ADAPTIVE INTERNAL MODEL CONTROL OF HAMMERSTEIN-NONLINEAR SYSTEMS

This paper adopts the adaptive neural internal model control strategy to design a control system for complex and uncertain process with delay. Because of the shortage of the classic internal model control system the performance of the control system will slip back if there exist large errors between the real plant and the model, an adaptive mechanism based on single neuron which can tune the parameters of the internal model and that of controller in the control system on line is designed. The multimodel approach is exploited to represent the direct model by a model basis made up of five linear models, and to construct a controller trained by the corresponding reverse models. The simulation results shows that the adaptive internal model control system designed in this paper has good performance of overcoming deviations of model parameters.

This paper considers the design and analysis of a discrete-time H2 optimal robust adaptive controller based on the internal model control structure. The certainty equivalence principle of adaptive control is used to combine a discrete-time robust adaptive law with a discrete-time H2 internal model controller to obtain a discrete-time adaptive H2 internal model control scheme with provable guarantees of stability and robustness. The approach used parallels the earlier results obtained for the continuous-time case. Nevertheless, there are some differences which, together with the widespread use of digital computers for controls applications, justifies a separate exposition. Copyright © 2001 John Wiley & Sons, Ltd.

Considers the design and analysis of a robust adaptive model algorithmic controller based on the internal model control structure. The certainty equivalence principle of adaptive control is used to combine a discrete-time robust adaptive law with a model algorithmic internal model controller to obtain an adaptive model algorithmic internal model control scheme with provable guarantees of stability and robustness. The approach used parallels the earlier results obtained for adaptive internal model control. Nevertheless, there are some important differences which, together with the widespread interest in model algorithmic control, justifies a separate exposition.

Several processes are nonlinear and operate under input constraints. Their effective control requires model-based techniques whose performance is often affected by modelling uncertainties. The paper addresses the control of single-input single-output nonlinear systems with input constraints in the presence of modelling errors. A nonlinear internal model control based on input output linearisation is designed, and an internal model control with antiwindup which removes the nonlinear mismatch due to input constraints is given. An adaptive internal model control with anti-windup to account for parameter uncertainty and an augmented internal model control with anti-windup to attenuate the effect of modelling errors, are proposed and analysed theoretically. Simulation results on temperature control of a continuous stirred tank reactor demonstrate the benefits of the proposed anti-windup schemes.

Adaptive internal model control: H2 optimization for stable plants

In this paper we have introduced as a model in the adaptive Internal Model Control (IMC) structure a fractional order system to obtain a fractional adaptive IMC scheme. We have shown that this adaptive control scheme always provides theoretical guarantees of stability when a stable fractional order transfer function is used as an IMC parameter and that the use of this fractional IMC parameter can improve the performances of the control system along with its robustness against noises. A comparative simulation example is given to illustrate the efficiency of the proposed fractional order adaptive control scheme. Copyright © 2010 John Wiley & Sons, Ltd.

The application of internal model control (IMC) for process control has received much attention during the past decade. In this paper, the application of IMC for robot control is investigated. Although the IMC approach is shown to be more robust compared to conventional robot control approaches, such as the computed-torque approach, its performance degrades in the presence of large modelling uncertainties and external disturbances. In this paper, a neuro-based adaptive internal model control scheme is proposed. Within the framework of this control structure, a backpropagation neural network algorithm is incorporated into a fired structure internal model controller for robot control. Simulation results, based on a two-link robot confirm the effectiveness of the proposed control algorithm even in the presence of large modelling uncertainties and external disturbances.

Combining neural network identification technique and internal model control (IMC) strategy, a novel nonparametric design for nonlinear plants is presented. Based on this idea, a multivariable adaptive decoupling internal model controller (DIMC) is developed to deal with multivariable nonlinear coupling systems with unknown structure and parameters. A neural network is used to detect the unknown nonlinear internal model. One advantage is that the design does not require the computation of the inverse model of the IMC parameters, but only depends on system input-output data and neural network output.

This paper presents an Internal model controller (IMC) for temperature control of heating, ventilation and air conditioning (HVAC) system. Firstly, the temperature of an air-conditioning system of the test room is controlled by using PID controller. The performance and stability of the control process are degraded due to the significant time delay of air-conditioning process. So, smith-predictor scheme is used to compensate the time delay. But when the process has larger system parameters and longer time delay, smith-predictor does not provide better results. To overcome this problem, an internal model (IMC) controller is developed. Simulation results compare the performance of internal model (IMC) controller with conventional PID controller and smith predictor controller. Simulation result shows that an internal model controller gives satisfactory performance with less overshoot and less settling time.

This article proposes adaptive internal model controls for the collocated output regulation of a flexible wing, where distributed disturbances, boundary disturbances, and references are from an exactly unknown exosystem. Observer-based tracking error feedback controls are first designed to address the robust output regulation in case of a known exosystem matrix. If the exosystem has an unknown matrix, an adaptive observer is further proposed with the observer error system converging to zero exponentially. Then, we can obtain adaptive observer-based controls by combining adaptive observers and observer-based controls, which are able to regulate the tracking errors toward zero in case of the exactly unknown disturbances and references. The corresponding closed-loop system is proved to be internally asymptotically stable. A simulation example is further provided for adaptive internal model control of the wing system.

Extreme learning machine is a novel single hidden layer feedforward neural networks with a strong abilities, for example simple net structure, fast learning speed, good generalization and so on. Aimed at the system with a delay unit, A new control strategy for internal model control is proposed to set up an inverse model of the minimal phase subsystem by using extreme learning machine with in-out system datum. Moreover, the relative stable error for internal model control system with a delay unit is presented to evaluate the system performance. The features for the internal model control system based on extreme learning machine are compared with that based on neural network. The experimental results indicate that the internal model control system based on extreme learning machine has small stable error and strong robustness.

A continuous-time decentralized adaptive internal model controller is proposed for the control of an N/spl times/N MIMO system with unknown parameters. By treating the MIMO system as an interconnection of N linear SISO subsystems with weak interactions, robust adaptive internal model controllers are designed for each isolated subsystem. Each local adaptive control law utilizes a normalizing signal which is generated using the local output and the inputs of all the subsystems. This necessitates the exchange of input signals between the different subsystems so that the scheme is no longer totally decentralized. This, however, does not pose any problem in industrial process control applications, where all the signals are anyway brought to or originate from a central control room. An additional feature which makes the scheme very attractive is the fact that its computational complexity is about the same as that of a totally decentralized one.

A two degree freedom internal model control (IMC) is implemented for the temperature control of a single tubular heat exchanger system. The experimental heat exchanger system is a very simple structure, however, the characteristics are regarded as a distributed parameter system and time delay made by software; the plant is regarded as complex system. With the aim of applying IMC structure, we approximate the heat exchanger system to a time delay model from a frequency response examination. In the laboratory experiment, it is clear that: (1) the two degree of freedom IMC is effective for the time delay system; (2) the two degree of freedom performance of IMC was superior to one degree freedom IMC; the disturbance was reduced compared to a PI controller or one degree of freedom IMC design; (3) since the controller had two degrees of freedom, the one tuning filtering parameter of the IMC controller effected the tracking of the reference and the other tuning parameters of IMC reduce disturbance; (4) the design method was as simple as an ordinary one degree of freedom IMC design; and (5) since the IMC controller was constructed from the system model, there were some problems to set up the modeling and identification.

For the thermal process is of special features, and it could be disturbed by uncertain factors easily, a novel modeling method based on least mean square (LMS) adaptive filter is presented. Based on the internal model control (IMC) principle, the sub-loop system and the inert area of the superheated steam system are considered as a generalized control object, an internal model-P cascade control system is designed. The models are built by the adaptive LMS filter, the corresponding internal controller is designed, and the self-adaptive control of the full operating condition is realized. The simulation result under typical operating condition in the superheated steam process of a supercritical once-through 600 MW boiler show that the method has a good regulating performance. The method is based on the online identification; it is adapted to the complex industrial process whose dynamic characters varied with different operating conditions.

An adaptive direct recurrent neural network model is developed for nonlinear dynamic system modelling in this paper. The model adaptation is achieved with the extended Kalman filter (EKF). A novel recursive algorithm is proposed to calculate the Jacobian matrix in the model adaptation so that the algorithm is simple and converges fast. The effectiveness of the developed adaptive model is demonstrated by applying to modelling a simulated continuous stirred tank reactor (CSTR). The model converges to the new process dynamics very quickly after a constant disturbance is added, and therefore can be used as an adaptive model in the adaptive model predictive control or internal model control for time-varying systems or fault tolerant control of nonlinear systems.