Traditional Control Strategy Research Articles

Observer-based robust servomechanism control of a three-phase induction motor drive

Induction motors (IMs) are widely used in industrial applications, but harmonic distortion in IM drives can lead to inefficiencies and reduced lifespan. Traditional control strategies and filtering methods have limitations in mitigating lower-order harmonics, particularly the 5th harmonic, which can generate reverse torque and increase overheating risk. Existing solutions often struggle to fully address these issues, especially in high-power applications with dynamic loading conditions. This paper proposes a control strategy to reduce voltage and current harmonics in a Z-source inverter (ZSI)-fed IM drive. The system incorporates an observer-based output feedback robust servomechanism controller, designed using Linear Quadratic Regulator (LQR) and Linear Matrix Inequality (LMI) techniques. It ensures closed-loop stability, precise tracking regulation, and effective disturbance rejection, while also featuring a speed adaptation mechanism for sensorless operation. Simulation results demonstrate significant harmonic mitigation, with the controller achieving 1.01% lower voltage THD and 1.03% lower current THD compared to conventional approaches. This improvement aligns with industry standards and outperforms several existing techniques. The proposed method contributes a comprehensive solution for harmonic mitigation in high-power IM drives, applicable to various industrial applications requiring high-quality power and precise speed control.

Energy management optimization of hybrid electric vehicles based on deep learning model predictive control

In this paper, the hybrid electric vehicle (HEV) energy management optimization method is proposed based on deep learning (DL) model predictive control. Through empirical research combined with the questionnaire survey, this article not only provides a new perspective and practical basis but also improves the efficiency and accuracy of the model by improving the relevant algorithms. The study first analyzes the importance of HEV energy management and reviews the existing literature. Then, the optimization method of HEV energy management based on the deep learning model is introduced in detail, including the composition of energy management for hybrid electric vehicles, the structure and working principle of the deep learning model, especially the backpropagation neural network (BPNN) and the convolutional neural network (CNN), and the steps of application of deep learning in energy management. In the experimental part, questionnaire data from 1,500 consumers were used to design the HEV energy management optimization scheme, and consumers’ attitudes and preferences towards HEV energy optimization were discussed. The experimental results show that the proposed model can predict HEV energy consumption under different road conditions (urban roads, highways, mountain areas, suburban areas, and construction sites), and the difference between the average predicted energy consumption and the actual energy consumption is between 0.1KWH and 0.3KWH, showing high prediction accuracy. In addition, the deep learning-based energy management strategy outperforms traditional control strategies in terms of fuel consumption (6.2 L/100 km), battery charge and discharge times (814), battery life, and CO2 emissions, significantly improving the efficiency of HEV energy. These results demonstrate the great potential and practical application value of deep learning models in the optimization of energy management of HEVs, helping to drive the development of more sustainable and efficient transportation systems.

Experimental validation and analysis of hybrid adaptive iterative learning sliding mode control for PMSM seeker coordinator

High-order nonlinearity, strong coupling, and external disturbances constrain the high-precision servo tracking control of the missile seeker coordinator, which compromises the guidance accuracy of guided weapons. The individual differences in the seekers cause model parameter perturbations, leading to uncertainty and time-varying disturbances, which reduces the tracking performance of the seeker servo system. To cope with these uncertainties various traditional Iterative learning control (ILC) strategies have been designed to suppress high-order nonlinear disturbances. However, these are very sensitive to system uncertainties, external disturbances and model parametric perturbations. In response to this challenge, this paper proposes a hybrid adaptive iterative learning sliding mode control (AILS) methodology. The iterative learning sliding mode control (ILSMC) strategy is used to reduce the impact of periodic disturbances in the system, ensuring a rapid system response. The enhanced adaptive learning law (EAL) strategy offers an estimation of the lumped disturbances of the system, encapsulating both parameter shifts and residual disturbances. Through this appropriate adaptive control, both disturbances compensation and the chattering effect due to sliding mode control are simultaneously minimized. Simulation and experimental results show that compared with the traditional open-loop iterative learning control, the learning convergence speed and convergence accuracy of the proposed hybrid AILS are highly significant. Experimental results also show that the control algorithm designed in this paper can also perform adaptively, has fast learning capability and ensures convergence while guaranteeing the tracking accuracy of the seeker coordinator servo system.

Control strategy of PV microgrid grid-connected inverter

Abstract Aiming at the limitation of a three-phase inverter system to access clean energy, a design scheme of a two-stage microgrid grid-connected inverter system is proposed. The traditional double closed-loop control strategy has some disadvantages such as slow response, large fluctuation, and delay of frequency and phase tracking. Therefore, this paper proposes an improved grid-connected control strategy for photovoltaic microgrids with the addition of prediction units, which is established and verified by simulation. The results show that compared with the traditional method, the proposed control method has the advantages of fast dynamic response, small pulsation, and high power quality.

Deep Siamese Neural Network-Driven Model for Robotic Multiple Peg-in-Hole Assembly System

Robots are now widely used in assembly tasks. However, when robots perform the automatic assembly of Multi-Pin Circular Connectors (MPCCs), the small diameter of the pins and the narrow gaps between them present significant challenges. During the assembly process, the robot’s end effector can obstruct the view, and the contact between the pins and the corresponding holes is completely blocked, making this task more precise and challenging than the common peg-in-hole assembly. Therefore, this paper proposes a robotic assembly strategy for MPCCs that includes two main aspects: (1) we employ a vision-based Deep Siamese Neural Network (DSNN) model to address the most challenging peg-in-hole alignment problem in MPCC assembly. This method avoids the difficulties of modeling in traditional control strategies, the high training costs, and the low sample efficiency in reinforcement learning. (2) This paper constructs a complete practical assembly system for MPCCs, covering everything from gripping to final screwing. The experimental results consistently demonstrate that the assembly system integrated with the DSNN can effectively accomplish the MPCC assembly task.

A Virtual Synchronous Generator-Based Control Strategy and Pre-Synchronization Method for a Four-Leg Inverter under Unbalanced Loads

Virtual synchronous generator (VSG) control has positive effects on the stability of microgrids. In practical power systems, both single-phase loads and three-phase unbalanced loads are present. The four-leg inverter is an alternative solution for the power supply of unbalanced loads and grid connections. The traditional VSG control strategy still faces challenges when using a four-leg inverter to provide a symmetrical voltage and stable frequency in the load power supply and pre-synchronization. This paper proposes a VSG-based control strategy along with a pre-synchronization method for four-leg inverters. An improved VSG control strategy is put forward for four-leg inverters. The improved virtual impedance control and power calculation methods are integrated into the control loop to suppress the voltage asymmetry and frequency ripples. Building on the improved VSG control strategy, a pre-synchronization control approach is proposed to minimize the amplitude and phase angle discrepancies between the inverter output voltage and the power grid voltage. In addition, an optimized design method for control parameters is presented to improve VSG dynamic performance. A hardware prototype of the four-leg inverter is built; the results show that the voltage unbalance ratio can be reduced by 89%, and the response time can be shortened by 50%.

ABBR: An augmented BBR for collaborative intelligent transmission over heterogeneous networks in IIoT

In the era of Industry 5.0, with the deep convergence of Industrial Internet of Things (IIoT) and 5G technology, stable transmission of massive data in heterogeneous networks becomes crucial. This is not only the key to improving the efficiency of human–machine collaboration, but also the basis for ensuring system continuity and reliability. The arrival of 5G has brought new challenges to the communication of IIoT in heterogeneous environments. Due to the inherent characteristics of wireless networks, such as random packet loss and network jitter, traditional transmission control schemes often fail to achieve optimal performance. In this paper we propose a novel transmission control algorithm, aBBR. It is an augmented algorithm based on BBRv3. aBBR dynamically adjusts the sending window size through real-time analysis to enhance the transmission performance in heterogeneous networks. Simulation results show that, compared to traditional algorithms, aBBR demonstrates the best comprehensive performance in terms of throughput, latency, and retransmission. When random packet loss exists in the link, aBBR improves the throughput by an average of 29.3% and decreases the retransmission rate by 18.5% while keeping the transmission delay at the same level as BBRv3.

Tracking control via time-varying feedback for an uncertain robotic system with both output constraint and dead-zone input

This paper is devoted to the tracking control for an uncertain robotic system with both output constraint and dead-zone input. Remarkably, the distinctive characters of the system are reflected by system uncertainties and output constraint. First, more serious uncertainties are involved since unknown nonlinear dynamic matrices, external disturbance and the dead-zone input (see unknown slopes and break points therein) are simultaneously considered, but those of the related literature are not. Second, weaker conditions on the output constraint are allowed since the constraint functions considered are only first but not more order continuously differentiable while any their time derivatives are not necessarily available for feedback. This leads to the incapability of the traditional control schemes on this topic. To solve the control problem, a novel control framework is proposed based on time-varying feedback which overcomes the serious system uncertainties while relaxes the conditions on output constraints. Specifically, a state transformation with a time-varying gain is first introduced to derive a new system. Then, by using the traditional backstepping method with the introduction of the time-varying gain in the estimations of some uncertain terms, a time-varying feedback controller is explicitly designed, which ensures that all the states of the resulting closed-loop system are bounded while system output asymptotically tracks the reference signal without any violation of the output constraint. Finally, simulation results for two practical examples are provided to validate the effectiveness of the proposed theoretical results, and moreover, a comparison with PID method is given to show the superiority of the proposed method on tracking accuracy and robustness.

Two efficient three‐mode control strategies for four‐switch buck‐boost converters

AbstractThe bidirectional DC‐DC conversion, as an important application scenario, places certain requirements on the topology structure. The four‐switch buck‐boost (FSBB) converter has received widespread attention due to its simple structure, flexible control and state switchability. However, the traditional control strategy makes it difficult to meet the requirements of high performance, high stability and high security at the same time. Furthermore, the control methods commonly adopted in domestic and international practical applications rely heavily on analogue control, which cannot maintain efficiency over a wide stability range. The use of digital control methods allows for optimized control algorithms, maintaining stable system operation and improving efficiency. Therefore, it is very important to use the digital control method to further improve the smoothness and steady‐state efficiency of the system multi‐mode switching while maintaining a wide input and output range, based on this, this study proposes two new three‐mode control modes: interleaved bias control mode and dual‐edge bias control mode.

Improved weighting factor selection method of predictive torque control for PMSM

AbstractIn the traditional predictive torque control strategy, the cost function considers two different control objectives: stator flux and torque. Therefore, it is necessary to reasonably set the weighting factor to ensure that the optimal voltage vector is accurately selected during the control process, thereby achieving better torque and flux control performance. In view of the complex tuning process caused by traditional predictive torque control setting method of weighting factor, a new weighting factor selection method for permanent magnet synchronous motor control system is proposed in this paper. The derivation process is based on the equivalent transformation between the torque–flux prediction and the voltage vector prediction. If both the cost functions of the torque–flux and voltage vector reach their minimum values, the same output voltage vector will be selected. Compared with previous studies, the algorithm proposed in this paper can find the precise location of the optimal weighting factor without changing the form of the cost function, which simplifies the parameter tuning process. Experimental results are also provided to validate the effectiveness of the proposed algorithm.

A Fast State-of-Charge (SOC) Balancing and Current Sharing Control Strategy for Distributed Energy Storage Units in a DC Microgrid

In isolated operation, DC microgrids require multiple distributed energy storage units (DESUs) to accommodate the variability of distributed generation (DG). The traditional control strategy has the problem of uneven allocation of load current when the line impedance is not matched. As the state-of-charge (SOC) balancing proceeds, the SOC difference gradually decreases, leading to a gradual decrease in the balancing rate. Thus, an improved SOC droop control strategy is introduced in this paper, which uses a combination of power and exponential functions to improve the virtual impedance responsiveness to SOC changes and introduces an adaptive acceleration factor to improve the slow SOC balancing problem. We construct a sparse communication network to achieve information exchange between DESU neighboring units. A global optimization controller employing the consistency algorithm is designed to mitigate the impact of line impedance mismatch on SOC balancing and current allocation. This approach uses a single controller to restore DC bus voltage, effectively reducing control connections and alleviating the communication burden on the system. Lastly, a simulation model of the DC microgrid is developed using MATLAB/Simulink R2021b. The results confirm that the proposed control strategy achieves rapid SOC balancing and the precise allocation of load currents in various complex operational scenarios.

A distributed coordinated control strategy for isolated AC microgrids based on consensus algorithm considering communication delay

AbstractIn the multi‐paralleled converter microgrid system, the traditional hierarchical control strategy can eliminate the bus voltage amplitude and frequency deviation from the rated value. However, isolated AC microgrids may face extreme scenarios such as communication delays and interruptions in data transmission due to the use of low‐bandwidth communication (LBC) lines. Additionally, the inconsistent line impedance of each distributed generation (DG) unit may result in the inaccurate division of reactive power in multi‐paralleled converter systems, thus affecting system stability. To address these issues, this paper presents a distributed coordinated control strategy for isolated AC microgrids based on the consensus algorithm. The proposed strategy first replaces LBC lines with a filter to alleviate the effects of communication delays. A small‐signal model is established in its state space, and stability of the microgrid system under the proposed control strategy is verified through eigenvalue analysis. Furthermore, based on the above theoretical analysis, a consensus algorithm is introduced, and a distributed control strategy for isolated AC microgrids based on the consensus algorithm is proposed to solve the issue of inaccurate equalization of the system's reactive power. Finally, factors that influence the dynamic convergence performance of the consensus algorithm are analyzed through simulation in PLECS software. Also, the maximum tolerable communication delays of the microgrid system under different communication topologies are also compared, and the system's robustness is evaluated under the condition of sudden communication interruption in a DG unit and sudden weather variation. These analyses confirmed the robustness of the proposed strategy against communication delays, output power fluctuation, and communication interruptions.

Optimization of the cruising speed for high-speed trains to reduce energy consumed by motion resistances

As one of the effective measures to reduce energy consumption of high-speed railway, train operation control has gained much attention because of its cost-effectiveness. Conventional approaches to eco-driving generally rely on single-mass train models and lack a thorough investigation into the vehicle cruising process, which can overlook real-world operation conditions and hinder further improvements in energy efficiency. To address these issues, this paper proposes a comprehensive framework for optimizing the cruising speed of high-speed trains (HSTs) based on track conditions to reduce energy consumed by motion resistances (ECMR). Firstly, a multibody dynamics simulation model of the HST is developed and validated to accurately evaluate ECMR under a range of operation scenarios. Simulation results show that the impact of speed on ECMR varies considerably according to track characteristics. Subsequently, a neural network is constructed to derive the regressive relationship between operation conditions and ECMR. On this basis, an optimization model for the cruising speed under different track conditions is formulated to minimize the ECMR of a railway segment with a given operation time. Finally, the effectiveness of the model is validated through case studies, which demonstrate that ECMR can be reduced by up to 27.85% compared to traditional control strategies. The proposed optimization method can be integrated with current train speed profiles to further enhance energy efficiency. This framework has broad applicability for addressing engineering problems related to train operation optimization.

Improved control strategy and designed control parameters of pitch system for wind turbine considering blade load reduction

As wind turbines increase in the power, the longer the blade, the more severe the blade load. Traditional control strategies and control parameters do not consider to reduce blade load, which seriously threatens the safe operation of wind turbines. Therefore, this study proposes a pitch control strategy based on blade active damping control and a multistage control parameter design method. Firstly, this study proposes a pitch control strategy based on blade active damping control to reduce the blade load based on the establishment of a mathematical model of the pitch system considering the blade load and a dynamic model of blade active damping control. Secondly, in the control parameter initial values design of Stage Ⅰ, this study proposes a variable forgetting factor recursive least squares to identify a simplified mathematical model of the pitch system and a Low–overshoot Chien–Hrones–Reswisk to solve the proportional integral control parameter initial values. Thirdly, in the control parameter optimal values design of Stage Ⅱ, this study proposes an adaptive genetic algorithm with improved hyperbolic tangent function to optimize control parameter at the equilibrium points and proposes an adaptive control to calculate control parameter at the non–equilibrium points. Finally, based on the Bladed software for 2 MW and 5 MW wind turbines, the superiority of the proposed methods, effectiveness and applicability in improving the control accuracy of the output power and generator speed, and reducing the load situation at the blade root position are verified.

Dynamic surface control for satellite attitude of the chained three-body tethered system during deployment

A fixed-time attitude stabilization scheme based on dynamic surface control (DSC) is proposed for the attitude of the three-body chained tethered system during tether deployment. Considering that the angular velocity of the satellite is difficult to measure, an extended state observer (ESO) is introduced to simultaneously compensate for the adverse effects of flexible panel vibrations and uncertain inertia tensors. On this basis, a fixed-time convergence attitude control scheme using DSC is designed, accounting for filtering error compensation. To offset the limitations of traditional fixed-time control strategies that ignore actuator saturation, an auxiliary system is introduced to incorporate control input constraints directly into controller design. The stability of the closed system is analyzed based on Lyapunov theory. Finally, the effectiveness and superiority of the proposed algorithm are verified by numerical simulation.

Fault detection and synchronization control in hybrid DC/AC microgrids using grid-forming inverter DC-link controller

This paper introduces a DC-link fault detection and synchronization control strategy for grid-forming inverters in hybrid DC/AC microgrids, aiming to bolster system stability and reliability. The proposed control scheme seamlessly integrates DC-link fault detection with virtual synchronous generator (VSG) control, facilitating inertia emulation and synchronization without the need for a phase-locked loop (PLL). The DC bus controller is designed to swiftly identify faults triggered by power imbalances, such as load fluctuations or generator integration, and dynamically adjust the power reference for the VSG controller to ensure inertia emulation. The Ziegler-Nichols method is utilized for meticulous tuning of the DC-link controller gains, while the modified swing equation governs VSG inertia and synchronization. MATLAB/Simulink demonstrate the superior performance of the proposed method compared to traditional GFM control strategies. Specifically, the proposed controller improved the frequency nadir to 59.98 Hz compared to 59.75 Hz and showed an enhanced power step response of 7.9×10^5 W compared to 6.9×10^5 W for conventional control method. The proposed GFM control also eliminated the 10×10^6 W switching spike observed with conventional controllers. These findings highlight the potential of this fault-controller approach to advance the reliability and efficiency of hybrid DC/AC microgrids.

The Sliding Mode Control for Piezoelectric Tip/Tilt Platform on Precision Motion Tracking

This paper presents the design of a sliding mode controller to compensate hysteresis nonlinearity and achieve precision motion tracking for a novel piezoelectric tip/tilt platform driven by a PZT actuator. The sliding mode control scheme is based on the unique physical characteristics of the piezoelectric tip/tilt platform. The proposed scheme effectively guides the platform state onto a predefined sliding surface and ensures its sustained movement along this manifold. This approach reduces tracking errors compared to conventional methodologies. The stability of the sliding mode control scheme is demonstrated by the Lyapunov theory framework. It achieves precise motion control with minimal tracking error on a piezoelectric tip/tilt platform. The properties of the controller have been confirmed through experimental tests. The proposed control scheme enhances the robust tracking and stability performance on the piezoelectric tip/tilt platform, outperforming traditional control schemes. Compared with the P562.6CD produced by PI Germany, the proposed innovative approach not only boosts the platform’s resolution by 32% but also implements a 33% reduction in cost.

A Nonlinear Active Disturbance Rejection Feedback Control Method for Proton Exchange Membrane Fuel Cell Air Supply Subsystems

The control strategy of the gas supply subsystem is very important to ensure the performance and stability of the fuel cell system. However, due to the inherent nonlinear characteristics of the fuel cell gas supply subsystem, the traditional control strategy is mainly based on proportional integral (PI) control, which has the disadvantages of large limitation, large error, limited immunity, and inconsistent control performance, which seriously affects its effectiveness. In order to overcome these challenges, this paper proposes an optimal control method for air supply subsystems based on nonlinear active disturbance rejection control (ADRC). Firstly, a seven-order fuel cell system model is established, and then, the nonlinear ADRC and traditional PI control strategies are compared and analyzed. Finally, the two strategies are simulated and compared. The validation results indicate that the integral absolute error (IAE) measure of PI control is 0.502, the integral square error (ISE) measure is 0.1382, and the total variation (TV) measure is 399.1248. Compared with the PI control, the IAE and ISE indexes of ADRC were reduced by 61.31% and 58.03%, respectively. ADRC is superior to PI control strategy in all aspects and realizes the efficient adjustment of the system under different working conditions. ADRC is more suitable for the nonlinear characteristics of the gas supply system and is more suitable for the oxygen excess ratio (OER).

Model Predictive Control with Variational Autoencoders for Signal Temporal Logic Specifications.

This paper presents a control strategy synthesis method for dynamical systems with differential constraints, emphasizing the prioritization of specific rules. Special attention is given to scenarios where not all rules can be simultaneously satisfied to complete a given task, necessitating decisions on the extent to which each rule is satisfied, including which rules must be upheld or disregarded. We propose a learning-based Model Predictive Control (MPC) method designed to address these challenges. Our approach integrates a learning method with a traditional control scheme, enabling the controller to emulate human expert behavior. Rules are represented as Signal Temporal Logic (STL) formulas. A robustness margin, quantifying the degree of rule satisfaction, is learned from expert demonstrations using a Conditional Variational Autoencoder (CVAE). This learned margin is then applied in the MPC process to guide the prioritization or exclusion of rules. In a track driving simulation, our method demonstrates the ability to generate behavior resembling that of human experts and effectively manage rule-based dilemmas.

HVAC terminal units are too darn loud

This paper discusses three design trends and their implications for heating, ventilation and air conditioning (HVAC) noise: interior designs resulting in exposed utilities in commercial buildings, particularly in flexible seating and open collaboration areas; improving indoor air quality by providing clean and fiber-free airstreams in healthcare, research, or educational facilities; and the use of recently developed sensible-cooling terminal units, also known as dedicated outside air system (DOAS) terminal units. Applying traditional noise control strategies to HVAC systems in these contexts cannot always achieve typical background noise criteria. Design and construction teams must consider alternative strategies which are often relatively extreme in terms of cost, size, and effort. In many cases, the drawbacks of these strategies outweigh their acoustic benefit and so they are not always implemented, resulting in noisy spaces. This paper argues that the design of HVAC terminal units should be refined to reduce generated noise. This would allow customers more flexibility when using terminal units and ultimately reduce noise impacting building occupants across all industries.