Existing PI Controller Research Articles

Application of self-improving Q-learning controller for a class of dynamical processes: Implementation aspects

This paper deals with a practical application of the Q-learning algorithm as a general-purpose self-improving controller operating in a class of industrial closed-loop control systems. The proposed approach solves a practical control engineering problem by designing a controller that starts its operation with the predefined closed-loop control performance and then, without unnecessarily disturbing the normally operated closed-loop system, it learns on-line from interactions with the controlled process to ensure the gradual improvement in the closed loop control performance until this performance reaches a desired user-defined level. It is proposed how to ensure its initial performance by an appropriate initialization of the Q-matrix without assuming any knowledge about the process model. The desired target closed-loop performance is defined by the first-order reference trajectory that should be preserved by the closed-loop system, which requires determining the current state of the closed-loop system by the values of the control error and its time derivative. The novelty of the proposed approach results from: (i) preserving the first order reference trajectory with reduced dimensions of the Q-matrix to two, which significantly reduces memory and computational requirements without any loss of generality, (ii) proposing a method for a reduction of the number of states designed directly for the proposed approach, and (iii) proposing a method for a very convenient initialization of the Q-matrix based only on the tunings of the existing PI controller. All these features bring the proposed approach closer towards practical applications. Results of the simulation and experimental validation show that the proposed Q-learning controller can substitute the existing PI controller bumplessly and provides a gradual improvement in the closed loop performance due to its online learning abilities.

Vector Proportional Integral Control with Delay Compensation for Stator Excited Doubly Salient Synchronous Motors

This paper proposes a novel current control technique to solve the problem that the special doubly salient structure of the studied stator excitation doubly salient synchronous motor (SEDSSM) causes the motor back EMF to cause large harmonic current and torque ripple. Based on the space vector pulse width modulation sinusoidal drive method, a vector proportional-integral controller with delay compensation (VPIC) is introduced into the current loop to replace the traditional PI controller to control the motor. The SEDSSM stator current under the current loop control mode of conventional PI controller is analyzed, and it is found that its waveform is obviously affected and disturbed by the third harmonic current, which will cause torque pulsation. Therefore, additional resonance items are considered to be introduced into the controller to suppress its harmonic. At the same time, considering that the digital delay in the application of the digital system will lead to an unstable state of the control system under the vector proportional-integral controller, the vector proportional-integral controller with delay compensation is introduced for this purpose. Experimental data tested on a 12/8 SEDSSM prototype show that the VPIC controller can significantly reduce stator current ripple and motor torque ripple compared to the existing PI controller.

A synthesis method for first-order active disturbance rejection controllers: Procedures and field tests

Active disturbance rejection control (ADRC) is getting explosive attention with many advantages. This paper proposes a synthesis method for the first-order ADRC based on the existing PI controller. Firstly, the relationship between the first-order ADRC and PI controller is derived. Then the initial parameters of ADRC can be obtained directly from the existing PI parameters and the synthesis procedure is summarized. Moreover, the parameter constraints to obtain better control performance are acquired based on theoretical analyses and ADRC parameters can be optimized based on these parameter constraints. The effectiveness of the proposed ADRC synthesis method and the parameter optimization method is validated by simulations and comparative experiments. Finally, the proposed methods are applied to the secondary air system of a power plant and running data indicate that ADRC can improve the control performance evidently.

Augmentation of Low Voltage Ride through (LVRT) Capability using ANN based electric spring for a Grid-tied Wind Energy Conversion System

Electric Spring (ES) is an innovative approach for distributed voltage control (DVC) whereas at the same time accomplishing nominal demand-side management (DSM) technique for continuously varying renewable energy sources. In order to demonstrate the efficiency of ES when deployed in enormous quantities across the grid-connected power distribution system, it is important to build easy and reliable simulation models of ES that can be integrated into large-scale simulation studies of the power system. The ES study gap identification method defines the ES dynamic simulation methodology that is appropriate for the WECS grid-connected voltage and frequency control studies. The integration of WECS to the electric grid leads to many serious power quality problems. Low voltage ride through (LVRT) capability improvement is one of the important constraints in grid connected WECS. This articleexamines the enhancement of LVRT capability of Doubly Fed Induction Generator (DFIG) based grid connected WECS by usingES. The effectiveness of proposed Artificial Neural Network (ANN) based Electric spring controller is compared with existing PI controller using MATLAB/Simulink software. It is evident that the proposed controller nurtures the trustworthiness of WECS when integrated with existing electric power grid system.

Power Quality Improvement in Hybrid Renewable Energy Source Grid-Connected System with Grey Wolf Optimization

Hybrid Renewable Energy Sources (HRES) such as Photovoltaic (PV) system, Wind Turbine (WT) and Battery Energy Storage System (BESS) into grid-connected system have been tremendously increasing its importance to meet the global energy demand. The integrated design mainly focuses on Power Quality (PQ) issues due to the non-linear load condition. Hence this paper proposes the Grey Wolf Optimization (GWO) with Unified Power Quality Conditioner (UPQC) to solve the PQ problems in HRES system. The UPQC performance is increased by Fractional Order Proportional Integral Derivative (FOPID) with Grey Wolf Optimization (GWO). The performance of the proposed method is compared with the various methods like Biogeography Based Optimization (BBO), Genetic Algorithm (GA), Genetic Search Algorithm (GSA) with existing PI controller and obtained the best results in terms of Total Harmonic Distortions (THD). The proposed system is implemented on MATLAB/Simulink platform

Tuning of Active Disturbance Rejection Control with application to power plant furnace regulation

Active Disturbance Rejection Control (ADRC) is emerging as a promising solution in dealing with the unmeasurable disturbances and unknown uncertainties, which are treated in a lumped manner and augmented as an extended state variable. Subsequently, an extended state observer (ESO) is designed to estimate and cancel the combined uncertain term in real time, modifying the uncertain plant to behave like a nominal model consisting of integrators. In the original ADRC formulation, the plant model is assumed to be of delay-free and its order is assumed to be equal to that of the real plant. However, a low-order ADRC is preferred and received a wide acceptance in practice because of its simplicity. Currently, the feasibility of such practice is not clearly revealed as well as its potential dangers. To this end, this paper analyzes the control mechanism from the perspective of the modified plant, which, in turn, would give guidance to parameter tuning. The effect of each parameter on the compensation efficiency and stability conditions of the modified plant is analyzed, based on which a complete tuning procedure for ADRC is developed where the initial settings is derived from the existing PI controller parameters. Finally, the proposed tuning method is experimentally used for a furnace pressure regulation of a 1000MW power plant, validating the feasibility of the low-order ADRC, even in the absence of both dynamic model and the information on the model order.

Síntesis secuencial de controladores PID basada en el método LQR

The linear quadratic regulator (LQR) method is generalized to allow synthesis of PID controllers in MIMO processes. The proposed method is sequentially applied to produce proportional, integral and derivative actions. Three major usages are conceived for the proposed methodology: (i) <<de novo>> design of PID controllers, (ii) addition of derivative action to existing PI controllers and (iii) diagonalization of PID gain matrices. The two last procedures can be applied to controllers designed with different methodologies. The developed method was applied to the <<de novo>> design of a centralized PID controller for a three input-three output distillation column as well as the addition of derivative action to existing both centralized and multiloop PI controllers for a nonlinear CSTR. Proposed LQR method allowed the synthesis of centralized and multiloop PID controllers with better characteristics for set-point tracking, disturbance rejection, limited use of control signal and insensitivity to plant model uncertainty than those reported by other authors.

Model predictive control from routine plant data

Advanced control strategies such as Model Predictive Control (MPC) offer a lot of benefits when applied to suitable processes. However, their application has been limited due to a number of reasons including the cost of implementation and unavailability in off-the-shelf control equipment. This paper presents a method of designing MPC controllers from historic plant data. Motivated by the concept of closed-loop identification, the developed method reduces the amount of engineering effort and cost associated with the tuning of MPC to replace an existing Proportional Integral/Proportional Integral Derivative (PI/PID) Controller. Simulation studies were carried out and the MPC parameters obtained were close to the true values. The resulting MPC controllers also had a performances similar to the existing PI controllers. These performances were easily improved using only a single tuning parameter.

A robust control approach for high-speed nanopositioning applications

In this paper, a robust controller for the positioning of a piezoelectric tube scanner (PTS) used in an atomic force microscope (AFM) is proposed. A minimax linear quadratic Gaussian (LQG) controller is designed based on an uncertain system model which is constructed by measuring modeling error between the measured and model frequency response. This controller is robust against uncertainties introduced as a result of spillover dynamics of the scanner at frequencies higher than the first resonance frequency (900 Hz) of the scanner and the variation of plant transfer function due to temperature, humidity, and duration of operation. The proposed controller is applied to the PTS in the AFM used in the experiments to evaluate the performance of the proposed method. It is observed that the proposed scheme provides up to 12 dB closed-loop damping of the resonant mode to track the reference triangular signal. The robust performance of the proposed controller has been investigated for 0–1.96 g sample mass variation. A high positioning accuracy up to 125 Hz frequency is achieved by reducing scanner's vibration and tracking error. Higher-quality imaging up to 125 Hz scanning frequency is achieved compared to the existing PI controller and some other existing methods. This control technique may be applied to control vibration for the systems with changing frequency response due to uncertainties, such as vibration control of disc-drive system.

Bilinear modelling, control and stability of directional drilling

This paper proposes an approach for the attitude control of directional drilling tools for the oil and gas industry. A bilinear model of the directional drilling tool is proposed and it characterises the nonlinear properties of the directional drilling tool more accurately than the existing linear model, hence broadens the range of adequate performance. The proposed bilinear model is used as the basis for the design of a Bilinear Proportional plus Integral (BPI) controller. The stability of the proposed BPI control system is proven using stability notions for LTI and LPV systems. The transient simulation results show that the proposed BPI controller is more effective, robust and stable for the attitude control of the directional drilling tool than the existing PI controller. The proposed BPI controller provides improved invariant azimuth responses and significantly reduces the adverse effects of measurement delays and disturbances with respect to stability and performance of the directional drilling tool.

Control Improvement Using MPC: A Case Study of pH Control for Brine Dechlorination

This paper describes a case study carried out to quantify the benefits achieved from the application of model predictive control (MPC) to regulate the pH in a brine dechlorination process. A mechanistic model of the process was developed and used to design a PI controller with feedforward compensation. The model was also used to quantify the potential benefits of commissioning an MPC controller on the process. Following the analysis with the mechanistic model, the designed MPC scheme was applied to the actual process. Through real-time results, it is shown that the deployment of this controller led to substantial improvements in disturbance rejection capabilities when compared to the existing PI controller with lead–lag feedforward compensator. The improved disturbance rejection led to a reduction in pH variation which resulted in reduced operational costs, more reliable production, and improvement in the efficiency of the dechlorination process.

Debottlenecking level control for tanks in series

Smooth operation of an industrial liquid–liquid extraction process with three tanks in series posed an interesting control problem: for control of each tank's liquid level through PI manipulation of each corresponding outlet flow rate, the conflicting objectives of keeping both level and flow rate fluctuations within bounds became impossible to satisfy when the main disturbance (feed flow rate) exceeded a certain magnitude. A solution had to be found, preferably using the existing PI controllers and no additional equipment such as flow sensors and advanced control hardware, to keep cost down. A solution was found based on implementation of a simple modified control structure that employs the same PI controllers. This structure essentially aggregates the three tanks into one larger tank, whose virtual level is controlled by a PI controller, supported by the remaining two PI controllers that keep the differences between tank levels to a minimum, so that the three tanks can behave as virtually one. In addition to theoretical analysis of the proposed structure, plant validation is presented. The questions of how this structure could be reached through numerical optimization are addressed, whereas the broader question of automating the intuitive process of solution generation is raised.

Application of fuzzy logic control system for regulation of differential pressure in Liquid Zone Control System

In this paper, fuzzy control scheme has been proposed for generating regulating signals to feed and bleed control valves, which are used in Liquid Zone Control System (LZCS) for maintaining constant pressure difference between gas outlet header and delay tank. The LZCS and the existing PI controller are briefly described. It is followed by the design of Fuzzy Controller. It consists of seven symmetric triangular input membership functions and output membership functions each. Mamdani implication has been used to infer output contribution from each rule. The centroid type of defuzzification method is employed to get the crisp output values. The fuzzy logic controller for feed and bleed control valves so designed has been validated by performing a variety of experiments on a full scale LZCS test setup at Bhabha Atomic Research Centre and its performance is analyzed and compared to that obtained with existing PI controller. In comparison with the existing PI controller, the fuzzy logic controller’s performance is superior in all cases considered.

Antiwindup Strategy for PI-Type Speed Controller

This paper proposes a new antiwindup strategy for PI speed controller to suppress the undesired side effect known as integrator windup when large set-point changes are made. When the speed control mode is changed from P control to PI control, an appropriate initial value for the integrator is assigned. This value then restricts the overshoot. In addition, the proposed method guarantees the designed performance independent of the operating conditions, i.e., different set-point changes and load torques, and can be easily implemented with existing PI controllers. In SIMULINK/MATLAB-based comparative simulations and experiments for a permanent-magnet synchronous motor speed controller, the proposed method shows a superior control performance compared with the existing well-known antiwindup methods, such as conditional integration and tracking back calculation.

A design framework for overlapping controllers and its industrial application

This paper presents a new practical framework for output feedback control design with overlapping information structure constraints. In comparison to the earlier work, the proposed method removes some restrictions in the control design algorithm by utilizing congruence transformations, simplifications, and the reciprocal variant of the projection lemma. This leads to a less conservative solution than previous design methods because the choice of some design parameters by trial and error is eliminated. Moreover, in some cases the structural constraint of having a diagonal Lyapunov function in linear matrix inequalities (LMIs) is removed. The results are extended to capture a more general scenario of output feedback control design for nonlinear interconnected systems. The validity of the proposed approach is demonstrated through applications to an industrial utility boiler and to a multi-area power system. Simulation experiments using a nonlinear simulation package of utility boilers called SYNSIM reveal that the proposed design strategy overcomes control problems in the present plant and maintains stability in the presence of sudden load variations. Furthermore, the performance of the overlapping controllers is found to be better than existing PI controllers in the plant.

APPLICATION OF RESET CONTROL TO A BATCH REACTOR IN THE PRECIOUS GROUP METALS INDUSTRY

Application of reset control to the temperature control loop of an exothermic batch reactor in the Precious Group Metals industry is investigated in this paper. Through modelling and simulation of the process temperature response to cooling, a comparison is drawn between the existing PI controller, an optimized linear PI controller, an optimized linear PI controller with lead, and a reset controller. The simulations confirm the ability of reset control to significantly reduce temperature overshoot as well as decrease the settling time in this process control loop. In addition, the simulations uncover the necessity of applying a non-zero initial condition to the reset controller in this specific control application. The main purpose of the paper is to demonstrate the potential for novel application of reset control with non-zero initial conditions to an existing industrial process in the hydrometallurgical industry. In the literature actual industrial applications of reset control appear to be scarce, and the application of a non-zero initial condition to the reset controller may well be unique. The paper also highlights some typical practical issues, including effects of dead time and noise, which are involved with the application of theoretical control solutions in an industrial environment.

Fuzzy-Adapted Recursive Sliding-Mode Controller Design for a Nuclear Power Plant Control

In this paper, a multi-input multi-output fuzzy-adapted recursive sliding-mode controller (FARSMC) is designed for an advanced boiling water reactor (ABWR) nuclear power plant, to control reactor pressure, reactor water level and turbine power. The FARSMC is intended to replace the existing conventional controllers for the power range of 70% to 100% rated power. The controller has a recursive form that treats model uncertainty and external disturbances in an implicit way. Thus there is no need to specify uncertainties and disturbances for this controller design in advance. Moreover, the chattering problem common among conventional sliding-mode controllers is completely removed by this recursive sliding-mode control (RSMC) algorithm. The performance of the resulting RSMC is further improved by parameter adaptation using fuzzy logic algorithm, resulting in fuzzy-adapted RSMC (FARSMC). To apply RSMC technique, the original nonlinear plant model is first transformed to a canonical form. Simulations of the simplified ABWR model with the designed FARSMC indicate that FARSMC may result in better performance than the existing PI controllers in that the plant transient responses to the desired output step change have shorter settling time and smaller magnitude overshoot/undershoot. The control actions of the FARSMC are milder than those from the PI controllers. Robustness of the FARSMC with respect to power level variation and capability to reject external disturbances is also achieved. Successful fuzzy adapted sliding-mode controller design and implementation on the model shows that FARSMC may be a practical choice for nuclear power plant control.

A radial basis function neural network controller for UPFC

This paper presents the design of radial basis function neural network controllers (RBFNN) for UPFC to improve the transient stability performance of a power system. The RBFNN uses either a single neuron or multi-neuron architecture and the parameters are dynamically adjusted using an error surface derived from active or reactive power/voltage deviations at the UPFC injection bus. The performance of the new single neuron controller is evaluated using both single-machine infinite-bus and three-machine power systems subjected to various transient disturbances. In the case of three-machine 8-bus power system, the performance of the single neuron RBF controller is compared with a BP (backpropagation) algorithm based multi-layered ANN controller. Further it is seen that by using a multi-input multi-neuron RBF controller, instead of a single neuron one, the critical clearing time and damping performance are improved. The new RBFNN controller for UPFC exhibits a superior damping performance in comparison to the existing PI controllers. Its simple architecture reduces the computational burden thereby making it attractive for real-time implementation.