers follow from the decentralization of the MIMO system and they are obtained via Diophantine equations in delta polynomials as a pole-placement problem. The global control strategy solves the interconnections of the entire control system. The proposed strategy is applied to control of two serially connected chemical reactors with a recycle in an adaptive form.

A structured adaptive supervisory control methodology for modeling the control of a discrete event manufacturing system

Two basic measures, model complexity and model construction efficiency, are usually used to evaluate the implementability of a methodology for modeling the control of a discrete event manufacturing system (DEMS) on the shop floor. Many well-recognized methods are used to represent and analyze the dynamics of DEMs, but not many relevant applications have been found in developing control software for the shop floor due to their shortcomings in satisfying these two measures. The paper explores a methodology for modeling the control of a DEMS, which leads to ease of control software development, rather than a new representational/analytical tool, by significantly reducing the model complexity (in terms of the number of required control states) and improving the model construction efficiency. First, an extended finite machine, called a deterministic finite capacity machine (DFCM) with parallel computing capability is developed. Based on DFCMs, the complexity growth function of a DEMS control model is linear in the number of synthesized control components. Then, an automaton structure of a DFCM control model, called structured adaptive supervisory control (SASC), is developed. By referring to supervisory control theory, an SASC model is created with three function layers: acceptance, adaptive supervision, and execution. The well-defined structure ensures that the control model can be constructed systematically.

Adaptive control using multiple models

Intelligent control may be viewed as the ability of a controller to operate in multiple environments by recognizing which environment is currently in existence and servicing it appropriately. An important prerequisite for an intelligent controller is the ability to adapt rapidly to any unknown but constant operating environment. This paper presents a general methodology for such adaptive control using multiple models, switching, and tuning. The approach was first introduced by Narendra et al. (1992) for improving the transient response of adaptive systems in a stable fashion. This paper proposes different switching and tuning schemes for adaptive control which combine fixed and adaptive models in novel ways. The principal mathematical results are the proofs of stability when these different schemes are used in the context of model reference control of an unknown linear time-invariant system. A variety of simulation results are presented to demonstrate the efficacy of the proposed methods.

Adaptive speed control of hydrogenerators by recursive least squares identification algorithm

An inherent challenge to hydrogenerator speed regulation is the nonlinear and time varying nature of the process. When a fixed gain controller is used to regulate such a process, globally stable system response can be achieved only at the expense of degraded off design-point performance. This paper considers the application of an adaptive control methodology that allows the controller gains to automatically adjust to changing process variables and thereby provide uniform closed loop response over a wide range of operating conditions. Preliminary findings derived from a calculated plant model, and a standard recursive identification technique, indicate that the adaptive system provides desirable dynamic response despite changes in system operating dynamics, maintaining stable operation in situations where constant gain schedules fail.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Microbial specific growth rate control via MRAC method

Abstract A problematic feature in most fermentation processes is that on-line measurement of the most important biological process variables, the concentration of the bio-mass and its specific growth rate, cannot be directly measured. An approach for estimating these biological variables and for controlling the specific growth rate in a continuous flow bioprocess of a stirred tank reactor is presented and analysed. The goal in the control is to get the specific growth rate to track the rate of a given reference model, A dynamic model for the specific growth rate is obtained from the general growth and substrate consumption model. The structure of the rate model is then applied in the reference model structure. The methodology studied is based on two issues, joint state and parameter estimation and direct adaptive control. From the theoretical viewpoint, this forms a combination of indirect and direct adaptive control. The structure of the control law is justified by the structure of the law obtained for known fixed parameters. Convergence of the estimation scheme and of the adaptive control scheme are demonstrated via Lyapunov's method, and separately. The choice of the appropriate Lyapunov function results in a Pl-type controller with the gains as the parameters to be adjusted by the user. A simulation study based on Monod's model and time-varying model parameters and subject to measurement noise shows the feasibility and robustness of the adaptive control methodology developed for this non-linear process.

多重モデルとモデル選定機構をもつ適応制御系とその応用

An adaptive control methodology using multiple identification models is presented. In the proposed method, the real process is identified simultaneously by multiple models with different structure and an adaptive control is carried out based on the optimum model selected by some rules. The selection rules of the suitable model for an operating condition are decided by theoretical and heuristic knowledge about the parameter estimation and adaptive control. This method requires few a priori information about the process and can deal with the abrupt change of the process characteristics. In this paper, first, the principle of the adaptive control system with multiple models is presented. Next, the main parts of the proposd system: model following control loop, recursive identifier with multiple models, model selecting function block and higher level adaptation mechanism are discussed. In order to investigate the effectiveness of the proposed method, computer simulations are carried out for processes with time-varying characteristics. Finally, the proposed method can be successfully applied to the temperature control of an incinerator.

Direct adaptive cutting force control of milling processes

Adaptive control of peak cutting force of a milling system with saturation nonlinearities is addressed. It is shown theoretically and demonstrated through experimental results that, while most adaptive control methodologies in the literature may not be applicable for such a system due to the presence of the nonlinearities, a modified version of an existing method continues to accomplish the control task.

On a Discrete Time Adaptive Control System with Multiple Models and its Selecting Functions

A discrete time adaptive control methodology using multiple models representing a real system in different operating conditions is presented. In the proposed method, the real plant is identified simultaneously by several models with different structure and an adoptive control is carried out using a controller designed by a certain model selected by some rules. The selection rules of the suitable model for an operating condition are decided by theoretical and experiential knowledge about the parameter estimation and adaptive control. This method requires few a priori information about the controlled plant. Moreover, this method can be applied for stable or unstable, and minimum phase or non-minimum phase plants with variable characteristics depending the operating conditions. From the practical point of view, this control algorithm is characterized by a parallel structure, and it can easily implemented by multi microprocessor systems.

Simple adaptive control of uncertain systems

AbstractThis paper considers adaptive control of various systems with unknown time‐varying or non‐linear dynamics in an attempt to show that practical adaptive control can be simple and robust. The simplified adaptive control methodology used is based on the observation that most real‐world systems, even non‐stationary or non‐linear, can be stabilized by simple controller configurations. More complex controllers may be needed only in order to improve the performance of the control systems. In the adaptive scheme, the stabilizability properties of the plants are used only to satisfy the necessary conditions that guarantee robustness of simple adaptive controllers. The desired performance is then imposed upon the plant by these adaptive controllers, which force the plant to follow the input‐output behaviour of low‐order reference models. Examples from the recent literature of control with uncertainty are used to illustrate the power of these adaptive techniques.

A Simulation Study of Singular Value Decomposition for Controlled Plant Identification

A methodology is presented for the adaptive control of a single-input single-output system using the numerically stable method of singular value decomposition (SVD) for identification. This methodology is suitable for industrial applications where the plant parameters change slowly compared to the plant dynamics. A procedure is included for automatically identifying situations where the results of the identification are questionable. This procedure is based on three indices which are used to evaluate the identification. Expressions for computing these indices from the output of the SVD are derived. The identification procedure is numerically stable but computationally expensive. The computation time for identification may exceed the largest suitable sampling interval for some applications. To accommodate the long computation time, a parallel processing structure is used. In this structure, the identification is done as a background task in parallel with the controller function being performed by the processor. This structure permits use of the SVD method at low hardware cost for applications where the plant parameters are expected to change slowly. The feasibility of using an 8-b microprocessor to implement this methodology for adaptive control is demonstrated for a simulated plant of known structure and measurement noise characteristics. The identification time required is tabulated as a function of a) the number of parameters to be estimated, and b) the number of equations that are formed from the observed data.

Intracavity adaptive optics 2: Tilt correction performance

This paper continues the discussion of the detailed experimental study conducted on adaptive optical control methodologies inside a laser resonator.(1) In this paper we discuss the experimental results for the correction of tilt within a laser resonator utilizing a multidither zonal COAT system. The tilt aberration was introduced in three different ways: (1) static tilt was introduced while the COAT system was open loop and then the control loop closed, (2) static tilt was incrementally introduced while the COAT system control loop was closed loop (denoted as continuously closed loop), and (3) dynamic tilt was applied while the COAT system's control loop was closed. Experimental data are presented on the correction capability of the COAT system for each method of introduction of tilt over a large range of tilt angles and control loop configurations (open/ closed and continuously closed). Limitations of the system are discussed, and design recommendations and conclusions are provided.

Intracavity adaptive optics 1: Astigmatism correction performance

A detailed experimental study has been conducted on adaptive optical control methodologies inside a laser resonator. A comparison is presented of several optimization techniques using a multidither zonal coherent optical adaptive technique system within a laser resonator for the correction of astigmatism. A dramatic performance difference is observed when optimizing on beam quality compared with optimizing on power-in-the-bucket. Experimental data are also presented on proper selection criteria for dither frequencies when controlling phase front errors. The effects of hardware limitations and design considerations on the performance of the system are presented, and general conclusions and physical interpretations on the results are made when possible.

A Distributed System Adaptive Control Strategy

One attitude control device being studied for large spacecraft consists of two counter-rotating rings, each designated as an annular momentum control device (AMCD), that are attached to a spacecraft using several magnetic bearings distributed along the circumference of the rings. For large spacecraft large rings are desirable. Unfortunately, for large rings flexibility is appreciable and it becomes necessary to account for the distributed nature of the rings in the design of the magnetic bearing controllers. Also ring behavior is unpredictably sensitive to ring termperature, spin rate, manufacturing imperfections, and other variables. For that reason a distributed adaptive microcomputer-based control system is being sought for ring stabilization and maneuvering. An original adaptive-control methodology for distributed-parameter systems is detailed and application to spinning ring, i. e., AMCD, stabilization is used as an illustration. The proposed methodology, presented as a step-by-step procedure, combines a lumped-parameter expansion description of distributed parameter systems with a fundamental simultaneous identification and control strategy. Simulations are presented providing preliminary evidence of the capabilities of the proposed procedure.