Multivariable internal model adaptive decoupling controller with neural network for nonlinear plants

Based on a brief review on current harmonics generation mechanism for grid-connected inverter under distorted grid voltage, the harmonic disturbances and uncertain items are immersed into the original state-space differential equation of grid-connected inverter. A new algorithm of global current harmonic rejection based on nonlinear backstepping control with multivariable internal model principle is proposed for grid-connected inverter with exogenous disturbances and uncertainties. A type of multivariable internal model for a class of nonlinear harmonic disturbances is constructed. Based on application of backstepping control law of the nominal system, a multivariable adaptive state feedback controller combined with multivariable internal model and adaptive control law is designed to guarantee the closed-loop system globally uniformly bounded, which is proved by a constructed Lyapunov function. The presented algorithm extends rejection of nonlinear single-input systems to multivariable globally defined normal form, the correctness and effectiveness of which are verified by the simulation results.

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 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.

Notice of Violation of IEEE Publication Principles<br><br>"Internal model control based on locally linear model tree (LOLIMOT) model with application to a PH neutral process"<br>by M. Jalili-Kharaajoo, A. Rahmati and F. Rashidi,<br>in proceedings of the 2003 IEEE Internationa Conference on Systems, Man, and Cybernetics, vol 4, pp. 3051-3-55.<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.<br><br>This paper contains portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br>"Nonlinear Internal Model Control for MISO Systems Based on Local Linear Neuro-Fuzzy Models"<br> by A. Fink, O. Nelles and R. Isermann,<br>in 15th IFAC World Congress, Barcelona, Spain, 2002.<br><br> <br/> The internal model control (IMC) scheme has been widely applied in the field of process control. So far, IMC has been mainly applied to linear processes. This paper discusses the extension of the IMC scheme to nonlinear processes based on local linear models where the properties of linear design procedures can be exploited. The IMC scheme results in controllers that are comparable to conventional multi layer perceptron (MLP) networks. In practice, the tuning of conventional MLP based controllers can be very time-consuming whereas the IMC design procedure is very simple and reliable. The design effort of the IMC based on locally linear model tree (LOLIMOT) algorithm are discussed and the control results are compared by application to nonlinear control of an industrial-scale PH neutralization process. Simulation studies of a PH neutralization process confirm the excellent nonlinear modeling properties of the proposed locally linear network and illustrate the potential for set point tracking and disturbance rejection within an IMC framework.

In this paper, a neural network approach for the identification and control of a separately excited direct (DC) motor (SEDCM) driving a centrifugal pump load is applied. In this application, two radial basis function neural networks (RBFNN) are used: The first is a RBFNN identifier trained offline to emulate the dynamic performance of the DC motor-load system. The second is a RBFNN controller, which is trained to make the motor speed follow a selected reference signal. Two RBFNN control schemes are proposed using direct inverse and internal model control schemes. The performance of the RBFNN identifier and controller is investigated in terms of step response, sharp changes in speed trajectory, and sudden load change, as well as changes in motor parameters. The performance of RBFNN in system identification and control has been compared with the performance of the well-known back-propagation neural network (BPNN). The simulation results show that both of the BPNN and RBFNN controllers exhibit excellent dynamic response, adapt well to changes in speed trajectory and load connected to the motor, and adapt to the variations of motor parameters. Furthermore, the simulation results show that the step response of RBFNN internal model and direct inverse controllers are identical.

Adaptive internal model control: H2 optimization for stable plants

A neural network model of a continuous fluidized bed dryer (FED) is used to synthesize an internal model control (IMC) strategy to control the FED. The controlled variable is exit air temperature and manipulated variable is inlet air temperature. Alternatively an IMC structure is formulated using a linear process model. A conventional controller performance is also studied. Using an error criterion the performance of these controllers is compared. The IMC structure with the neural network performs better. >

The internal model control (IMC) scheme has been widely applied in the field of process control. This is due to its simple and straightforward controller design procedure as well as its good disturbance rejection capabilities and robustness properties. So far, IMC has been mainly applied to linear processes. This paper discusses the extension of the IMC scheme to nonlinear processes based on local linear models where the properties of the linear design procedures can be exploited directly. The resulting controllers are comparable to gain-scheduled PI or PID controllers which are the standard controllers in process industry. In practice, the tuning of conventional PI or PID controllers can be very time-consuming. In this paper, the design effort of the nonlinear IMC and conventional controller design methods are discussed and the control results are compared by applying it to a Hammerstein process and nonlinear temperature control of a heat exchanger.

Combined internal model and inferential control of a distillation column via closed-loop identification

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.

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.

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.

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 &amp; Sons, Ltd.

Adaptive internal model control is analyzed for a single-input-single-output nonlinear system represented by the Hammerstein model wherein the nonlinearity is an odd order polynomial. The recursive least-squares method of Chang and Luus (1971) is used for parameter estimation. To obtain a stable approximation to the inverse of the process model for use as the controller, Vogel-Edgar's method (1980) for linear system is employed. Simulation results have shown that the adaptive internal model control (IMC) performs well even with non-minimum phase Hammerstein systems in the presence of unmeasured load changes and dead-time variations. The performance of nonlinear IMC is found to be superior to that of linear IMC.

In the present paper, we examine the parameterization of all robust stabilizing Internal Model Controllers (IMCs) for multiple-input/multiple-output plants with uncertainties. The parameterization problem is the problem in which all stabilizing controllers for a plant are sought. Since this parameterization can successfully search for all proper stabilizing controllers, it is used as a tool for many control problems. For stable plants, the parameterization of all stabilizing controllers can be represented by the IMC structure. The IMC structure has advantages such as closed-loop stability is assured simply by choosing a stable IMC parameter. Additionally, closed-loop performance characteristics are related directly to controller parameters, which make online tuning of the IMC very convenient. However, there exists a question whether or not, stabilizing controllers for unstable plants can be represented by the IMC structure. The solution to this problem, Morari and Zafiriou, Chen et al. and Mai et al. clarified that any stabilizing controller for unstable plants can be represented by the IMC structure by the parameterization of all stabilizing IMCs for unstable plants. However, their IMCs cannot guarantee the stability of control system for plants with uncertainties. In this paper, we clarify the parameterization of all proper robust stabilizing IMCs for multiple-input/multiple-output plants such that the IMC and the internal model are proper.