Multi-output Control Research Articles

Inverse system based decoupling design and control strategy for crude oil heat exchanger networks

This work focuses on a decoupling control approach for the crude oil heat exchanger network to improve the energy efficiency and achieve continuous energy saving. The desired control variables are identified by the signed directed graph (SDG) model based on the complex network theory. Then the principal component analysis (PCA) method and the evaluation index are employed to construct the initial controller as well as the control variables pairing. The presented system is a multi-input multi-output (MIMO) control system where the coupling problems are considered, and a decoupling control strategy based on the inverse system theory of neural network is proposed. The existence of the inverse system is proved by the reversibility derivation, and the original system is decoupled into two second-order subsystems. Consequently, the proposed decoupling control system is tested on the example of heat exchanger network (HEN) belonging to Crude Distillation Unit. The fluctuating value of temperature is reduced averagely by 57.1% compared with the non-decoupling control strategy and the mean absolute percentage error is within 0.02 under different operating conditions. The results show that the proposed decoupling control method has excellent decoupling performance and can improve the control performance of the system.

Optimal Design of I-PD and PI-D Industrial Controllers Based on Artificial Intelligence Algorithm

This research aims to apply Artificial Intelligence (AI) methods, specifically Artificial Immune Systems (AIS), to design an optimal control strategy for a multivariable control plant. Two specific industrial control approaches are investigated: I-PD (Integral-Proportional Derivative) and PI-D (Proportional-Integral Derivative) control. The motivation for using these variations of PID controllers is that they are functionally implemented in modern industrial controllers, where they provide precise process control. The research results in a novel solution to the control synthesis problem for the industrial system. In particular, the research deals with the synthesis of I-P control for a two-loop system in the technological process of a distillation column. This synthesis is carried out using the AIS algorithm, which is the first application of this technique in this specific context. Methodological approaches are proposed to improve the performance of industrial multivariable control systems by effectively using optimization algorithms and establishing modified quality criteria. The numerical performance index ISE justifies the effectiveness of the AIS-based controllers in comparison with conventional PID controllers (ISE1 = 1.865, ISE2 = 1.56). The problem of synthesis of the multi-input multi-output (MIMO) control system is solved, considering the interconnections due to the decoupling procedure.

A new multi-input multi-output control design method for a large-scale web transport system

The paper introduces an innovative multi-input multi-output (MIMO) control design approach tailored for the complex dynamics of large-scale web transport systems in printed electronics manufacturing technology. First, a novel hybrid control design is proposed to a MIMO strict-feedback nonlinear dynamic system, in which a modified tracking error is introduced in term of a sliding surface for design purpose. Then, a combined use of Lyapunov-based control design and sliding mode control is employed to formulate a novel control law, called Backstepping Sliding Mode Control (BSMC) and a BSMC based control structure is provided. Second, application of this control strategy is implemented to control web tension and velocity of a three-span web transport system. In this study, a series of assumptions are provided to transform an original system dynamics of web transport system into a standard strict-feedback form. Then, the proposed theory is applied for the achieved system, a BSMC for the system is synthesized for simulation study. Finally, numerical simulations are conducted under different operating conditions to validate the effectiveness, reliability, and robustness of the proposed control method.

Multi-channel decentralized decoupling FxLMS algorithm and active vibration control experiment

When vibrations generated by marine machinery propagate through a ship’s hull into the ocean, they produce low-frequency radiated noise with distinct “acoustic fingerprint” characteristics. This noise, characterized by stable and concentrated energy, long transmission distances, and difficulty in elimination, becomes the primary target for enemy sonar detection. Active vibration isolation serves as a critical method for reducing low-frequency vibrations in ships and enhancing their acoustic stealth performance. However, control challenges persist, including multi-frequency excitation, frequency fluctuation, multi-channel coupling, and slow convergence speed. To address these issues, this paper introduced an innovative multi-channel decentralized decoupling filtered-x least mean square (DMFxLMS) algorithm. Firstly, a recursive least squares identification algorithm with a forgetting factor was proposed, taking into account the characteristics of single-input, multi-output and multi-input, and multi-output control systems, effectively enhancing the algorithm’s convergence speed and control accuracy. Secondly, based on the decentralized decoupling control concept, the multi-channel control system was simplified into parallel single-channel control loops. The control weight coefficient updates were only related to adjacent error signals, significantly reducing the algorithm’s computational complexity. Thirdly, an anti-impact link was designed to improve the algorithm’s robustness, considering the interference caused by other mechanical equipment during the control process. The influence of abnormal error signals in the control weight coefficient correction term was suppressed, and a percentage function was introduced to limit the output signal. Finally, the feasibility and effectiveness of the DMFxLMS algorithm were verified through simulations and experiments. The results demonstrated that the DMFxLMS algorithm achieved significant control effects for both constant frequency line spectrum excitation and frequency fluctuating line spectrum excitation, fulfilling the objective of reducing base vibration. The DMFxLMS algorithm exhibited fast convergence and excellent robustness, making it suitable for practical engineering applications.

Kinematics, dynamics and control of stiffness-tunable soft robots

Modeling and control methods for stiffness-tunable soft robots (STSRs) have received less attention compared to standard soft robots. A major challenge in controlling STSRs is their infinite degrees of freedom, similar to standard soft robots. In this paper, demonstrate a novel STSR by combing a soft-rigid hybrid spine-mimicking actuator with a stiffness-tunable module. Additionally, we introduce a new kinematic and dynamic modeling methodology for the proposed STSR. Based on the STSR characteristics, we model it as a series of PRP segments, each composed of two prismatic joints(P) and one revolute joint(R). This method is simpler, more generalizable, and more computationally efficient than existing approaches. We also design a multi-input multi-output (MIMO) controller that directly adjusts the pressure of the STSR’s three pneumatic chambers to precisely control its posture. Both the novel modeling methodology and MIMO control system are implemented and validated on the proposed STSR prototype.

Harmonic Suppression Strategy for Dual Three-phase Permanent Magnet Synchronous Machines Based on a Multi-Input Multi-Output Controller

Harmonic Suppression Strategy for Dual Three-phase Permanent Magnet Synchronous Machines Based on a Multi-Input Multi-Output Controller

Systematic design of a multi-input multi-output controller by model-based decoupling: a demonstration on TCV using multi-species gas injection

In this paper, we present the first results of a systematically designed multi-input multi-output gas-injection controller on Tokamak á Configuration Variable (TCV). We demonstrate the simultaneous real-time control of the NII emission front position and line-integrated electron density using nitrogen and deuterium gas injection. Injection of nitrogen and/or deuterium affects both the NII emission front position and line-integrated electron density. This interplay between control loops is termed interaction and, when strongly present, makes designing a controller a significantly more complex problem. Interaction between the control loops can be reduced to an acceptable level by redefining inputs, decoupling the multi-input multi-output control problem to separated single-input single-output problems. We demonstrate how to achieve this by defining virtual control inputs from linear combinations of the actuators available. For the demonstration on TCV, linear combinations of deuterium and nitrogen gas injection are computed from transfer-function models to obtain these virtual inputs. The virtual inputs reduce the interaction in the control-relevant frequency range to a point where control of the NII emission front position and line-integrated electron density can be considered decoupled, allowing for the much simpler design of single-input single-output controllers for each loop. Implementing the controllers with the virtual inputs gives the multi-input multi-output gas-injection controller. This approach is well established in the control community, and is presented here as a demonstration to drive developments of multi-input multi-output control strategies. In particular, the envisioned control of particle- and heat fluxes impacting the divertor targets by injection of multiple gas species.

On-line automatic controller tuning of a multivariable grinding mill circuit using Bayesian optimisation

Process controllers are abundant in the industry and require attentive tuning to achieve optimal performance. While tuning controllers by the most primitive method of trial and error is possible, it often leads to sub-optimal performance if not conducted by a skilled expert. It is much more appealing to develop an on-line, sample efficient, automated tuner which can optimise the performance of a given controller to the task at hand. The automatic tuning procedure can be conducted during commissioning, when poor controller performance is observed or when process conditions have changed. The problem statement is formulated as the minimisation of an objective function constructed to achieve the desired controller performance. In this context the automatic tuning problem of multi-input multi-output (MIMO) controllers is considered within the framework of Bayesian optimisation and applied in simulation to an ore milling circuit with three manipulated and three controlled variables. Regulatory and set point tracking controllers are tuned automatically and are shown to achieve better performance than a reference controller.

Closed-loop modeling of central and intrinsic cardiac nervous system circuits underlying cardiovascular control.

The baroreflex is a multi-input, multi-output control physiological system that regulates blood pressure by modulating nerve activity between the brainstem and the heart. Existing computational models of the baroreflex do not explictly incorporate the intrinsic cardiac nervous system (ICN), which mediates central control of the heart function. We developed a computational model of closed-loop cardiovascular control by integrating a network representation of the ICN within central control reflex circuits. We examined central and local contributions to the control of heart rate, ventricular functions, and respiratory sinus arrhythmia (RSA). Our simulations match the experimentally observed relationship between RSA and lung tidal volume. Our simulations predicted the relative contributions of the sensory and the motor neuron pathways to the experimentally observed changes in the heart rate. Our closed-loop cardiovascular control model is primed for evaluating bioelectronic interventions to treat heart failure and renormalize cardiovascular physiology.

MIMO Multi-Frequency Active Vibration Control for Aircraft Panel Structure Using Piezoelectric Actuators

Undesirable vibration of aircraft panel structures has a significant impact on the safety of their occupants and the operational reliability of various systems. The traditional time-domain controller based on the parallel multi-frequency filtered-x least mean square (FxLMS) control algorithm is simple and effective. However, the mutual influence of convergence steps and the induced harmonic components will limit its performance. An improved parallel multi-input multi-output multi-frequency FxLMS (MIMOMF-FxLMS) algorithm is proposed, considering the multi-input multi-output (MIMO) control requirement of the aircraft panel structure active vibration control (AVC) system and its inevitable channel coupling phenomenon. The feedback error signal and the control channel model are processed by frequency division in the MIMOMF-FxLMS algorithm, so that the FxLMS algorithm of each control channel runs independently, effectively avoiding mutual interference between convergence steps and the generation of induced harmonic components. Simulation and experimental research on multi-frequency AVC of the panel structure model is performed using a new type of piezoelectric stack actuator. The test results show that the proposed controller reduces the vibration level of each observation point by more than 98%, and the vibration responses of the first two-order frequency component are significantly suppressed, which also has strong adaptability and anti-noise ability.

Machine learning-based ethylene and carbon monoxide estimation, real-time optimization, and multivariable feedback control of an experimental electrochemical reactor

Electrochemical reduction of CO2 gas is a novel CO2 utilization technique that has the potential to mitigate the global climate crisis caused by anthropogenic CO2 emissions, and enable the large-scale storage of energy generated from renewable sources in the form of carbon-based chemicals and fuels. However, due to the complexity of the electrochemical reactions, the explicit first-principles models for CO2 reduction are not available yet, and there has been a limited effort to develop process modeling, optimization and control of CO2 electrochemical reactors. To this end, a rotating cylinder electrode (RCE) reactor has been constructed at UCLA to understand the mass transfer and reaction kinetics effects separately on the productivity. In the RCE reactor, the applied potential strongly influences the reaction energetics and the electrode rotation speed affects the hydrodynamic boundary layer and modifies the film mass transfer coefficient, which involves convective and diffusive transport. The present work aims to develop a multi-input multi-output (MIMO) control scheme for the RCE reactor that integrates techniques from artificial and recurrent neural network modeling, nonlinear optimization, and process controller design. Specifically, production rates of two products from the experimental reactor, ethylene and carbon monoxide, are controlled by manipulating two inputs, applied potential and catalyst rotation speed. Process dynamics and controllability are analyzed, a feedback control strategy is designed and the controllers are tuned accordingly. The experimental electrochemical cell is employed to gather data for process modeling and implement the multivariable control system. Finally, the experimental results are presented which demonstrate excellent closed-loop performance by the control system and regulation of the outputs at three different set-points including an economically-optimal set-point.

Multi-Input Multi-Output Sliding-Mode Control of LCL-based Grid-Connected Modified Y-Source Inverter for Power Conditioning of Photovoltaic Generation

Multi-Input Multi-Output Sliding-Mode Control of LCL-based Grid-Connected Modified Y-Source Inverter for Power Conditioning of Photovoltaic Generation

State Feedback Reshaping Control of Voltage Source Converter

Admittance reshaping is a widely used strategy to address the converters low-frequency stability issues in weak grid, caused by the PLL and its interaction with the dc and ac voltage controls. However, the asymmetric control of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d$</tex-math></inline-formula> - and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula> -axis current references and the coupling between the converter ac and dc sides restricts the damping capability of single-input single-output feedbacks. This phenomenon gets even worse in the presence of nearby converters. This article extends the concept of admittance reshaping to multi-input multi-output control. A full state feedback is added to the current reference of the converter to increase the damping of the conventional multiloop control. A systematic offline algorithm is delegated to design the feedback, and a scalar coefficient is employed to activate/deactivate online the reshaping feedback, making the proposed solution user-friendly. The proposed control is analyzed both in time and frequency domains and tested in parallel-operation with other converters, and shows higher damping capability than conventional solutions and good robustness with respect to grid impedance and operating point variations. Experimental tests under ac and dc disturbances are conducted both in lab setup and in hardware-in-the-loop.

Virtual pre-test analysis for optimization of multi-channel control strategies in direct field acoustic testing

Direct field acoustic excitation testing, or DFAX, is gaining the attention of the space industry as a reliable and flexible method to perform acoustic qualification tests of spacecrafts. The test aims at reproducing the sound conditions of the space launch using an array of high-powered loudspeakers placed around the test specimen. To achieve this end, advanced multi-input multi-output (MIMO) control strategies are employed to design the independent signals which drive the different speakers based on the responses acquired at the control locations over the test volume. Consequently, the performance and overall efficiency of the test campaign is then determined by a number of factors, among them stack configuration, test specimen orientation, number of speakers, control matrix and sensor location. To assist engineers in the design of such tests, this work proposes the use of numerical models to simulate the entire test set-up before the on-site campaign takes place. State-of-the-art vibro-acoustic solvers are employed to predict the pressure responses at all points in the test volume for various design choices, allowing test engineers to perform a virtual pre-test analysis based on numerical solutions to optimize their control strategy. The results included in this work illustrate the methodology followed to account for all the transfer functions of the electro-acoustic system as well as the validation of the levels of accuracy which can be achieved in a full-scale DFAX test via direct comparison with experimental measurements.

Synthetic Lyapunov stabilization technique for designing actuation-constrained multi-input multi-output control systems

The main objective of this paper is to present a non-predictive method in the design of nonlinear multi-input multi-output (MIMO) control systems with the presence of constraints that are determinant in practical conditions, namely, the frequency bandwidth limitation of the actuation system and saturation boundaries in control commands. If these constraints are applied in the non-predictive control design problem, it is not possible to simultaneously satisfy Lyapunov stability and actuation constraints, analytically. Instead of model-predictive-based algorithms, which in most cases are computationally expensive, this paper proposes an algorithm based on synthetic Lyapunov stability. In this technique, by defining an intelligent filter applied to the system desired trajectories, defining intelligent proximity coefficients in decoupled inequalities resulting from Lyapunov stability, and determining the admissible boundaries of control commands, a space of regulatory parameters is generated. By appropriately adjusting these parameters based on statistical analysis conducted on the overall dynamics of the system, the Lyapunov stability is guaranteed, and the mentioned control constraints are not violated. In summary, the proposed control algorithm includes the formulation of discrete-time dynamics of sliding functions, the presentation of the procedure of defining and adjusting the control algorithm parameters with the proposed synthetic stability criterion, and the calculation of control inputs based on constraints imposed on the problem. Finally, the algorithm is applied to a cart moving in the X-Y plane, including two rigid cooperative arms that are carrying a load. The most important features of synthetic Lyapunov stability compared to the model predictive-based method are its small computational load and its acceptable performance in satisfying both the Lyapunov stability conditions and determinant control constraints in more realistic situations.

Layer-by-layer model-based adaptive control for wire arc additive manufacturing of thin-wall structures

Improving the geometric accuracy of the deposited component is essential for the wider adoption of wire arc additive manufacturing (WAAM) in industries. This paper introduces an online layer-by-layer controller that operates robustly under various welding conditions to improve the deposition accuracy of the WAAM process. Two control strategies are proposed and evaluated in this work: A PID algorithm and a multi-input multi-output model-predictive control (MPC) algorithm. After each layer of deposition, the deposited geometry is measured using a laser scanner. These measurements are compared against the CAD model, and geometric errors are then compensated by the controller, which generates a new set of welding parameters for the next layer. The MPC algorithm, combined with a linear autoregressive (ARX) modelling process, updates welding parameters between successive layers by minimizing a cost function based on sequences of input variables and predicted responses. Weighting coefficients of the ARX model are trained iteratively throughout the manufacturing process. The performance of the designed control architecture is investigated through both simulation and experiments. Results show that the real-time control performance is improved by increasing the complexity of implemented control algorithm: controlled geometric fluctuations in the test component were reduced by 200% whilst maintaining fluctuations within a 3 mm limit under various welding conditions. In addition, the adaptiveness of designed control strategy is verified by accurately controlling the fabrication of a part with complex geometry.

Stabilization of the Cart-Inverted-Pendulum System Using State-Feedback Pole-Independent MPC Controllers.

In this paper, a pole-independent, single-input, multi-output explicit linear MPC controller is proposed to stabilize the fourth-order cart–inverted-pendulum system around the desired equilibrium points. To circumvent an obvious stability problem, a generalized prediction model is proposed that yields an MPC controller with four tuning parameters. The first two parameters, namely the horizon time and the relative cart–pendulum weight factor, are automatically adjusted to ensure a priori prescribed system gain margin and fast pendulum response while the remaining two parameters, namely the pendulum and cart velocity weight factors, are maintained as free tuning parameters. The comparison of the proposed method with some optimal control methods in the absence of disturbance input shows an obvious advantage in the average peak efficiency in favor of the proposed SIMO MPC controller at the price of slightly reduced speed efficiency. Additionally, none of the compared controllers can achieve a system gain margin greater than 1.63, while the proposed one can go beyond that limit at the price of additional degradation in the speed efficiency.

Development of multi-input multi-output virtual tuned mass damper controls for the vibration suppression of structures

This study is concerned with the development of multi-input multi-output control algorithms for the active vibration suppression of structures using accelerometer signals and force-type actuators. The concept of the single-input single-output virtual tuned mass damper control algorithm developed in the previous study was extended to cope with multiple natural modes of structure equipped with a limited number of sensors and actuators. Two control algorithms were developed based on the assumption of collocated control. One is the decentralized virtual tuned mass damper control that produces the actuator signal using only the accelerometer signal of that actuator position. The other is the centralized virtual tuned mass damper control that is designed in modal-space, and produces the modal control force using the modal coordinate. Both the theoretical and experimental results show that the proposed control algorithms are effective in suppressing multiple natural modes with a lesser number of sensors and actuators. However, the decentralized virtual tuned mass damper control can be designed and implemented more easily than the centralized virtual tuned mass damper control.

Optimal fractional-order PID control design for time-delayed multi-input multi-output seismic-excited structural system

The application of the fractional-order PID (FOPID) controller is recently becoming a topic of research interest for vibration control of structures. Some researchers have successfully implemented the FOPID controller in a single-input single-output (SISO) control structural system subjected to earthquake excitations. However, there is a lack of research that focuses on its application in multi-input multi-output (MIMO) control systems to implement it in seismic-excited structures. In this case, the cross-coupling of the process channels in the MIMO control structural system may result in a complex design process of controllers so that each loop is independently designed. From an operational point of view, the time delay and saturation limit of the actuators are other challenges that significantly affect the performance and robustness of the controller so that ignoring them in the design process may lead to unrealistic results. According to the challenges, the present study proposed an optimal fractional-order PID control design approach for structural control systems subjected to earthquake excitation. Gases Brownian motion optimization (GBMO) algorithm is utilized for optimal tuning of the controller parameters. Considering six real earthquakes and seven performance indices, the performance of the proposed controller, implemented on a ten-story building equipped with an active tendon system (ATS), is compared with those provided by the classical PID controller. Simulation results indicate that the proposed FOPID controller is more efficient than the PID in both terms of seismic performance and robustness against time-delay effects. The proposed FOPID controller can maintain suitable seismic performance in small time delays, while a significant performance loss is observed for the PID controller.

Designing a robust controller for a lower limb exoskeleton to treat an individual with crouch gait pattern in the presence of actuator saturation

Crouch gait is a gait anomaly observed in youngsters with cerebral palsy (CP). Rehabilitation robots are useful for treating individuals with crouch gait. Multiple factors have impact on crouch, including contracture, spasticity, weak motor control, and muscle feebleness, which make the designing and controlling of these exoskeletons for this population a challenging job. A harsh kinematic trajectory enforced by an exoskeleton control strategy may place individuals with spasticity at a high risk of muscle tissue injury. Therefore, in this article, a multi-input multi-output (MIMO) control method is proposed to reduce this risk and improve crouch gait pattern. A constrained control law is used in the model since high power demands may threaten the wearer. In addition, the controller needs to be robust enough against external disturbances and uncertainties. Thus, a nonlinear disturbance observer (NDO) is presented to compute the wearer-generated muscular torque and the uncertainties in the modeling. In addition, a robust constrained MIMO backstepping sliding controller (CMBSC) based on NDO is used to deal with the effect of actuator saturation and uncertainties. A simulation test was used to validate the proposed model and controller. The results of Simulation confirmed the efficiency of the proposed control method when applied to crouch gait with subject specific gait reference. Then, some experimental tests were undertaken to validate the efficiency of the proposed controller.