Set-point Tracking Research Articles

Maximum sensitivity constrained modified Smith predictor for delayed integrating type stirred tank reactors model

Abstract A maximum sensitivity constrained modified Smith predictor consisting of two PD controllers is developed for integrating type continuously stirred tank reactors (CSTRs) model. This CSTR type model approximated as integrating first order plus time delay with and without inverse response processes is challenging to control as it contains integrating term, process time delay and a zero lying in the right half of s-plane. The inner and outer loop PD blocks are tuned to achieve the desired set-point tracking and load disturbance rejection responses using direct synthesis approach. The proposed control is free from integrating action and therefore, smooth setpoint tracking response with no overshoot is obtained which is highly desirable in the process industries. Two design parameters λ and τ are involved in the suggested control whose suitable values are recommended to achieve the desired robustness level by specifying the maximum sensitivities of the inner and outer loop transfer functions. Various illustrative models are considered to show the effectiveness and superiority of the proposed control structure. The system robustness towards process parameter perturbations and load disturbances are investigated in all the examples. Performance of the suggested modified Smith predictor is found superior as compared to the other recently published works.

3DoF-KF HMPC: A Kalman filter-based Hybrid Model Predictive Control Algorithm for Mixed Logical Dynamical Systems

This paper presents the formulation, design procedure, and application of a hybrid model predictive control (HMPC) scheme for hybrid systems that is embedded in a mixed logical dynamical (MLD) framework. The proposed scheme adopts a three degrees-of-freedom (3DoF) tuning method to accomplish precise setpoint tracking and ensure robustness in the face of disturbances (both measured and unmeasured) and uncertainty. Furthermore, the HMPC algorithm employs setpoint and disturbance anticipation to proactively enhance controller performance and potentially reduce control effort. Slack variables in the objective function prevent the mixed-integer quadratic problem from becoming infeasible. The effectiveness of the proposed algorithm is demonstrated through its application in three distinct case studies, which include control of production–inventory systems, time-varying behavioral interventions for physical activity, and management of epidemics/pandemic prevention. These case studies indicate that the HMPC algorithm can effectively manage hybrid dynamics, setpoint tracking and disturbance rejection in diverse and demanding circumstances, while tuned to perform well in the presence of nonlinearity and uncertainty.

Constraint Optimal Model-Based Disturbance Predictive and Rejection Control Method of a Parabolic Trough Solar Field

The control of the field outlet temperature of a parabolic trough solar field (PTSF) is crucial for the safe and efficient operation of the solar power system but with the difficulties arising from the multiple disturbances and constraints imposed on the variables. To this end, this paper proposes a constraint optimal model-based disturbance predictive and rejection control method with a disturbance prediction part. In this method, the steady-state target sequence is dynamically corrected in the presence of constraints, the lumped disturbance, and its future dynamics predicted by the least-squares support vector machine. In addition, a maximum controlled allowable set is constructed in real time to transform an infinite number of constraint inequalities into finite ones with the integration of the corrected steady-state target sequence. On this basis, an equivalent quadratic programming constrained optimization problem is constructed and solved by the dual-mode control law. The simulation results demonstrate the setpoint tracking and disturbance rejection performance of our design under the premise of constraint satisfaction.

Active disturbance rejection control of pressurizer pressure during startup process in a pressurized water reactor nuclear power plant

To cope with the participation of power plant peak shaving and realize the diversification of nuclear energy applications including cogeneration, desalination, etc. The application of automatic procedure startup and shutdown system (APS) is an important research direction to improve the safety, economy and flexibility of pressurized water reactor (PWR) nuclear power plant (NPP). But it is difficult to realize automatic control of pressurizer pressure in the PWR NPP startup condition with drastic changes and disturbances. To solve this problem, an active disturbance rejection control (ADRC) algorithm was proposed for the pressure control of the pressurizer. The controller parameters were tuned based on a two-step tuning scheme. Its control performance and disturbance rejection ability were studied and compared with proportional-integral-derivative (PID) controller under different operation conditions. The simulation results showed that the designed controller with fixed parameters can outperform the PID controller with specific optimized parameters on setpoint tracking and disturbance rejection. This study is of great significance for realizing the automatic control of pressurizer pressure in the startup process, and can provide reference for the design of the automatic control system during PWR startup. The controller was applied in typical startup process and achieved satisfactory control performance.

Hybrid Model using Bond Graph-TCN Network and Event Triggered Predictive Control of pH Neutralization Process

The pH neutralization process is a concrete decisive unit in major reactor units of industrial process control loops. This article presents a new fuzzy-based hybrid 'Bond Graph-Temporal Convolution Network' (BG-TCN) model, structured for the convoluted dynamics of a real-time pH neutralization unit, known for its complexity and high nonlinearity. The TCN scheme suggested in this article, exploits a one-dimensional causal convolution strategy within a residual learning framework to execute dilated causal convolutions through time series data analysis. Conversely, Bond Graph (BG) is a graphical tool, designed on an energy-centric approach, to represent energy transfer and interactions across different compartments of the nonlinear pH neutralization system. Furthermore, a linguistic fuzzy rule-based inference system is encompassed to handle uncertainties from BG and TCN models, allowing smooth integration and flexible transition between these two approaches. Additionally, the performance of the hybrid BG-TCN model is assessed against the individual TCN and BG models in a Python environment. On top of that, this article also envisions an event-triggered predictive control utilizing a fuzzy event handler mechanism to demonstrate the efficacy of the proposed hybrid BG-TCN in attaining precise set point tracking for closed-loop servo and regulatory problems.

A model predictive control algorithm for double-hormone artificial pancreas based on optimal three-branch decision switching rules

At present, double-hormone artificial pancreas is a hotspot in the research of regulating blood glucose level; however, there are few studies on switching mechanism of the subsystems for double-hormone artificial pancreas, and most of the existing studies rely on the prior knowledge of application fields. A model predictive control algorithm for double-hormone artificial pancreas based on optimal three-branch decision switching rules is proposed to solve this problem. The blood glucose controllers of insulin subsystems, zero infusion subsystems, and glucagon subsystems are constructed using model predictive control algorithms. Then, using the three-branch decision theory, switching rules between the three subsystems are designed, and the optimal switching threshold value can be solved by the mixed fruit fly algorithm. The algorithm can realize automatic acquisition of the threshold in the three-branch decision switching rules and gets rid of the dependence on the prior knowledge in the field of diabetes research. By the end, the UVa simulation platform is used to conduct experiments on 33 virtual patients and the simulation results verify that the proposed algorithm can control blood glucose effectively within the normal range and can achieve satisfactory performabce in error of tracking set point, safety, operation efficiency, group control quality, and system stability.

Dynamic Inversion-Enhanced U-Control of Quadrotor Trajectory Tracking

This study proposes a universal (U)-control enhanced with dynamic inversion for a class of multiple-input multiple-output (MIMO) quadrotor flight tracking operations. In the technique, a robust compensator is integrated with the U-controller to achieve stabilisation at the equilibrium and setpoint tracking in the presence of any unmodelled uncertainties and external disturbances. A series of bench tests of simulated and real experiments on a Parrot Mambo quadrotor are conducted to show the design framework from the academic formulation to Simulink simulation and real flight tests.

Control of a Directional Downhole Drilling System Using a State Barrier Avoidance Based Method

Abstract Vibrations such as bit-bouncing, stick–slip, and whirl motion can significantly reduce downhole drilling's performance and efficiency. These phenomena are likely to arise once the dynamical states of drilling fall into undesired operating regimes, as verified by both theoretical analyses and experimental studies. To effectively avoid such undesired operating conditions of a downhole drilling process, this paper introduces an advanced nonlinear state-constrained control method to formulate a setpoint tracking problem of high-order directional drilling dynamics under state constraints. These constraints are carefully defined using the field test results, and their shapes are in complex state-space regions, causing additional complexity to the control design. Moreover, unlike vertical drilling, the directional drilling system requires modeling of coupled axial and torsional dynamics with a higher degree-of-freedom (DOF). In this study, the proposed method will tackle these challenges by converting the original high-order constrained control problem into a standard nonlinear control problem. Leveraging on the linear parameter varying (LPV) method that is applied to the converted system, we can actively prevent drilling from falling into the undesired regimes, to achieve the constrained control objective.

Enhancing disturbance rejection in boost converter for CPL: A controller design approach with partial pole placement and approximate model matching

In DC microgrids, the cascade operation of the DC-DC converters leads to a constant power load fed by the boost converter, resulting in unstable behavior in addition to the non-minimum phase dynamics. An enhanced load disturbance rejection control scheme based on partial pole placement and an approximate model matching technique is proposed. This approach is inspired by the well-known direct synthesis method to achieve optimal load disturbance rejection performance. The effectiveness of the proposed control scheme has been validated through hardware implementation, demonstrating its capability in setpoint tracking and load disturbance rejection under variations in load power and input voltage. The robust stability of the suggested scheme has been investigated through the Kharitonov theorem and Monte-Carlo simulation, considering different performance matrices. The suitability of the presented scheme has been established by analyzing the comparative performance of recently reported works. The proposed controller reduces peak overshoot and settling time by ∼50%, handles system uncertainties up to 75%, and outperforms FOPID controllers tuned with particle swarm optimization, queen bee genetic algorithm, and chaos game optimization methods.

Study of parabolic trough collector with crucial analysis on controller and estimator with variable mirror efficiency

Solar Thermal Power (STP) plants are a crucial technology for converting solar energy into electricity. Among the STP, the Parabolic Trough Collector (PTC) is one of the integral parts of STP systems for electrical power generation. Maximizing PTC performance is a challenge due to dynamic and unpredictable solar radiation and environmental disturbances, such as cloud cover and dust accumulation. The optical efficiency parameter of the PTC is critical for calculating the desired heat gain, which is difficult to measure in real-time, necessitating sophisticated control and estimation strategies for optimal heat regulation. This study introduces two control mechanisms for the PTC system and comparing their efficacy. A classical PI controller supplemented by Static Feed-Forward (SFF) control demonstrates improved performance with an optimal transfer function model. In contrast, Nonlinear Model Predictive Control (NMPC) excels in disturbance rejection and setpoint tracking, considering operational constraints. The NMPC controller notably outperforms the PI controller with SFF in performance metrics, with the Integral Time Squared Error (ITSE) decreasing by approximately 99% across case studies. Also, in the case of heat gain, NMPC controller exhibits percentage increases when compared to the PI controller. Furthermore, since the measurement of optical efficiency is essential, but due to difficulty in the measurement for various reasons, the estimator is used as a virtual sensor to estimate those optical efficiency. The unmeasured states and parameters, including optical efficiencies of the PTC, are estimated using Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) techniques, with UKF exhibiting superior accuracy. However, considering the average computation time, EKF is preferred for real-time applications.

Efficient control scheme development for a highly integrated multi-component distillation process

This research aims to address intricate control challenges in a highly integrated multi-component distillation process within a polyolefin elastomers plant. The studied process incorporates various process intensification techniques into a unified unit, resulting in strong interactivity and presenting unique control challenges. To tackle these challenges, a rigorous mathematical model of the distillation process is established, and index reduction is employed to handle the high-index DAE system. Various control structures and algorithms are utilized to comprehensively assess their feasibility and effectiveness in disturbance rejection, setpoint tracking, and handling inaccurate measurements. The control structures include temperature inferential control, temperature-composition hybrid control, and composition control. The control algorithms encompass PI control, linear model predictive control (LMPC), and nonlinear model predictive control (NMPC). Results indicate that both temperature-composition hybrid control and composition control exhibit comparable and superior performance. This suggests that temperature-composition hybrid control is an effective and cost-efficient configuration for complex distillation systems. The dynamic responses of MPC outperform those of PI control, as evidenced by a reduction in the integral of the absolute error. NMPC exhibits the most outstanding performance in all scenarios, showcasing the smallest maximum deviations and shortest settling times.

An active disturbance rejection stair-like generalized predictive control with time delay weakening for denitrification process in thermal power unit

The optimization control of the selective catalytic reduction (SCR) denitrification system holds significant importance in the production of the power generation industry. This paper presents an active disturbance rejection stair-like generalized predictive control with time delay weakening (WADRC-SGPC) to enhance the control performance in SCR denitrification system. Compared with classical generalized predictive control (GPC), active disturbance rejection generalized predictive control (ADRC-GPC) combines the advantages of ADRC, uses Extended State Observer (ESO) to compensate the uncertainty of the system, and regards the compensated system as a series integrator. Therefore, it has the advantages of no system identification and online calculation of Diophantine equation. In addition, a time delay weakening link is incorporated for the known part of the system model, which effectively improves the performance of the controller. The parameter selection issue of the weight function in time delay weakening is addressed using a hybrid strategy whale optimization algorithm. Finally, the superior performance of the proposed control strategy in terms of set-point tracking and disturbance rejection is verified through simulation experiments.

FOPDT model and CHR method based control of flywheel energy storage integrated microgrid

The main causes of frequency instability or oscillations in islanded microgrids are unstable load and varying power output from distributed generating units (DGUs). An important challenge for islanded microgrid systems powered by renewable energy is maintaining frequency stability. To address this issue, a proportional integral derivative (PID) controller is designed in this article. Firstly, islanded microgrid model is constructed by incorporating various DGUs and flywheel energy storage system (FESS). Further, considering first order transfer function of FESS and DGUs, a linearized transfer function is obtained. This transfer function is further approximated into first order plus time delay (FOPTD) form to design PID control strategy, which is efficient and easy to analyze. PID parameters are evaluated using the Chien-Hrones-Reswick (CHR) method for set point tracking and load disturbance rejection for 0% and 20% overshoot. The CHR method for load disturbance rejection for 20% overshoot emerges as the preferred choice over other discussed tuning methods. The effectiveness of the discussed method is demonstrated through frequency analysis and transient responses and also validated through real time simulations. Moreover, tabulated data presenting tuning parameters, time domain specifications and comparative frequency plots, support the validity of the proposed tuning method for PID control design of the presented islanded model.

Real‐Time Model Predictive Control of Air‐Conditioners Through IoT—Results From an Experimental Setup in a Tropical Climate

ABSTRACTThere is an increasing demand for air‐conditioners (ACs) to maintain a comfortable indoor environment for all types and sizes of buildings including commercial, industrial, and office spaces. Such ACs are mostly operated by traditional ON–OFF controllers to maintain a temperature setpoint. Extensive control engineering knowledge resulting from experiments on actual buildings is needed before the wide application of an advanced control methods, such as model predictive control (MPC), which are more effective and energy‐efficient than the traditional controllers. Simulation studies on the application of control may provide promising results, but the corresponding experimental validations may prove otherwise. User‐friendly experimental setups to investigate the performance of real‐time advanced control on an actual building and its HVAC system is scarce. This paper details the design, implementation and testing of a user‐friendly, remote and autonomous test bed to acquire measured data through an IoT platform, and to control ACs in real time through MATLAB Thingspeak. Measurement and data acquisition equipment are installed in a two‐zone concrete building in Mauritius. MPC of the indoor air temperature achieved an AC temperature setpoint tracking RMSE which was 0.7°C lower than that achieved by the built‐in ON/OFF AC control. The test bed also revealed that portable air‐conditioners are not very efficient, given that the maximum cooling efficiency achieved in this work was only 60%. It also provided valuable insights based on the experiments carried out, in terms of improvements to sensing and data acquisition. Control engineers can implement such a test bed for the development and application of advanced controllers as per their needs and applications.

State feedback control of HPR1000 average coolant temperature based on dominant pole

Control of nuclear power plant is still based on the traditional PID control system, which is difficult to obtain high control quality in the process of a wide range of load changes. To effectively use the measurable information of the system and consider the constraints, state feedback control based on the dominant pole method is proposed for the average coolant temperature control of HPR1000. The control system is divided into two parts: one part is a feedback branch, which realizes the state feedback by using the measurable system state quantity including the core inlet temperature, the core outlet temperature and the reactor power, and at the same time introduces the integral link to reduce the steady-state error; the other part is a feedforward branch, which uses the nominal load change to make feedforward compensation to improve the control performance of load tracking. At the same time, Particle Swarm Optimization (PSO) method is used to optimize the controller parameters, and the dominant pole meeting the requirements is obtained. The control performance under different working conditions is verified on the HPR1000 model. The test results show that the state feedback control can effectively improve the setpoint tracking ability and anti-disturbance ability.

Adaptive neural network control of a centrifugal chiller system

In this paper, a neuro-adaptive PI control strategy for a water-cooled centrifugal chiller system is developed, and its control performance is examined. The system model consists of a flooded evaporator, a flooded condenser, a centrifugal compressor, and an electronic expansion valve. The overall system consists of three control loops: a compressor speed control loop, an inlet guide vane (IGV) control loop, and a condenser liquid level control loop. A neuro-adaptive control strategy for compressor speed control was designed. The control performance of the chiller was tested by carrying out simulation runs using an integrated building and HVAC (IB-HVAC) system model. The major details of the IB-HVAC model are described in this paper. Results show that the neuro-adaptive controller can adapt to new system dynamics of the IB-HVAC system and give good setpoint tracking responses under a wide range of operating conditions. The results show that the neuro-adaptive controller performs better than the constant gain PI controller in terms of speed of response and setpoint tracking properties.

Closed-loop Speed Control for a Three-Phase Alternating-Current Motor using a Modbus Network

This paper presents a closed-loop controller for speed regulation of a three-phase induction motor by means of a Modbus network, which is compound only by industrial-purpose hardware, namely two programmable logic controllers, a human-machine interface and a variable-frequency drive. This represents a step beyond the state of the art because the results in similar works reported in the literature were obtained using personal computers, academic-purpose hardware or a combination of both academic-industrial technologies. The speed regulation was achieved through a proportionalintegral controller executed in a programmable logic controller, whose feedback was supplied by another similar device completely dedicated to read and decode an industrial optical encoder; furthermore, the control signals were commanded to the alternating current motor using a variable-frequency drive. The proposed control network was tested through diverse regulation experiments, under different conditions, in order to assess its effectiveness and robustness. The obtained results were successful in terms of accuracy of the speed set-point tracking in comparison to the most similar works of the state of the art, namely zero steady-state error was achieved, 12 seconds earlier for 75% larger references and 22% less RMSE than other works, which are the best results reported so far. Such successful achievements encourage the later implementation of more advanced control techniques on three-phase-motor based industrial machinery using only industrial-purpose hardware.

A Simple modification to the Smith Predictor Structure for dealing with high-order delayed processes considering one unstable pole

The traditional Smith Predictor (SP) is restricted to dealing with stable plants. In this paper, a slight modification of the SP is proposed in order to control unstable plants: systems of any order but with one unstable pole are tackled. In fact, many modifications to the SP can be found in the literature dealing with this kind of system, but none, to our best knowledge, have the simplicity of the structure here proposed. Only one or two gains are added to the traditional SP structure to achieve the stabilization of this kind of unstable system. A simple relation states the necessary and sufficient condition guaranteeing the existence of stabilizing gains, in terms of the location of the unstable pole and the size of the delay term. The range of values for the gains solving the problem is characterized. In addition, the tracking of setpoints and disturbance rejections are analysed. Some numerical examples are presented to illustrate the effectiveness of the proposed strategy, as well as one real-time example.

A hierarchical multi-parametric programming approach for dynamic risk-based model predictive quality control

In this work, we present a hierarchical batch quality control strategy with real-time process safety management. It features a multi-time-scale decision-making framework augmenting: (i) Risk-aware model predictive controller for short-term set point tracking and dynamic risk control under disturbances; (ii) Control-aware optimizer for long-term quality and safety optimization over the entire batch operation; (iii) Intermediate surrogate model to bridge the timescale gap by readjusting the optimizer operating decisions for the controller. All of the above problems are solved via multi-parametric mixed-integer quadratic programming with a key advantage to generate offline explicit control/optimization laws as affine functions of process and risk variables. This allows for the design of a fit-for-purpose risk management plan prior to real-time implementation, while reducing the need for repetitive online dynamic optimization. A unified process model is used to underpin the consistency of hierarchical operational optimization. The proposed approach offers a flexible strategy to integrate distinct decision-making time scales which can be selected separately tailored to the process-specific need of control, fault prognosis, and end-batch quality control. A T2 batch reactor case study is presented to showcase this approach to systematically address the interactions and trade-offs of multiple decision layers toward improving process efficiency and safety.

Nonlinear model predictive control for mode‐switching operation of reversible solid oxide cell systems

AbstractSolid oxide cells (SOCs) are a promising dual‐mode technology for the production of hydrogen through high‐temperature water electrolysis, and the generation of power through a fuel cell reaction that consumes hydrogen. Switching between these two modes as the price of electricity fluctuates requires reversible SOC operation and accurate tracking of hydrogen and power production set points. Moreover, a well‐functioning control system is important to avoid cell degradation during mode‐switching operation. In this article, we apply nonlinear model predictive control (NMPC) to an SOC module and supporting equipment and compare NMPC performance to classical proportional‐integral (PI) control strategies, while switching between the modes of hydrogen and power production. While both control methods provide similar performance across various metrics during mode switching, NMPC demonstrates a significant advantage in reducing cell thermal gradients and curvatures (mixed spatial‐temporal partial derivatives), thereby helping to mitigate long‐term degradation.