Hybrid Control Research Articles

Clutching inertial system for base-isolated liquid storage tanks: Seismic performance and optimal parameters

Liquid storage tanks (LSTs) are susceptible to significant damage during earthquake events, necessitating their continued functionality and additional protection through isolation techniques. The sloshing response and overall base shear of LSTs are critical factors, particularly in base isolated (BI) tanks. This study aims to enhance the seismic resilience of LSTs by leveraging a newly developed advanced inerter-based vibration control methodology for structures. This innovative approach integrates a supplemental clutching inertial system (CIS) with a laminated rubber isolator (LRB). Utilizing an equivalent linearization technique, the study evaluates the equivalent damping and inertance constants to account for the nonlinear behavior of the CIS. These equivalent parameters are then used to assess the stationary peak response of the isolated tank structure, which encompasses sloshing and bearing displacements, forces within the CIS and the isolator, and the total base shear. Further, the analysis employs the stationary power spectral density function (PSDF) model of earthquake excitation by Kanai-Tajimi and the performance of the inerter-based hybrid control strategy is examined by varying system parameters, such as isolation period, isolation damping, CIS inertance mass ratio, and the tank's aspect ratio (i.e., broad or slender). An optimal inertance of the CIS is identified, leading to the least overall base shear for both isolated tank configurations. The impact of these parameters inertance mass ratio of the CIS is also investigated. Subsequently, the isolated tanks are exposed to eleven near-fault (NF) and far-field (FF) real-time earthquake excitations. The study evaluates the CIS's performance as an auxiliary system during these seismic events and contrasts the outcomes with the seismic response under stochastic excitations. The results highlight the effectiveness of the advanced inerter-based hybrid control strategy in reducing the seismic response of isolated tank configurations.

Design of active vibration isolation controller for pointing stabilization mechanism based on feedforward–feedback hybrid control

The use of space technology and small space loads is increasingly common. To address this, a mechanism for isolation vibration in small optical load has been proposed. The mechanism includes a coarse and fine stage parallel pointing platform (CFPP). This paper investigates an active vibration isolation scheme for the novel pointing stabilization mechanism. The kinetic energy minimization principle is derived from the analysis of its working mechanism and dynamic feedforward characteristics. This principle is confirmed by the feedforward of the indeterminate degrees of freedom of the under-constrained mechanism. A hybrid control scheme of feedback and feedforward is developed based on the H∞ algorithm and the optimal feedforward control algorithm. Simulation and experimentation have proven that the vibration isolation efficiency of more than 20 dB can be achieved in all three axis rotation directions. This meets the precision pointing requirements of small optical load effectively.

Trajectory control and optimization of PID controller parameters for dual-arms with a single-link underwater robot manipulator

ABSTRACT This paper focuses on the modeling, simulation, and control of the trajectories of a dual-arm single-link underwater robot manipulator (DS-URM), having dual arms (left and right) with a single link each. The dynamic coupling between the base and the dual arms makes trajectory control of the end effector difficult. This study attempts to simulate and control dual arms with a single-link underwater robotic manipulator using a hybrid controller. To reduce the error for a sinusoidal wave and a cycloid trajectory, a hybrid controller is the combination of the Proportional-Integral-Derivative (PID) controller and overwhelm controller. The underwater dynamic’s buoyancy, gravity, and hydrostatic forces acting on the underwater robot are modeled in an integrated SIMULINK model, including the DS-URM. The DS-URM has been investigated, revealing some significant trajectories of the left and right tips. Effective tuning methods include Ziegler-Nicholas (Z-N), genetic algorithms, Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). The Integral of Time multiplied by Absolute Error (ITAE) criteria has been used to improve trajectory tracking via particle swarm optimization. Results show significant improvements in trajectory tracking, with ITAE error reduction by 96.44% and 98.6% for left and right arms, respectively, achieving stability in 0.000645s.

Development of Smart Hydroponics System using AI-based Sensing

This paper proposes a smart hydroponic system that operates automatically using a fuzzy logic algorithm, integrating IoT functionalities to support smart agriculture. The system allows for remote monitoring and control via the internet, providing real-time data on water levels, pH levels, temperature, and nutrient solution temperature. Precise dosing and temperature control are critical for optimal plant growth, and the system schedules temperature measurements to ensure stability. Unstable temperature can affect pH levels, thereby impacting nutrient absorption. The proposed system employs sensors to continuously monitor the electrical conductivity (EC) and pH levels of the nutrient solution. Fuzzy control is utilized to regulate the nutrient solution pump, automatically adjusting EC and pH levels to promote optimal plant growth. This approach reduces the time burden on producers and provides more precise control over the nutrient solution, resulting in improved growth outcomes. The main contributions of this work are as follows: the development and implementation of an AI-based system integrating a controller, IoT environment, fuzzy logic algorithm, and NFT (nutrient film technology) hydroponics; the creation of a user-friendly interface for farmers through the Smart-Hydroponic application, enabling hybrid monitoring and control of hydroponic farms; the establishment of an IoT-based cloud environment for sensor data monitoring; the implementation of a smart hydroponic system for nutrient sensing, monitoring, and control; and a comparative analysis between smart and conventional hydroponics based on morphological results.

BIFURCATION ANALYSIS AND CHAOS CONTROL OF THE DISSOLVED OXYGEN-PHYTOPLANKTON DYNAMICAL MODEL

The production of oxygen through phytoplankton photosynthesis is a crucial phenomenon in the dynamics of marine ecosystems. A generic oxygen-phytoplankton interaction model is considered to comprehend its underlying mechanism. This paper investigates the discrete-time dynamics of oxygen and phytoplankton in aquatic ecosystems, incorporating factors that cause phytoplankton mortality due to external influences. We explore the conditions for the local stability of steady states concerning the oxygen content in dissolved water and phytoplankton density. The analysis reveals that the model undergoes a co-dimension one bifurcation, encompassing flip and Neimark–Sacker bifurcations, utilizing the center manifold theorem and bifurcation theory. To manage the chaos resulting from the Neimark–Sacker bifurcation, we apply the OGY feedback control method and a hybrid control methodology. Finally, we present numerical simulations to validate the theoretical discussion.

Schlieren-Based Analysis of Supersonic Multistream Control Using Data-Driven and Filtering Techniques

Synchronized time-resolved schlieren and far-field pressure measurements were used to examine control of a supersonic multi-aperture rectangular single- expansion ramp nozzle with an aft deck. The design contains core (Mach 1.6) and bypass (sonic) streams mixing behind a splitter plate, generating a high-frequency tone. Control was applied through splitter-plate trailing-edge spanwise waviness (passive), spanwise-arranged microjet blowing actuators (active), and their combination (hybrid). Passive and hybrid control successfully alleviate the tone, whereas active control amplifies and alters the tone frequency. Time-averaged schlieren data show changes to mean shock structures with control. Spectral proper orthogonal decomposition (SPOD) spectra from the raw schlieren data are consistent with the far-field measurements and reveal coherent structural details. Radiation associated with high-frequency structures is inhibited by passive and hybrid control that enhance mixing, but not by active control. Momentum potential theory filtering prior to SPOD application augments the analysis by isolating irrotational content, providing additional clues on acoustic vs hydrodynamic mechanisms. The results indicate shock–shear-layer interaction and shedding as sources of low- and high-frequency content, respectively. As such, through suitable processing, relatively easy-to-obtain high-speed schlieren provides a powerful tool to assess complex flow-control physics.

Investigations of BLDC motor speed characteristics via THD under conventional and advanced hybrid controllers

This project investigates brushless direct current (BLDC) motor speed control through total harmonic distortion (THD) analysis, employing proportional integral (PI), fuzzy logic (FLC), adaptive neuro-fuzzy inference system (ANFIS), and an innovative hybrid ANFIS-PD/PI controller. Considering the vital role of BLDC motors in precision-dependent industries like robotics, electric vehicles, and industrial automation, our primary focus is on understanding BLDC motor operation and recognizing THD's significance as a performance metric. Controllers are meticulously implemented in real-time, fine-tuned, and optimized to achieve desired speed characteristics, incorporating considerations like response time, accuracy, and energy efficiency. The project's core involves THD analysis, quantifying harmonic content in the BLDC motor's speed waveform. This facilitates a comprehensive comparative evaluation of controller performance, assessing their capability to maintain speed stability and influence power quality. The discussion covers the merits and limitations of each controller, with a special emphasis on the hybrid ANFIS-PD/PI controller, seamlessly blending ANFIS adaptability with PD/PI control stability. Results illustrate the hybrid controller's excellence in optimizing BLDC motor speed control, demonstrating superior performance in speed accuracy, disturbance rejection, and THD reduction. These findings drive advancements in motor control technology, providing practical guidance for selecting controllers tailored to specific application requirements. Simulation results can be analyzed using MATLAB/Simulink 2018a Software.

The improved force/position hybrid control system for multi joint humanoid multi fingered dexterous hand

The improved force/position hybrid control system for multi joint humanoid multi fingered dexterous hand

Robust hybrid control strategy for active power management in Kabertene wind farm within Algeria’s PIAT grid

The paper introduces a hybrid control strategy for optimised active power management in Algeria's Kabertene wind farm, crucial for the pole insalah-adrar-timimoune (PIAT) grid's stability. This strategy merges simultaneous interconnection and damping assignment (SIDA) passivity theory, passivity-based control (PBC), and multivariable proportional-integral-derivative (PID) controllers. This combined approach ensures frequency and voltage stability within the PIAT grid, which encompasses various elements like wind farms, solar plants, gas turbines, and dynamic impedance (Z), current (I), and active power (P) (D-ZIP), load model. By tailoring controllers for doubly fed induction generators (DFIGs) using SIDA-PBC principles and optimising internal parameters, the strategy achieves precise control of active power output. Additionally, particle swarm optimisation (PSO) refines power scheduling, which is especially beneficial for intermittent renewable sources like DFIGs. This comprehensive strategy offers numerous advantages: improved network stability, minimized voltage deviations, reduced frequency fluctuations, and enhanced integration of renewable energy sources. The paper emphasises practical implementation considerations, providing valuable guidance for efficient Kabertene wind farm operation and integration. This research contributes significantly to fostering cleaner and more reliable energy systems, facilitating the PIAT grid's transition towards sustainable energy generation.

Adaptive hybrid control for the formed morphology in powder-based laser metal deposition

Adaptive hybrid control for the formed morphology in powder-based laser metal deposition

Complex Dynamics of a Discretized Predator–Prey System with Prey Refuge Using a Piecewise Constant Argument Method

The objective of this work is to investigate the complex dynamics of a discrete predator–prey system using the method of piecewise constant argument for discretization. An analysis is conducted to examine the presence and stability of fixed points. Furthermore, the system is shown to undergo period-doubling (PD) and Neimark–Sacker (NS) bifurcations by the use of center manifold and bifurcation theories. The feedback and hybrid control strategies are used to regulate the system’s bifurcating and chaotic behaviors. Both strategies seem to be effective in managing bifurcation and chaos inside the system. Finally, the main results are validated by numerical evidence. Parameters of the system are varied to produce time graphs, phase portraits, bifurcation diagrams, and maximum Lyapunov exponent (MLE) graphs. The discrete model displays rich dynamics, as seen in the numerical simulations and graphs, indicating a complex and chaotic system.

A novel development of h-CSLQFL controller for optimal sizing and economic pricing of battery in electric vehicle system

This paper proposes a novel design of h-CSLQFL controller for reducing the battery size (kWh) and battery cost (US$/kWh) of electric vehicle (EV) system. The h-CSLQFL controller is a hybrid controller which is formed by the combination of Cuckoo search optimization (CSO), linear quadratic regulator (LQR), and fuzzy logic controller (FLC). The higher battery cost is one of the biggest challenges with EV due to which a significant number of consumers cannot afford to purchase it. The replacement of conventional vehicles with EV creates ecofriendly and sustainable solutions. For attaining the optimal reduced cost of battery, a strategy has been demonstrated by framing a multi-objective function which is controlled with h-CSLQFL controller. It commences with the design of mathematical model of EV in which supply voltage and current are the input parameters whereas the speed and torque are the output parameters. Thereafter, LQR controller was designed and implemented for analyzing the performance parameter of EV. Afterwards, CSO controller in support with LQR controller has been formulated in closed loop manner. At the end, the multi-objective function is controlled with LQR based CSO and LQR controller. It is observed that the least optimal economic cost and size of EV battery have been attained with h-CSLQFL controller in comparison to the LQR based CSO and LQR controller. In addition to this, the least risk factor and reduced sensitivity have been also attained with the proposed approach. The validation of the proposed h-CSLQFL controller has been done on a hardware set up.

Design of hybrid controller for broadband vibration isolation with active negative stiffness and ground-hook

This paper presents a novel hybrid control strategy that integrates active negative stiffness (ANS) with the ground-hook principle, aiming to enhance the vibration isolation system’s performance across both low and high-frequency ranges. Firstly, to improve vibration isolation performance, this study analyzes the existing ANS model. It identifies key factors affecting ANS stability and proposes a control parameter adjustment scheme based on stability conditions. This approach effectively refines ANS control theory and provides theoretical support for the application of ANS by ensuring stability. Secondly, addressing the limitations of ANS in high-frequency vibration isolation, this study proposes targeted solutions based on the ground-hook principle. While ANS excels in low-frequency isolation, this work tackles its shortcomings to enhance high-frequency vibration isolation. These solutions include the implementation of relative velocity feedback (RVFB) control and absolute velocity feedforward (AVFF) control to enhance high-frequency vibration isolation performance, and AVFF exhibits superior advantages over RVFB. Furthermore, the stability of different control strategies is analyzed. The findings reveal that sky-hook significantly enhances the control stability margin of the ANS loop, while RVFB decreases it, with AVFF having a minimal impact. Finally, comprehensive experiments validate a significant enhancement in both low and high-frequency vibration isolation performance, providing robust evidence for the practical engineering application of the vibration isolation system.

Model-based hybrid control of combined active–passive vertical zero-gravity system

Ground-based full-scale physical experiments on spacecraft have become an essential part of ground simulations. However, achieving a vertical translational degree of freedom for zero-gravity simulation has become a key technological bottleneck for six-degree-of-freedom ground simulations. The combined active–passive vertical zero-gravity system discussed in this paper can achieve zero-gravity simulation in the vertical direction; however, it exhibits deficiencies such as inaccurate modeling, low control precision, and significant control delays. Addressing the issue of inaccurate modeling, this study uses interpretable ODE neural networks to identify the dynamic model of the system. For resolving the problems of poor control precision and significant delays, this study proposes a hybrid control algorithm based on the ODE dynamic model. The controller includes nominal model feedforward, PID feedback, and learning-based feedforward components. Finally, comparative experiments are designed to validate the proposed model-based hybrid control algorithm. Compared to PID-Feedforward controller, the hybrid controller exhibits a higher response speed, while compared to model-based feedforward controller, it exhibits a better control accuracy. Therefore, the proposed hybrid control algorithm based on the ODE dynamic model effectively enhances the control response speed and improves the control precision of the system.

Long Short‐Term Memory‐Based Multi‐Robot Trajectory Planning: Learn from MPCC and Make It Better

The current trajectory planning methods for multi‐robot systems face challenges due to high computational burden and inadequate adaptability in complex constrained environments, obstructing efficiency improvements in production and logistics. This article presents an innovative solution by integrating model predictive contouring control (MPCC) and long short‐term memory (LSTM) networks for real‐time trajectory planning of multiple mobile robots. Based on the datasets generated by MPCC, a customized LSTM network is constructed to learn the collaborative planning behavior from these datasets offline, subsequently producing smooth and efficient trajectories online with a low computational burden. Moreover, a hybrid control scheme, incorporating a lidar‐based safety evaluator, avoids unexpected collision risks by switching to MPCC when necessary, ensuring the overall safety and reliability of the multi‐robot system. The proposed hybrid LSTM method is implemented and tested in the robot operating system (ROS) within diverse constrained scenarios. Experimental results demonstrate that the hybrid LSTM method achieves ≈6% enhancements in trajectory productivity and a reduced computational burden of roughly 75% compared to MPCC, thereby providing a promising solution for local multi‐robot trajectory planning in logistics transportation tasks.

New PI-synergetic hybrid control for suppressing circulating currents of an electrical drive system fed by two parallel inverters

New PI-synergetic hybrid control for suppressing circulating currents of an electrical drive system fed by two parallel inverters

A Hybrid Control Framework for Chemical Processes with Long Time Delay: Theory and Experiments.

This paper proposes a hybrid control framework based on internal model concepts, sliding mode control methodology, and fractional-order calculus theory. As a result, a modified Smith predictor (SP) is proposed for nonlinear systems with significant delays. The particular predictive approach enhances the sliding mode control (SMC) controller's transient responses for dead-time processes, and the SMC gives the predictive structure robustness for model mismatches by combining the previous methods with fractional order concepts; the result is a dynamical sliding mode controller. A numerical example is considered to evaluate the performance of the proposed approach, where a step change, external disturbance, and parametric uncertainty test are performed. A real application in the TCLab Arduino kit is presented; the proposed method presented good performance with a little amount of chattering, and in the disturbance rejection case, the overshoot increased with an aggressive response; in both cases, better tuning parameters can improve the process response and the controller action.

Hybrid control of ISOP-DAB converters based on double-loop decoupling

Hybrid control of ISOP-DAB converters based on double-loop decoupling

Adaptive variable impedance force/position hybrid control for large surface polishing

PurposeThis paper aims to propose a hybrid force/position controller based on the adaptive variable impedance.Design/methodology/approachFirst, the working space is divided into a force control subspace and a position subspace, the force control subspace adopts the position impedance control strategy. At the same time, the contact force model between the robot and the surface is analyzed in this space. Second, based on the traditional position impedance, the model reference adaptive control is introduced to provide an accurate reference position for the impedance controller. Then, the BP neural network is used to adjust the impedance parameters online.FindingsThe experimental results show that compared with the traditional PI control method, the proposed method has a higher flexibility, the dynamic response accommodation time is reduced by 7.688 s and the steady-state error is reduced by 30.531%. The overshoot of the contact force between the end of robot and the workpiece is reduced by 34.325% comparing with the fixed impedance control method.Practical implicationsThe proposed control method compares with a hybrid force/position based on PI control method and a position fixed impedance control method by simulation and experiment.Originality/valueThe adaptive variable impedance control method improves accuracy of force tracking and solves the problem of the large surfaces with robot grinding often over-polished at the protrusion and under-polished at the concave.

Optimized integration of renewable energy sources using seven-level converter controlled by ANFIS-CS-GWO

The global energy landscape is undergoing a significant transformation, driven by the need to reduce greenhouse gas emissions, enhance energy security, and provide sustainable and clean power solutions. Renewable energy sources (RES) such as Solar Photovoltaic (SPV), wind, fuel cells, and tidal power play a crucial role in this transition due to their abundant availability and low environmental impact. However, integrating these diverse energy sources into power systems requires advanced control strategies to ensure stability, efficiency, and reliability. Existing systems, however, face significant challenges such as unstable voltage profiles, prolonged fault settling time (FST), and high total harmonic distortion (THD), which compromise their efficiency and reliability. To overcome these limitations, a novel methodology is proposed, integrating Solar PV (SPV), wind, fuel cell, and tidal energy sources into a seven-level converter (SLC) system, which is named as Four Level RES based SLC (FLRES-SLC) system. This system is managed by an Adaptive Neuro-Fuzzy Inference System (ANFIS), fine-tuned by Cuckoo Search adopted Grey Wolf Optimizer (CS-GWO). This hybrid control strategy ensures a robust voltage profile, significantly reduces FST, and lowers THD, thereby markedly improving the overall performance of the converter system. The innovative approach offers a reliable and efficient solution for modern power systems, enhancing their integration with diverse RES and ensuring high-quality power delivery.