Direct adaptive control using an adaptive reference model

Direct model reference adaptive control is considered when the plant-model matching conditions are violated due to large changes in the plant or incorrect knowledge of the plant's mathematical structure. Because of the mismatch, the plant can no longer track the original reference model, but may be able to track a modified reference model that still provides satisfactory performance. The proposed approach uses a time-varying ‘adaptive’ reference model that reflects the achievable performance of the changed plant. The approach consists of direct adaptation of state feedback gains for state tracking and simultaneous estimation of the plant-model mismatch. The reference model adapts to the changed plant, and is redesigned if the estimated plant-model mismatch exceeds a bound determined via robust stability and/or performance criteria. The resulting controller offers asymptotic state tracking in the presence of plant-model mismatch as well as matched parameter deviations.     

Acceptance of online banking information systems: an empirical case in a developing economy

This paper proposes an extended model of technology acceptance to understand potential users’ acceptance of online banking information systems (OBIS). The proposed model integrates key constructs from information systems and e-commerce acceptance research streams into the theoretical frame of the technology acceptance model. The model was tested on a sample of 353 Internet banking users in Pakistan. Using structural equation modelling with Analysis of Moment Structures software, data analysis showed considerable support for the extended hypothesised model. The results suggested that, in order of importance: perceived usefulness (PU), perceived ease of use (PEOU) and trust explained 45.7% of the variance in intended acceptance behaviour. Trust and technological self-efficacy (TSE) predicted 28.1% of the variance in PU. While in PEOU 21.8% of the variance was predicted by TSE, accessibility and terminology clarity. This study produced valuable insights into the factors that influence acceptance of OBIS by intended users and offers new ideas in understanding the acceptance of technology, especially in developing countries.     

Dissolved oxygen control through an adaptive non-linear model approach: a simulation study

Several exact linearization methods were applied to a simplified non-linear model for the concentration of dissolved oxygen in a waste water treatment plant, and digital control algorithms were derived based on these linearizations. A non-linear adaptive control algorithm is proposed and compared with a well tuned PID, a linear adaptive controller, and a non-adaptive non-linear controller. The proposed algorithm shows a better performance under a variety of perturbations. However, users must be careful in the choice of the appropriate model parameters to be estimated.     

Technology acceptance model for internet banking: an invariance analysis

The technology acceptance model (TAM) has been applied in different contexts to investigate a wide range of information technologies (IT), and a cumulative tradition has already been developed in this stream of research. Most TAM studies have been empirical investigations, using the survey approach with great success. TAM is a mature model and has been validated in different contexts. However, it still needs to be empirically investigated for its invariance across different respondent subgroups in order to make sure that different sample profiles would not have a negative effect on the findings. Unfortunately, this has not happened in most TAM research. Here, we applied different levels of invariance analysis on the TAM construct in the context of Internet banking acceptance. We concluded that the TAM construct was invariant for our sample across different gender, age, and IT competence subgroups. These findings suggested that male and female, old and young, IT expert and novice, conceptualized the TAM construct in very similar ways. These findings allowed us to understand TAMs validity in technology acceptance research.     

Sequential control of a monetary policy model for India

This paper attempts to incorporate expectation generating mechanisms in a formal economic planning model. We have tried to introduce a simple bayesian filter method into an econometric model to see how the system parameters would change subject to a ‘policy-optimization-process’. The method was applied to a monetary policy model for India to see how the political authorities would react to private sector reactions to the policy-optimization-process. Through continuous updating of the response parameters, we can derive a very different type of monetary-fiscal policy structure than that which can be obtained through the usual optimal control process.     

Central tendency minimum variance adaptive control

The authors propose designing indirect adaptive controllers using measures of central tendency of the a posteriori probability function of the control for parameters. That is, given a knowledge of plant uncertainty at each time instant from estimation algorithms, and a controller design rule, controller parameters are sought which maximize the likelihood of achieving the control objectives. The particular case of adaptive minimum variance control is studied in detail     

Nonlinear adaptive model following control for a 3-DOF tandem-rotor model helicopter

This paper considers two-input, two-output nonlinear adaptive model following control of a 3-DOF (degree-of-freedom) tandem rotor model helicopter. The control performance is studied by real time implementation of the control algorithms in an actual helicopter testbed. Since the decoupling matrix of the model helicopter is singular, the system is not decouplable by static state feedback, and it is challenging to design a feedback control system. Dynamic state feedback is applied. The controller is designed using a nonlinear structure algorithm. Furthermore, a parameter identification scheme is introduced in the closed-loop system to improve the control performance. Three identification methods are discussed.     

Simple method for model reference adaptive control

A simple method is presented for combined signal synthesis and parameter adaptation within the framework of model reference adaptive control theory. The results are obtained using a simple derivation based on an improved Lyapunov function.     

Discrete model reference adaptive control with an augmented error signal

A method is developed for designing discrete model reference adaptive control systems when one has access to only the plant's input and output signals. Controllers for single-input, single-output, nonlinear, nonautonomous plants are developed via Lyapunov's second method. The augmented error signal method is employed to ensure that the normally used true error signal approaches zero asymptotically without requiring anticipative values of the plant output signal. Such anticipative signals are replaced by others easily obtained from low pass digital filters operating on the plant's input and output signals.     

Bang-bang control strategy using a tabular adaptive model

An adaptive-predictive model for nonlinear, finite settling-time processes has been described by Roy et al., This paper utilizes this model for the trajectory following control of both finite and infinite settling-time nonlinear systems with switched two-level input.     

Fuzzy model based adaptive control for a class of nonlinear systems

A fuzzy model based adaptive control algorithm for a class of continuous-time nonlinear dynamic systems is presented. The fuzzy model consisting of a set of linear fuzzy local models that are combined using a fuzzy inference mechanism is used to model a class of nonlinear systems. Each fuzzy local model represents a linearized model corresponding to the operating point of the controlled nonlinear system. The proposed control algorithm employs the fuzzy controller that is designed by considering the linear state feedback controller corresponding to the fuzzy local model with the maximum weight and the switching-σ modification adaptive controller to adaptively compensate for the plant nonlinearities. Stability robustness of the closed-loop system is analyzed in Lyapunov sense. It is shown, that the proposed control algorithm guarantees global stability of the system with the output of the system approaching the origin if there are no disturbances and uncertainties, converging to the neighborhood of the origin for all realizations of uncertainties and disturbances. The simulation examples for controlling inverted pendulum system are given to illustrate the effectiveness of the proposed method     

一类非线性多变量系统的多模型自适应控制

针对一类不确定的非线性多变量离散时间动态系统,提出了一种基于切换的多模型自适应控制方法.该控制方法的特点在于以下两个方面:首先,引入一个高阶差分算子使得非线性系统的非线性项的限制条件不再要求全局有界;其次,提出的控制方法由线性自适应控制器、神经网络非线性自适应控制器以及切换机构组成:线性控制器用来保证闭环系统的输入输出信号有界,神经网络非线性控制器用来改善闭环系统的性能,基于性能指标的切换机构在每一时刻选择性能指标较好的控制器对系统进行控制.理论分析和仿真实验说明了提出的多模型自适应控制方法的有效性.     

一种实用的模型自适应MPC策略

在丙烯精馏塔的控制中,由于被控对象的非线性特性,采用线性模型的模型预测控制器难以保持良好的控制性能。本文提出基于系统稳态模型的模型自适应MPC策略,利用稳态模型在不同操作点上被控变量对操纵变量及扰动变量的相对变化率的变化,来刷新RMPCT控制器中各通道的模型增益。在模型输出对输入的相对变化率的计算中,使用主操作区间内的变化率以替代实际操作点的变化率,并采用设定模型变化域和控制模型变化频度的方法,以解决模型变化过大,与模型变换周期和RMPCT控制周期不协调而引起的系统不稳定等问题。实际投运效果表明:采用该控制策略,塔顶、塔底温度控制偏差与传统RMPCT比下降了一个数量级,有利于稳定与提高产品质量。     

Robust model reference adaptive control

We propose a new model reference adaptive control algorithm and show that it provides the robust stability of the resulting closed-loop adaptive control system with respect to unmodeled plant uncertainties. The robustness is achieved by using a relative error signal in combination with a dead zone and a projection in the adaptive law. The extra a priori information needed to design the adaptive law, are bounds on the plant parameters and an exponential bound on the impulse response of the inverse plant transfer function.     

Fuzzy model reference adaptive control

This paper investigates a fuzzy model reference adaptive controller (FMRAC) for continuous-time multiple-input-multiple-output (MIMO) nonlinear systems. The proposed adaptive scheme uses a Takagi-Seguno (TS) fuzzy adaptive system, which allows for the inclusion of a priori information in terms of qualitative knowledge about the plant operating points or analytical regulators (e.g., state feedback) for those operating points. A proportional-integral update law is used to obtain a fast parameters adaptation. Stability and robustness of this adaptive scheme are established using Lyapunov stability tools. The simulation results, for a two-link robot, confirm the performance of the proposed approach.     

Multiple model adaptive control for a class of nonlinear systems with unknown control directions

This study introduces an improved multiple model adaptive control (MMAC) algorithm for a class of nonlinear discrete-time systems. The controller consists of a linear direct adaptive controller, a neural network-based nonlinear direct adaptive controller and a switching mechanism. The assumption of the nonlinear term is relaxed by incorporating a parameter estimator with an augmented error. The control direction of the system is not required to be known by employing a linear direct adaptive controller with the discrete Nussbaum gain and future output predictions. The stability of the closed-loop systems applying the proposed MMAC method is proved and the improved transient performance of the system is illustrated by the simulation results.     

Identifier-tracking model reference adaptive control

Model reference adaptive control is a major design method for controlling plants with uncertain parameters. The primary objective of this paper is to develop a new design approach for a differentiator-free model reference adaptive control of a single-input single-output linear time-invariant plant. The proposed method, called the “Identifier-tracking model reference adaptive control”, uses a stacked identifier structure that is new to the field of adaptive control. The goal is to make the output of the plant asymptotically track the output of the first identifier, and then driving the output of the first identifier to track that of the second identifier, and so forth, up to the qth identifier where q is the relative degree of the plant. Lastly, the output of the qth identifier is forced to converge to that of the reference model. Simulation results show the superiority of the proposed method over the traditional model reference adaptive control with augmented error in terms of the transient response. Since the resulting control systems are non-linear and time-varying, the stability analysis of the overall system plays a central role in developing the theory.     

Predictive functional control based on an adaptive fuzzy model of a hybrid semi-batch reactor

In this paper a new approach to the control of a nonlinear, time-varying process is proposed. It is based on a recursive version of the fuzzy identification method and predictive functional control. First, the recursive fuzzy identification method is derived, after which it is used in connection with fuzzy predictive functional control to construct an adaptive fuzzy predictive functional controller. The adaptive FPFC is then tested on a nonlinear, time-varying, semi-batch reactor process and compared with the standard FPFC, which uses non-adaptive fuzzy model. The simulation results are promising; they indicate that the control of time-varying, nonlinear processes with the FPFC can be improved with the use of an adaptive fuzzy model. An improvement in reference tracking and disturbance rejection can be observed, but the main advantage is the reduced number of switchings between hot and cold water. This is an important improvement in the case of real applications.     

Dynamical properties of an adaptive control system with a nonlinear reference model. II

We consider the problem of constructing an adaptive control system with nonlinear reference model for a low order nonstationary dynamical object with a nonlinear actuator. We show the structure, analysis, and mathematical modeling results for the adaptive control system.     

A simplified algorithm for model reference adaptive control

Generation of one of the auxiliary inputs in model reference adaptive control systems requires a positive definite quadratic function of2nsystem variables for annth order system. To generate such a function can require as many as2nsignal multiplications. In this paper, it is shown how this input can be replaced by one that is much simpler to generate and reduces the number of signal multiplications required to one. The property of global asymptotic stability of the closed-loop adaptive system is preserved with the simpler auxiliary input.