Multi-loop Proportional Integral Controller Research Articles

Distributed generation integrating a photovoltaic-based system with a single- to three-phase UPQC applied to rural or remote areas supplied by single-phase electrical power

This paper presents a distributed generation (DG) system integrating a photovoltaic (PV) system with a 1-Phase to 3-Phase unified power quality conditioner (UPQC-1PH-3PH). The system named DG-UPQC-1PH-3PH injects into the grid the energy produced from a PV array, contributing to feed loads connected to a local three-phase four-wire electrical system, to attend consumers of rural and/or remote areas supplied by single-phase utility. Besides the injection of active power into the grid, the DG-UPQC-1PH-3PH improves power quality indicators, such as voltage sags/swells, grid voltage harmonics, and power factor. Multi-resonant state-feedback controllers are employed to ensure sinusoidal grid current and load voltages, while an effective PV array voltage control is achieved by using a multi-loop proportional-integral controller. Since the DG-UPQC-1PH-3PH inverters handle the power produced by the PV system plus those demanded by the load, the converters sizing must be carefully performed. Thus, curves that allow the sizing of the power converters based on the power flow through the converters are presented. The static performance is experimentally tested considering several operation conditions involving the utility grid voltage, load and energy produced by the PV array, while its dynamic behavior is evaluated when the PV array is subjected to abrupt solar irradiation changes.

LPV decoupling for control of multivariable systems

This article investigates methods for decoupling multivariable linear parameter varying (LPV) systems and proposes a new interaction measure for decoupled proportional-integral (PI) feedback control design in LPV systems. The proposed approach seeks to benefit the multivariable control of multi-input multi-output (MIMO) systems with variable operating conditions, variable parameters or nonlinear behaviour. This method can improve the tracking performance and reduce the operating conditions variability of such systems with significant coupling in the system dynamics. We design MIMO decoupling feedback LPV controllers to address loop interaction effects. The proposed method uses a parameter-dependent static inversion or SVD decomposition of the system to minimise the effects of the off-diagonal terms in the MIMO system transfer function matrix. A new parameter-dependent interaction measure is introduced based on the SVD decomposition and static inversion which is subsequently utilised for tuning multi-loop PI controller gains. Numerical examples are presented to illustrate the validity of the proposed LPV decoupling methods, as well as the use of the proposed interaction measures for a decoupled multi-loop PI control design.

Genetically Tuned Decentralized Proportional-Integral Controllers for Composition Control of Reactive Distillation

This paper presents a genetic algorithm (GA) based autotuning method to design a decentralized proportional-integral (PI) control system for composition control of a highly interactive and nonlinear reactive distillation column. The control relevant characteristics such as nonlinearities, interactions, and stability are analyzed for assessing the complexity of the process. The objective of GA tuning is to account the multivariable interactions and nonlinear dynamics of the process to find a unique set of parameters for the control system that is robust to all kinds of disturbances. The performance function in GA is formulated by incorporating the dynamic state information of the process derived from its model for various closed-loop disturbance conditions. The controller tuning problem of this multivariable process is resolved as an optimization problem and multiloop PI controllers are designed by exploiting the powerful global search features of GA. An estimator is designed to provide the compositions wh...

다중 시간지연 공정을 위한 개선된 다중루프 PI 제어기 설계

An analytical method for robust design of the multi-loop proportional-integral (PI) controller is proposed for various types of multi-delay processes. On the basis of the direct synthesis and generalized IMC-PID approach, the analytical tuning rules of the multi-loop PI controller are firstly derived for achieving the desired closed-loop response, and the structured singular value synthesis is then utilized for the tradeoffs between the robust stability and performance by adjusting only one design parameter (i.e., the closed-loop time constant). To verify the superiority of the proposed method, the simulation studies have been conducted on a wide variety of multivariable processes. The multi-loop PI controller designed by the proposed method shows a fast, well-balanced and robust response with the minimum integral absolute error (IAE) in compared with other renowned methods.

Neurocontrol of a multi-effect batch distillation pilot plant based on evolutionary reinforcement learning

The time cost of first-principles dynamic modelling and the complexity of nonlinear control strategies may limit successful implementation of advanced process control. The maximum return on fixed capital within the processing industries is thus compromised. This study introduces a neurocontrol methodology that uses partial system identification and symbiotic memetic neuro-evolution (SMNE) for the development of neurocontrollers. Partial system identification is achieved using singular spectrum analysis (SSA) to extract state variables from time series data. The SMNE algorithm uses a symbiotic evolutionary algorithm and particle swarm optimisation to learn optimal neurocontroller weights from the partially identified system within a reinforcement learning framework. A multi-effect batch distillation (MEBAD) pilot plant was constructed to demonstrate the real world application of the neurocontrol methodology, motivated by the nonsteady state operation and nonlinear process interaction between multiple distillation columns. Multi-loop proportional integral (PI) control was implemented as a reduced model, reflecting an approach involving no modelling or significant controller tuning. Rapid multiple input multiple out nonlinear controller development was achieved using SSA and the SMNE algorithm, demonstrating comparable time and cost to implementation in relation to the reduced model. The optimal neurocontroller reduced the batch time and therefore the energy consumption by 45% compared to conventional multi-loop SISO PI control.

Multi-loop PI controller design based on the direct synthesis for interacting multi-time delay processes

In this article, a new analytical method based on the direct synthesis approach is proposed for the design of a multi-loop proportional-integral (PI) controller. The proposed design method is aimed at achieving the desired closed-loop response for multiple-input, multiple-output (MIMO) processes with multiple time delays. The ideal multi-loop controller is firstly designed in terms of the relative gain and desired closed-loop transfer function. Then, the standard multi-loop PI controller is obtained by approximating the ideal multi-loop controller using the Maclaurin series expansion. The simulation study demonstrates the effectiveness of the proposed method for the design of multi-loop PI controllers. The multi-loop PI controller designed by the proposed method shows a fast, well-balanced, and robust response with the minimum integral absolute error (IAE).

Nonlinear Control of a Simulated Industrial Evaporation System Using a Feedback Linearization Technique with a State Observer

This paper demonstrates the application of a feedback linearization control technique with a state observer on an industrial multiple-effect evaporation system of an alumina refinery. The nonlinear controller consists of a state feedback controller, single-input single-output (SISO) proportional−integral (PI) controllers, and a reduced-order observer. The simulation study serves to verify the performance of the nonlinear controller on the evaporation system by comparing the existing multiloop PI control system. Closed-loop simulation results showed that the nonlinear controller provides smoother operation for the evaporation despite significant model mismatches between the controller model and the simulated evaporator. A computer algorithm, consisting of the sequential execution code for the state feedback control law, SISO PI controllers, and a reduced-order observer, has been developed for implementation on the evaporator on-site.

Robust Multivariable Controllers for a Tubular Ammonia Reactor

The specific objective of this paper is to develop multivariable controllers that would achieve asymptotic regulation in the presence of parameter variations and disturbance inputs for a tubular reactor used in ammonia synthesis. A ninth order state space model with three control inputs and two disturbance inputs is generated from the nonlinear distributed model using linearization and lumping approximations. Using this model, an approach for control system design is developed keeping in view the imperfections of the model and the measurability of the state variables. Specifically, the design of feedforward and robust integral controllers using state and output feedback is considered. Also, the design of robust multiloop proportional integral controllers is presented. Finally the performance of these controllers is evaluated through simulation.