Adaptive Generic Model Control Research Articles

Adaptive generic model control scheme for an optimized external heat integrated air separation column using unscented kalman filter

An External Heat-Integrated Air Separation Column (E-HIASC) process is a promising air separation technology. This study focuses on the operational stability of the optimized E-HIASC process for separating nitrogen, oxygen, and argon mixtures. The operation stability of process is achieved through an Adaptive Generic Model Control (AGMC) scheme which is designed by incorporating the identified E-HIASC state-space dynamic model into the controller algorithm. The controller synthesizes the Generic Model Control (GMC) algorithm, decoupled ARX model, and Unscented Kalman Filter (UKF) algorithm to enable the auto-regression and exogenous (ARX) for model identification and the UKF algorithm to estimate time-varying parameters and compute unmeasured E-HIASC state parameters required in the GMC algorithm. A Generic Model Control (GMC) and Multivariable PID (M-PID) control schemes were also designed for benchmarking study. Simulation results show that an AGMC scheme performs better than the GMC and M-PID schemes in tracking the product concentration set point and disturbances rejection.

Adaptive model predictive control scheme for partially internal thermally coupled air separation column

Partially Internal Thermally Coupled Air Separation Column (P-ITCASC) is a highly energy-efficient and cost-effective technology. However, its complex dynamics resulting from thermal coupling pose a challenge to the operating stability of this technology. This article, therefore, proposes an adaptive model predictive control (AMPC) scheme for the P-ITCASC process. The controller incorporates an auto-regressive and exogenous inputs (ARX) model, a linear time-varying Kalman filter, and a recursive polynomial model estimator (RPME) algorithm. Within RPME, ARX polynomial models are identified and utilized to estimate the time-varying parameters and update the prediction model of the process. The process states are observed through the Kalman filter, and the constrained receding horizon optimization problem is solved using quadratic programming. This control scheme ensures the closed-loop system's feasibility and stability in the presence of output/input constraints. An adaptive generic model control, model predictive control, and adaptive internal model control schemes were also designed for benchmarking study. Numerical simulations show that AMPC is more efficient in handling nonlinear dynamics and maintaining product concentration to desired set points compared to other control schemes.

A novel adaptive generic model control strategy for internal thermally coupled air separation columns with multivariable recursive estimation

A gasification process is key in clean electricity production and decarbonization in an integrated gasification combined cycle (IGCC) plant. The quality of the gasification process depends on the stability of the product-concentration streamed from the internal thermally coupled air separation column (ITCASC) unit. There is a need for a stable product-concentration supply to sustain clean electricity production. A proportional-integral-derivative control (PID) control and a nonlinear internal model control (NIMC) scheme are first explored to control the IGCC-ITCASC. Moreover, a robust generic model control (RGMC) is employed to deal with the IGCC-ITCASC inherent nonlinearity and strong interaction that make it very difficult to control. To obtain a satisfactory control performance, an adaptive generic model control (AGMC) with a multivariable recursive estimation is developed. The compared control systems show that AGMC can better handle both the system nonlinearity and strong interaction while improving the control performance.

Adaptive Generic Model Control of Multivariable Processes: Application to Semi-Batch Reactors with Different Relative Degrees

In this study, a multivariable Generic Model Control (GMC) approach is proposed based on input-output linear-in-parameters time series data-driven models. Adaptation of the model parameters is carried out at every sampling instant. For higher relative degree systems, two different definitions are used for output derivatives, yielding two versions of adaptive GMC for multivariable processes. The performance of the proposed control algorithms is illustrated by application to multivariable semi-batch reactors without and with coolant dynamics for control of temperature and one of the reactant concentrations. The study indicated that the adaptive GMC (AGMC) algorithms for higher relative degree multiple-input and multiple-output (MIMO) systems with a different relative degree have exhibited performance comparable to or better than the phenomenological model-based GMC with respect to both set point tracking and smooth input profiles, and also that the predictive version of AGMC (AGMC-II) has exhibited slightly lower integral square error (ISE) values compared to AGMC-I in case of multivariable semi-batch reactor with coolant dynamics.

Dynamic behaviours and control of full tower heat integrated air separation columns

The heat integrated air separation column (HIASC) is a frontier in the energy‐saving distillation research. The dynamic model of HIASC is first proposed. The dynamic behaviours such as strong asymmetric nonlinearity and distinct inverse responses are first investigated based on the simulation results of the dynamic model to give some insight into the dynamic difficulties associated with the control of a high purity HIASC. The multi‐loop PID (M‐PID) and the autoregressive exogenous (ARX) model based generic model control (GMC) scheme are then explored. Furthermore, a multivariable decoupling ARX model is proposed in order to eliminate the interaction between the two single control loops. At last, an adaptive generic model control (ADGMC) scheme for HIASC is presented. The detailed comparative research works are carried out, and the results show that the ADGMC overcomes the interaction of the control loops and the model mismatch of GMC, and hence improves the control performances.

Research of Multiple Sensors Adaptive Fault-Tolerant Control Based on T-S Fuzzy Model for EMB System

In the electro mechanical brake (EMB) system, an improvement of the fault-tolerant control, like invalid protections of sensors and electric systems are the key problems to the development of electric vehicles. The adaptive fault-tolerant control in this paper is focus on the descriptor nonlinear system which contains double time-delays and parameter uncertainties. In fault detection and estimation, construct the controlled system model which contains multi-sensor, double time-delays and parameter uncertainties based on T-S fuzzy model, then design the observer to realize fault detection and estimation in real-time. In building the fault-tolerant control model, the first step is to choose an appropriate sliding surface, and combine the algorithm of sliding control with adaptive generic model control. Then apply the state observer to the designed sliding adaptive generic model, and build the decision model with dynamic fault reconfiguration. And the goal of this paper about the adaptive robust fault-tolerant control for the complex nonlinear controller system is achieved. Numerical SIMULINK simulation examples are given to illustrate the application and the effectiveness of the proposed design method.

Nonlinear dynamic behaviors and control based on simulation of high-purity heat integrated air separation column

In this paper, the dynamic behaviors on the basis of simulation for high-purity heat integrated air separation column (HIASC) are studied. A nonlinear generic model control (GMC) scheme is proposed based on the nonlinear behavior analyses of a HIASC process, and an adaptive generic model control (AGMC) scheme is further presented to correct the model parameters online. Related internal model control (IMC) scheme and multi-loop PID (M-PID) scheme are also developed as the comparative base. The comparative researches are carried out among these linear and nonlinear control schemes in detail. The simulation research results show that the proposed AGMC schemes present advantages in both servo control and regulatory control for the high-purity HIASC.

Research of Adaptive Fault-Tolerant Control Based on T-S Fuzzy Model

The adaptive fault-tolerant control problem for descriptor nonlinear system is researched in this paper. Contents include the properties of Adaptive Fault-tolerant like uncertainties, state delays, and input delays using T-S fuzzy model. First, the design of the state observer to estimate system state and sensor fault is introduced. Then, the adaptive fault-tolerant control structure with the state observer is proposed when combining with the sliding model control (SMC) and the adaptive generic model control (AGMC). Finally, an adaptive fault-tolerant control structure for nonlinear descriptor system is constructed. About above results, both of theoretical analysis and illustrative examples demonstrating the feasibility and validity were proposed.

Sensitivity Compensating Control: Data-driven model based adaptive approach

A novel Sensitivity Compensating Control (SCC) approach is proposed in a data-driven model based platform and combined with an Extended External Reset Feedback (EERF) method to handle sensitivity, input saturation, and accurate process model requirement problems associated with application of Generic Model Control (GMC). Two versions of Adaptive GMC (AGMC) are proposed using linear-in-parameters time-series models with time-varying parameters for higher relative degree systems, and are used in the formulation of SCC and EERF approaches. The steps involved in the proposed approach consist of defining a new process, control law and set point such that the determined control action drives the original process to its desired set point. The performance of the proposed control algorithms is illustrated by application to a benchmark multi-product polymerization reactor control challenge problem. The proposed approaches are applicable to chemical engineering systems exhibiting input sensitivity.

APPLICATION OF MULTIVARIABLE NONLINEAR ADAPTIVE GENERIC MODEL CONTROL TO A PACKED DISTILLATION COLUMN

Nonlinear adaptive generic model control and self-tuning PID control systems were applied to control the top and bottom product temperature of a packed distillation column separating methanol-water mixture. In the first control algorithm, an adaptive generic model control (AGMC) structure was proposed for dual temperature control of the system. In the second control algorithm, nonlinear self tuning PID (NLSTPID) control based on pole-placement technique was used to control the same system. For NLSTPID control purposes pseudo random binary sequence (PRBS) signal and recursive identification algorithm were used to estimate the relevant parameters of a polynomial NARMAX model. In this work, real-time application has been carried out. In both dynamic and control studies, perturbations in feed composition were utilized as the disturbance, and the reboiler heat duty and the reflux ratio were selected as the manipulated variables. The control performances have been obtained by using ISE and, in general, AGMC results were better than those of the STPID control algorithm.

Adaptive generic model control for a class of nonlinear time-varying processes with input time delay

In this article, an adaptive control approach––Adaptive Generic Model Control (AGMC) for a class of nonlinear time-varying processes with input time delay is proposed. First, a nonlinear state predictor (NSP) is introduced, which extends the conventional generic model control (GMC) to a class of nonlinear processes with input time delay. Then a class of nonlinear time-varying processes with input time delay is further considered. A modified strong tracking filter (MSTF) is adopted to estimate the time-varying parameters of the nonlinear processes, and the state estimates are then utilized to update the plant models used in the NSP and MSTF, this results in an adaptive generic model control scheme for a class of nonlinear time-varying processes with input time delay. A modified mathematical model of a three-tank-system is used for computer simulations, the results show that the proposed AGMC algorithm is satisfactory, and it has definite robustness against model/plant mismatch in the measurement noise.

Sensor adaptive fault tolerant control for non-linear processes

Adaptive fault tolerant control of non-linear processes is an open problem. In this paper, on the basis of a strong tracking filter (STF), an approach to sensor adaptive fault tolerant generic model control for non-linear processes is proposed. When the process runs normally, Adaptive Generic Model Control (AGMC) based on parameter estimation is used to control non-linear time-varying processes. A sensor fault model is set up by introducing a bias vector into the output equation of the process. The bias vector is estimated on-line based on the STF during every control period. With the estimated sensor bias vector and the time-varying parameters, a fault detection mechanism is developed to supervise sensors. When a sensor fault is detected, AGMC will be switched to a state estimation and soft-sensor-based GMC. This strategy constitutes a sensor-adaptive fault tolerant generic model control for non-linear processes. Experimental results on a three-tank system demonstrate the effectiveness of the proposed approach.

Strong tracking filter based adaptive generic model control

Generic Model Control (GMC) is a control algorithm capable of using nonlinear process model directly. Parameters in GMC controllers are easily tuned, and measurable disturbances can be compensated effectively. However, the existence of large modeling errors and unmeasurable disturbances will make the performance of GMC deteriorate. In this paper, based on the theory of Strong Tracking Filter (STF), a new approach to Adaptive Generic Model Control (AGMC) is proposed. Two AGMC schemes are developed. The first is a parameter-estimation-based AGMC. After introducing a new concept of Input Equivalent Disturbance (IED), another AGMC scheme called IED-estimation-based AGMC is further proposed. The unmeasurable disturbance and structural process/model mismatches can be effectively overcome by the second AGMC scheme. The laboratory experimental results on a three-tank-system demonstrate the effectiveness of the proposed AGMC approach.

Non-linear adaptive control of fermentation processes utilizing a priori modelling knowledge

This paper describes both SISO and MIMO adaptive versions of non-linear generic model control (GMC) applied to a baker's yeast fermentation. A mechanistic non-linear model is used whose structure can represent a wide range of different fermentation systems and operating conditions. This a priori representation of process knowledge is then combined with a simple and effective adaptation scheme to yield optimal flexibility of the model. Only a few parameters which appear linearly in the model need to be estimated on-line, resulting in very fast and potentially offset-free tracking of the process by the model-based controller. Simulations demonstrate that an adaptive MIMO version of GMC is superior to the corresponding non-adaptive version when faced with modelling errors. Experiments with a bench-scale yeast fermentation system demonstrate for the SISO case the applicability of adaptive non-linear control methods to an actual process and compare adaptive GMC performance with that of a conventional PI controller.

Adaptive generic model control: Dual composition control of distillation

AbstractGeneric model control (GMC) takes care of parametric mismatch for underdamped, closed‐loop specification, whereas robust generic model control (RGMC) can handle parametric mismatch for any closed‐loop specification. But, neither GMC nor RGMC is capable of compensating for structural mismatch. In this study, adaptive GMC(AGMC) and adaptive RGMC (ARGMC) structures are proposed, and their effectiveness over GMC and RGMC is demonstrated with several examples. AGMC exhibits better performance over ARGMC, GMC, and RGMC in all the cases of no process/model mismatch, parametric mismatch as well as structural mismatch.Distillation adaptive generic model control (DAGMC) structure is also proposed for dual composition control of distillation. Since embedding of distillation state‐space model in the basic GMC law is practically impossible, linear and nonlinear models are proposed with adaptation using distillation process data, and DAGMC is applied to two typical nontrivial distillation units. Nonlinear DAGMC exhibited better performance over linear DAGMC.