Original Adaptive Control Research Articles

A more general incremental inter-agent learning adaptive control for multiple identical processes in mass production

Mass production has been a trend for modern manufacturing. Information from peer processes may be used to improve the individual process control performance. The idea of ‘incremental step mimicking’ has been presented in our previous work [1] which resulted in ‘incremental inter-agent learning’ (IIAL) adaptive control. However, that work is based on a primitive adaptive control in which the control input is directly calculated from the online-identified model. This paper explores the inter-agent learning adaptive control based on a more complicated adaptive control, and proposes a more general Full-Scale IIAL (FS-IIAL) of which the previous IIAL [1] can be viewed as a special case. With the ensured robust stability, the proposed work is applied on the case when LIP formulae is a single layer RBF neural network, and simulation result validates the superior control performance of each process over the original adaptive control and the previous IIAL.

ADAPTIVE ALGORITHMS OF CONTROLLING THE SMART LI-ION BATTERY CONTROLLER

The article substantiates the necessity of organizing the control system of modern lithium-ion batteries. Passive and active methods of cell balancing are described. The method of increase of efficiency of modes of accumulation of electric energy by means of the special electronic control device (the intellectual controller) and its further use for power supply of the functional equipment is considered. The structure of the intelligent controller as a part of the autonomous power supply system with the description of its main functional units and purpose is presented. Practical results of application in the intellectual controller of original adaptive control algorithms defining modes of operation of lithium-ion drives depending on various environmental conditions are resulted. The results of the analysis obtained by the results of experimental operation of the battery system, reflecting the qualitative and quantitative advantages of the proposed method.

Control allocation-based adaptive control for greenhouse climate

ABSTRACTThis paper presents an adaptive approach to greenhouse climate control, as part of an integrated control and management system for greenhouse production. In this approach, an adaptive control algorithm is first derived to guarantee the asymptotic convergence of the closed system with uncertainty, then using that control algorithm, a controller is designed to satisfy the demands for heat and mass fluxes to maintain inside temperature, humidity and CO2 concentration at their desired values. Instead of applying the original adaptive control inputs directly, second, a control allocation technique is applied to distribute the demands of the heat and mass fluxes to the actuators by minimising tracking errors and energy consumption. To find an energy-saving solution, both single-objective optimisation (SOO) and multiobjective optimisation (MOO) in the control allocation structure are considered. The advantage of the proposed approach is that it does not require any a priori knowledge of the uncertainty bounds, and the simulation results illustrate the effectiveness of the proposed control scheme. It also indicates that MOO saves more energy in the control process.

Adaptively locating unknown steady states: Formalism and basin of attraction

The adaptive technique, which includes both dynamical estimators and coupling gains, has been recently verified to be practical for locating the unknown steady states numerically. This Letter, in the light of the center manifold theory for dynamical systems and the matrix spectrum principle, establishes an analytical formalism of this adaptive technique and reveals a connection between this technique and the original adaptive controller which includes only the dynamical estimator. More interestingly, in study of the well-known Lorenz system, the selections of the estimator parameters and initial values are found to be crucial to the successful application of the adaptive technique. Some Milnor-like basins of attraction with fractal structures are found quantitatively. All the results obtained in the Letter can be further extended to more general dynamical systems of higher dimensions.

Direct Model Reference Learning and Repetitive Control

Abstract Iterative learning and repetitive controllers learn to improve their performance when repeatedly executing a task. The learning process can be based on many different convergence concepts. However, the similarity of objective with adaptive control suggest the use of adaptive concepts. Here direct model reference adaptive control ideas are converted to produce discrete-time direct model reference learning control and direct model reference repetitive control laws with guaranteed convergence to zero tracking error. These control laws are used to modify the command going into a feedback control system, converging on whatever command is necessary to produce the desired output. The original adaptive control concepts used are for linear time invariant systems without deterministic disturbances, but the learning and repetitive control laws developed handle time varying systems with repetitive disturbances. Such general models are important since they can represent the behavior of nonlinear systems linea...

A simplified approach to force control for electro-hydraulic systems

In this paper, a Lyapunov-based control algorithm is developed for force tracking control of an electro-hydraulic actuator. The developed controller relies on an accurate model of the system. To compensate for the parametric uncertainties, a Lyapunov-based parameter adaptation is applied. The adaptation uses a variable structure approach to account for asymmetries present in the system. The coupled control law and the adaptation scheme are applied to an experimental valve-controlled cylinder. Friction modeling and compensation are also discussed. The experimental results show that the nonlinear control algorithm, together with the adaptation scheme, gives a good performance for the specified tracking task. The original adaptive control law is then simplified in several stages with an examination of the output tracking at each stage of simplification. It is shown that the original algorithm can be significantly simplified without too significant a loss of performance. The simplest algorithm corresponds to an adaptive velocity feedback term coupled with a simple force error feedback.

A control method for a nonlinear multivariable system: application to interstitial laser hyperthermia

An original adaptive control method is presented for controlling a nonlinear multivariable system. The method, which could be described as a modified quasi-linear approach, involves dividing the source excitation into a series of pulsing rounds and is implemented as a control algorithm on a computer. The theory underlying the method is developed with reference to an application involving temperature control in interstitial laser hyperthermia. In this application, the method is both successful and necessary to achieve optimally uniform elevated temperatures in a ground beef phantom. Apart from variable and parameter definitions, the method is otherwise general and might be useful for controlling a nonlinear system in which no prior exact characterization of the system is possible. Simulations were conducted to assess the effectiveness of the method in systems for which the unit excitation response changes by factors ranging from zero to three over the total period of excitation. In each case the method has proven stable.

A Stochastic Adaptive Control Problem and Related Non-adaptive Problems

Abstract For a continuous time non-linear stochastic adaptive control problem in which the unknown parameters enter the state equation in an affine way three related nonadaptive stochastic control problems are obtained. Under appropriate conditions optimal controls for any of the related problems give optimal controls for the original adaptive control problem.