Multivariable Control Strategy Research Articles

A multivariable control strategy based on fuzzy logic interference rule for a solar-driven, direct air-cooled H[formula omitted]O-LiBr absorption chiller

The operation of a solar-driven H2O-LiBr Absorption Chiller (AbC) is subjected to various limitations regarding its ability to satisfy efficiently the required cooling demand while avoiding certain internal conditions, such as H2O-LiBr solution crystallization and water freezing. These circumstances would lead to problematic situations and jeopardize the continuous operation of the system. Thus, the development of accurate control strategies is an issue of interest. A new operational control strategy approach is introduced and tested through simulation studies, by employing a transient model of the entire solar cooling application. A prototype under development is used for the parametrization of the direct air-cooled AbC model used in the simulations. The developed control strategy aims to minimize the electrical consumption of the auxiliary components of the AbC (i.e., fan, pumps) during the partial load operation period, by using a fuzzy logic interference rule for determining the cooling capacity demand and then applying an optimization algorithm for determining the velocity of the auxiliary components. Within the proposed control strategy, a fast-calculation model is embedded, which predicts the chiller’s capacity under variable external conditions and partial load operation. The electrical COP is increased up to 2.2 times with respect to a baseline case, based on a basic two-step control strategy, while allowing the chiller to operate smoother, minimizing the crystallization and freezing risk.

Optimize energy storage performance of NaNbO3 ceramics by multivariable control strategy

NaNbO3 ceramics have broad application prospects in ultra-high power electronic systems, electromagnetic pulses, and other applications. In this study, (1-y)[(1-x)NaNbO3-xSmMg0.5Zr0.5O3]-y(Bi0.5Na0.5)0.7Sr0.3TiO3 (x = 0.06, 0.08 and 0.10)(y = 0.30, 0.35, 0.40 and 0.45) ceramics were fabricated. The effects of the incorporation of SmMg0.5Zr0.5O3 (SMZ) and (Bi0.5Na0.5)0.7Sr0.3TiO3 (BNST) on the energy storage performance, impedance, actual charge and discharge capacity, phase structure and microstructure of the ceramic samples were analyzed. Finally, when x = 0.08, and y = 0.35, the recoverable energy storage density of the sample was 5.8 J/cm3 and a high energy storage efficiency of 88 % was obtained at 545 kV/cm. In addition, the ceramic maintains a stable actual charge and discharge capacity and an extremely fast discharge time (16.1 ns) in a wide temperature range (20–160 °C). These excellent energy-storage performances may be due to the fine grain size and large relaxation and activation energies.

Model predictive control of a dual fluidized bed gasification plant

Dual fluidized bed (DFB) gasification is a promising method for producing valuable gaseous energy carriers from biogenic feedstocks as a substitute for fossil fuels. State-of-the-art DFB gasification plants mainly rely on manual operation or single-input single-output control loops, and scientific contributions only exist for controlling individual process variables. This leaves a research gap in terms of comprehensive control strategies for DFB gasification. To address this gap, we propose a multivariate control strategy that focuses on crucial process variables, such as product gas quantity, gasification temperature, and bed material circulation rate. Our approach utilizes model predictive control (MPC), which enables effective process control while explicitly considering process constraints. A simulation study is given demonstrating how different MPC parametrizations influence the behavior of the closed-loop system. Experimental results from a 100kW pilot plant at TU Wien demonstrate the successful control achieved by the proposed control algorithm.

A practical solution for multivariable control of temperature and humidity in greenhouses

This paper presents a simple and effective control solution to regulate temperature and humidity inside a greenhouse, which are the main climatic variables affecting crop growth and health. A multivariable control strategy with inverted decoupling is proposed in two schemes. The nighttime control scheme uses heating and dehumidification, and the daytime control scheme uses natural ventilation, dehumidification, and humidification. A system identification methodology is used to obtain low-order linear models from data measured in a greenhouse. This simplifies the design of proportional–integral (PI) controllers and allows the use of well-known control techniques. For instance, anti-windup is used to deal with the saturation of the actuators and for a bumpless transfer mechanism to switch between the nighttime and the daytime control schemes. Setpoint weighting is used for the humidity controllers to attenuate undesired effects on the temperature control loop when the humidity setpoint is changed. Furthermore, split range control is used for the first time to control humidity by combining a heat-pump dehumidifier and a fogging system. The control solution presented in this work allows the integral absolute error to be reduced, on average, by 88.5% for the interactions affecting the temperature control loop, and by 85.6% for the humidity control loop, compared to decentralized PI control. The control schemes have been tested in simulation with real disturbance data from a Mediterranean greenhouse and in preliminary form in a real facility to conclude that the proposed multivariable control strategy could be effective in decoupling the temperature and the humidity control loops.

Research on the New Hydrostatic Transmission System of Wheel Loaders Based on Fuzzy Sliding Mode Control

To improve the control performance and energy efficiency of the hydrostatic transmission (HST) system of wheel loaders, this study proposes a novel variable structure dual-motor hydrostatic transmission system, accompanied by a multivariable control strategy based on fuzzy sliding mode control. By constructing a simulation model of the transmission system, the performance of the novel transmission system is verified, and the control effects of common control strategies are compared. The simulation results show that the proposed control strategy and system have good matching and can adapt to disturbances actively. Field experiment results show that the proposed solution can achieve smooth automatic shifting of wheel loaders, while effectively adjusting system pressure, flow, and power. Compared with the traditional hydrodynamic torque converter loader transmission system, its transmission efficiency is effectively increased by 30%, and the efficiency is increased by 8% compared with the traditional hydrostatic transmission system This achievement is of great significance for improving the working efficiency of wheel loaders and reducing energy consumption. The future research focus is to further improve the multivariable control strategy, achieve intelligent operation, and improve the efficiency and reliability of the transmission system.

Design of Multivariable PID Control Scheme for Humidity and Temperature Control of Neonatal Incubator

Design of Multivariable PID Control Scheme for Humidity and Temperature Control of Neonatal Incubator

On positive systems with multivariable positive control: Process stabilization

The process stabilization is tackled from the perspective of a family of positive systems with multivariable positive control. This framework is consistent because each of the n state variables and m control inputs in chemical processes is in R+. The aim is to provide multivariable positive feedback control schemes which take values in the hyperbox U∈R+m; while sufficient conditions are satisfied to ensure the stabilization process. Two approaches are discussed: the first is a sliding mode control based on hyperplanes intersecting a (n−m)-dimensional segment of the state space which comprises the equilibrium point; such scheme guarantees robust stability. The second is a continuous feedback stabilizer based on Lyapunov functions, which enhances convergence rate while stabilization is ensured. The process stabilization is shown with two examples: a heat exchanger and an isothermal continuous stirred tank reactor. Both examples illustrate the usefulness of the proposed framework of multivariable positive control.

Collective large-scale wind farm multivariate power output control based on hierarchical communication multi-agent proximal policy optimization

Wind power is becoming an increasingly vital source of renewable energy worldwide. However, controlling power generation in wind farms faces significant challenges due to the inherent complexity of these systems. To address this issue and maximize power output, we propose a novel communication-based multi-agent deep reinforcement learning approach for large-scale wind farm control. We introduce a multivariate power model for wind farms to analyze the impact of wake effects on power output. This model considers controllable variables such as axial induction factor, yaw angle, and tilt angle. To coordinate the continuous controls in large-scale wind farms, we propose the hierarchical communication multi-agent proximal policy optimization (HCMAPPO) algorithm. The wind farm is divided into multiple wind turbine aggregators (WTAs), and neighboring WTAs can exchange information through hierarchical communication to optimize power output. Our simulation results demonstrate that the multivariate HCMAPPO approach significantly increases wind farm power output compared to traditional PID control, coordinated model-based predictive control, and the multi-agent deep deterministic policy gradient algorithm. The HCMAPPO algorithm can be trained using a thirteen-turbine wind farm environment and effectively applied to large-scale wind farms. Importantly, as the wind farm scale increases, there is no significant increase in wind turbine blade fatigue damage from wake control. This multivariate HCMAPPO control strategy enables the collective maximization of power output in large-scale wind farms.

Event-triggered robust model reference adaptive control for drag-free satellite

Aiming at the requirement of bounded disturbance suppression for low-frequency space gravitational wave detection satellite, a robust model reference adaptive drag-free control scheme is proposed in this paper via linear matrix inequalities (LMIs) approach. The multivariable model reference adaptive control (MRAC) scheme is applied to the drag-free control system with parameter uncertainties, which realizes the adaptive tracking to the reference state. The LMI system is established as an adaptive compensation term, which provides robustness against nonlinear disturbances. To reduce the communication burden of the actuation information and further save the total energies, an event-triggered mechanism (ETM) is introduced, with both the actuation inputs and the adaptive laws of the feedback gains updated only at the triggering time instants. The ultimately uniformly boundedness of the closed-loop signals is proved by Lyapunov analysis, with each system state convergence. From the feasibility analysis, Zeno behavior can be further proved to be strictly excluded. The numerical simulation verifies that the proposed scheme has efficient robustness to nonlinear disturbances with low energy cost, and achieves good effect in response to the requirements of space gravitational wave detection mission.

Motion-Tracking Control of Mobile Manipulation Robotic Systems Using Artificial Neural Networks for Manufacturing Applications

Robotic systems have experienced exponential growth in their utilization for manufacturing applications over recent decades. Control systems responsible for executing desired robot motion planning face increasingly stringent performance requirements. These demands encompass high precision, efficiency, stability, robustness, ease of use, and simplicity of the user interface. Furthermore, diverse modern manufacturing applications primarily employ robotic systems within disturbed operating scenarios. This paper presents a novel neural motion-tracking control scheme for mobile manipulation robotic systems. Dynamic position output error feedback and B–Spline artificial neural networks are integrated in the design process of the introduced adaptive robust control strategy to perform efficient and robust tracking of motion-planning trajectories in robotic systems. Integration of artificial neural networks demonstrates performance improvements in the control scheme while effectively addressing common issues encountered in manufacturing environments. Parametric uncertainty, unmodeled dynamics, and unknown disturbance torque terms represent some adverse influences to be compensated for by the robust control scheme. Several case studies prove the robustness of the adaptive neural control scheme in highly coupled nonlinear six-degree-of-freedom mobile manipulation robotic systems. Case studies provide valuable insights and validate the efficacy of the proposed adaptive multivariable control scheme in manufacturing applications.

Evolutionary algorithm-based model predictive control for a reactive distillation column in biodiesel production

Biodiesel is touted to be an alternative to the fossil fuels as it is conducive to the environment. This investigation proposes a control framework to produce biodiesel in a reactive distillation column via a transesterification process. To extract quality product, the temperature profile must be maintained along the column as per the requirements. However, constant interactions among the products inside the column disturb the temperature profile and consequently the product quality. Therefore, this investigation treats the process as a single input and single output system, where in the process interactions are modelled as disturbances. A model predictive controller (MPC) is designed for the proposed system to ensure product quality. The MPC parameters must be selected appropriately to ensure optimal performance. In this regard, to tune the MPC parameters optimally, we use two evolutionary algorithms namely, the real coded genetic algorithm (RGA) and the bio-geography based optimization algorithm (BBO). The results indicate the proposed control strategy provides offset free set point tracking when compared to the multivariable control strategy employed using the MPC algorithm. Among the two evolutionary controllers used for tuning the MPC parameters, the RGA MPC controller provides a satisfactory performance.

Multi-variable extremum seeking control for high temperature heat pump system incorporating double refrigerant injection

The use of refrigerant injection technology to enhance the heating efficiency of heat pumps with larger temperature lifts is becoming more common. Heat pumps with double refrigerant injection loops have better energy efficiency than traditional ones with single refrigerant injection loops. A multi-variable extremum seeking control (ESC) strategy for a high-temperature heat pump system with double refrigerant injection technology is proposed in this study. A Modelica-based dynamic simulation model of the considering system is established in Dymola, and a co-simulation with the proposed ESC strategy is built to validate the effectiveness of this control strategy to minimize power consumption. The strategy uses compressor power consumption as the feedback. The effective throttling area of the upper and lower EEVs is adjusted by a PI controller to manipulate the low and high-pressure vapor injection pressures. Three scenarios were simulated for control: stable, step curve, and freely variable heat source temperature conditions. The simulation results demonstrate that the multi-variable ESC has excellent convergence performance and confirm its effectiveness in searching for the optimal operating point.

Residual-triggered threshold decision and performance self-healing control for wastewater treatment process

The delicate control of effluent quality in wastewater engineering becomes imperative for fulfilling its role in protecting the ecological environment and promoting the water recycling. In this paper, an adaptive multivariable self-healing control strategy for the wastewater treatment process (WWTP) operating under complicated environments and facing abnormal conditions. The control goal is to meet the strict effluent standards of the WWTP and to optimize the abnormal conditions by analyzing the process operating status in the event of sudden sensor failures. Therein, a new residual-triggered threshold decision method is designed and integrated into a dynamical detection mechanism to identify such sensor failures. After locating the failures, a multivariable self-healing control scheme incorporating failure compensation information is activated to guarantee the process output tracking and improve the self-recovery capability of faulty performance in the WWTP. Extensive experiments are carried out on a standardized wastewater treatment platform, and the regulation results on the key component concentrations indicate that the proposed control scheme can ensure excellent control performance and reduce the impact of sensor failures.

Artificial neural network based identification of process dynamics and neural network controller design for continuous distillation column

In this study, an equation based nonlinear autoregressive with exogenous input neural network model for a multiple input and output process was proposed to enhance the accuracy of the model, a control system’s speed, and robustness. A continuous lab-scale binary distillation column was considered for a case study, and an experiment was conducted. The forward and inverse neural network models have been developed and incorporated into a multivariable internal model control strategy to control the distillate and bottom compositions of the column simultaneously. These models demonstrated excellent performances, as supported by lower mean square error values of 2.73E-05 and 1.26E-04 for the forward and inverse models. Then, the proposed control method was applied to control both set point changes and disturbance changes, and in each case, it is contrasted with the traditional proportional-integral-derivative (PID) technique. The integrated absolute and integrated square errors for the proposed control were 0.1603 and 2.32E-03, respectively, while for PID control, these performance indexes were 0.7822 and 8.67E-03, respectively, for set-point tracking in top composition. Similarly, for bottom composition in tracking set points and rejecting disturbance, these performance indexes were also very less for the neural network control scheme.

Add-on Harmonic Disturbance Cancellation Control in Continuous Hot-Dip Galvanizing Lines

In continuous hot-dip galvanizing of steel strips, unwanted vibrations of the processed strip can significantly degrade the coating uniformity and quality. Thus, an increasing number of hot-dip galvanizing lines are equipped with electromagnetic strip stabilization devices to enable active strip-shape and vibration control. Typically, commercial strip stabilizers achieve adequate broad-band vibration attenuation. However, their disturbance rejection capabilities are usually insufficient in case of persistently exciting harmonic disturbances. This work proposes a new multivariable harmonic disturbance cancellation control strategy which can be used as add-on to the position control loop of an existing strip stabilization device. The unknown amplitude, phase angle, and disturbance frequency are estimated during online operation. The systematic consideration of controller-output and estimator constraints in the design guarantees a reliable and safe operation in the challenging industrial environment. The devised control system works well even in the case of non-persistently exciting harmonic disturbances. The effectiveness of the proposed control scheme is demonstrated by short- and long-term measurement results from an industrial hot-dip galvanizing line.

Partial State Feedback MRAC Based Reconfigurable Fault-Tolerant Control of Drag-Free Satellite With Bounded Estimation Error

Aiming at the ultra-precision and ultra-stable requirements of drag-free control in space detection missions, a multivariable model reference adaptive control (MRAC) scheme is proposed in this paper based on partial observation state, to provide adaptive suppression of uncertain disturbances and improve detection accuracy. The MRAC scheme utilizes model output matching with partial state containing uniform and bounded observation error, and estimates the unknown state parameters through the adaptive law and high-frequency gain decomposition. In response to the actuator bias fault of the drag-free satellite, a set of state error iterative convergence sequence in the actuator loop is established based on the sequence Lyapunov analysis, which is further introduced into the reconfiguration control input to achieve the fault-tolerant target without changing the nominal controller. Through the Lyapunov stability analysis, the convergence of the closed-loop system states in the presence of actuator faults and observation errors is obtained. Numerical simulation results for a 6-DOF drag-free control system demonstrate the effectiveness of the proposed MRAC-based reconfigurable fault-tolerant control scheme.

Optimizing Surplus Heat Recovery using Fast Fourier Transform-based Extremum Seeking Control

In large industrial parks, the surplus heat generated by the different manufacturing and processing plants can be recovered and utilized (e.g. for district heating) using a network of heat exchangers. Efficient management of the heat exchanger network is an important factor in maximizing the surplus heat recovery. This is accomplished by adjusting the flow of cold stream through the heat exchangers as the amount of heat generated by the different heat sources vary over time. Traditional real time optimization typically requires detailed process models, production schedules, and real-time process measurements in order to optimize the process. However, in an eco-industrial park with multiple heat sources operated by different companies, data sharing may not be desirable. This paper proposes the use of a multivariable extremum seeking control scheme using fast Fourier transform (FFT) to optimize waste heat recovery using only a single temperature measurement, measured at the end of the heat exchanger network. By mapping the time series data into a series of frequencies, we show that FFT allows us to easily extract the effect of different input perturbations from the single temperature measurement, and use it to optimize surplus heat recovery from an industrial cluster.

Multivariable control of nighttime temperature and humidity in greenhouses combining heating and dehumidification

This work presents a multivariable control strategy to regulate nighttime temperature and relative humidity inside a greenhouse, using a pipe heating system and a dehumidification system. The greenhouse is modeled as a two-input two-output (TITO) process with a system identification methodology to calculate low-order linear models. Proportional-integral (PI) controllers with anti-windup are used, and an inverted decoupling scheme is adopted. The strategy was tested in simulation with real data. Results suggest that it is attractive for real implementation and show for the first time that the use of dehumidification systems can be applied as part of a combined control strategy.

HVAC multivariable system modelling and control

Heating, Ventilation and Air Conditioning (HVAC) is a multivariable process where any alteration with one system input affects most or all of the system’s outputs simultaneously. Owing to its comprehensiveness, a readily derived multivariable HVAC mathematical model is selected for this work, mainly a hybrid distributed-lumped parameters model. As the transfer function matrix was not established in the selected HVAC model, it was exclusively developed in this study, using the time domain graphical responses of the chosen model. Based on the developed transfer function matrix, a conceptual two-step approach was followed to control HVAC model performance. The first was decoupling the interactions that affect all the system outputs, and the second was designing proper PID controllers for each decoupled loop similar to those used for single input single output (SISO) systems. A direct Nyquist Array (DNA) multivariable control strategy was used for this purpose and successfully decoupled the HVAC system into three separate (SISO) loops. Three PID controllers afterwards were applied for each decoupled loop. The results showed quite decoupled system outputs with a minor coupling percentage so that any change in a system input only affected the corresponding system output. The output responses are also underdamped with almost zero steady-state error confirming the effectiveness of the selected PID parameters. The values of steady-state responses are obtained in (10–15) s compared with (200–600) s of open-loop response time. However, various overshoot percentages in the responses are encountered but are relatively small, with a short settling time, so they don’t affect the thermal comfort of the ventilated volume. System stability using the Nyquist criterion has also been examined and found to satisfy the criterion. The multivariable DNA control technique and the SISO closed-loop PID controllers have shown the capability to suppress external disturbances and restore the system to its original functional steady-state values.

A Continuous Multivariable Finite-Time Control Scheme for Double Integrator Systems With Bounded Control Input

A multivariable finite-time control algorithm is proposed for double integrator systems with matched disturbances. The remarkable feature of the developed method is to ensure the finite-time stability of the closed-loop system with uniformly bounded control signal with respect to arbitrary initial conditions. The convergence criteria are derived by using the Lyapunov-based techniques, and the upper bound of the control signal can be calculated based on the obtained criteria. Finally, the effectiveness of the proposed algorithm is confirmed by some numerical simulations.