Proposed Control Scheme Research Articles

On‐board charger applications: A new hybrid control strategy for bidirectional CLLC resonant converter

AbstractThis paper proposes a new hybrid control strategy to solve the problem of bidirectional wide voltage range regulation in CLLC converters. On the (LLC) side (G2V direction), the SSSA control method is employed for voltage boost, combined with bus voltage and burst control to achieve wide voltage regulation. On the (C) side (V2G direction), the SSSA and full/half‐bridge mode switching control are used to achieve V2G direction wide voltage regulation. This hybrid control strategy also enables high conversion efficiency in both directions without the need for additional components to reduce the converter power density. This is particularly suitable for OBC applications with strict requirements for converter volume and weight. A detailed analysis with mathematical derivation, hybrid control strategy, and converter operation principles is introduced in this paper. The following subchapters supplement the converter voltage gain, soft switching implementation conditions, and design considerations. Finally, the feasibility of the proposed control strategy was verified through an experimental prototype with an input voltage ranging from 340 to 430 V to an output voltage ranging from 225 to 450 V and bidirectional rated power of 6.6/3.3 kW. This prototype achieved a maximum conversion efficiency of 97.7% in the G2V direction and 97.1% in the V2G direction.

Dynamic Power Control In Utility-Connected Microgrids: A Back To Back Converter Approach For Efficient Integration Of Solar And Wind Energy

This research directs the imperative need for robust power management and control in utility-connected microgrids featuring solar and wind energy sources. The suggested method employs back to back converters for precise control on the transfer of both active and reactive power between the microgrid and the utility. The new control strategy operates in two states, effectively sharing power among the utility and the microgrid. An innovative arrangement among DGs ensures seamless load sharing in both grid-connected (GCM) and islanded modes (IM). The back-to-back converters provide complete frequency isolation, protecting the microgrid from voltage or frequency fluctuations from the utility side. The study introduces synchronized relay-breaker operation during faults, along with disabling back to back converters, ensuring smooth resynchronization after fault clearance. System stability is confirmed under different load changes. The proposed control setup proves resilient to changes in grid-side voltage and frequency, ensuring a stable and reliable power-sharing mechanism under various operating conditions. Simulations conducted in MATLAB/SIMULINK validate the efficacy of the proposed control strategy, highlighting its ability to enhance the stability and reliability of utility-connected microgrids incorporating solar and wind energy sources. This study contributes to advancing sustainable energy solutions, advocating for resilient and eco-friendly power systems.Top of Form.

Decoupled average model-based sliding mode current control of LC-filtered inverters in rotating frame

Recently, LC-filtered inverter has been a trendy research object towards the efficient exploitation and use of sustainable energy sources. The nested control scheme of the system includes a voltage control loop and a current one, which can be developed on the rotating frame. The conventional PI controller is employed for the outer loop voltage controller and it performs effectively. However, the PI current controller introduces delay to the system and overshoot occurs frequently due to the influence of nonlinear load. This paper proposes a novel method of decoupled average model-based sliding mode current control performing faster response and better tracking performance of the desired current. Besides, the comprehensive model of the nonlinear load is applied to examine the robustness of the proposed control scheme. This method is developed on the average model, fixed-frequency PWM can therefore be used to modulate the inverter. The novel method is compared with a conventional PI current controller. Offline simulation on MATLAB/Simulink and real-time simulation using Opal-RT experimental platform prove the advantages of the proposed current controller considering critical scenarios with the nonlinear load.

Research on fast frequency response to AC grid of a battery‐free MVDC system incorporating a high proportion of distributed photovoltaic generation units with reserve capacity

AbstractWith the fast development of renewable energy, system operators intend to require renewable energy generation to have more grid support capabilities. This paper studies the grid‐connected control strategy of a battery‐free medium‐voltage direct current (MVDC) system with distributed photovoltaic generation units (DPVGUs), aiming to realize the rapid grid support function to AC grid frequency. Firstly, a scheme for the MVDC system can keep some reserve capacity is provided. At this moment, DPVGUs execute a master‐slave coordinated control strategy, that is, the master DPVGU adopts an improved DC constant voltage control strategy to maintain the MVDC bus voltage, and the remaining DPVGUs adopt an improved droop control strategy to quickly provide corresponding power. Secondly, when the frequency deviation of AC grid exceeds the deadband of primary frequency control, fast frequency response (FFR) control strategy of the modular multilevel converter is proposed, aiming to actively transmit some or all of the reserved power of the MVDC system to the AC grid and can quickly provide frequency support to AC grid. In FFR control strategy, an additional control strategy is added on the vector control strategy. Finally, simulation studies are carried out in RTDS to verify the effectiveness of the proposed control scheme.

Extended state observer-based primary load frequency controller for power systems with ultra-high wind-energy penetration

In this paper, a novel extended state observer-based (ESO) load frequency control is implemented. Specifically, the proposed control law focuses on the incorporation of wind energy injection as one of the disturbances, treating it as an additional state within the system. The proposed ESO is designed to estimate both the system states and the net disturbance, thereby enhancing its ability to regulate the overall load frequency performance. The proposed control strategy hinges on the judicious selection of control gains and disturbance gain. The estimated disturbance is then effectively compensated to regulate the load frequency. To evaluate the efficacy of the proposed controller, tests are conducted on both single and three area systems. The results demonstrate superior performance, even under conditions involving load and parameter variations.

Improved VSG strategy of grid-forming inverters for supporting inertia and damping

A virtual synchronous generator (VSG) strategy can introduce the rotational inertia and damping characteristics of the synchronous generator to the static inverter, e.g., PV, wind generation, and ESS, which are used to enhance the system frequency support characteristics of the micro-grid. However, under various operations of the VSG, the inertia and damping support capabilities are different, and the conventional VSG strategy with a fixed control coefficient sacrifices a certain degree of dynamic regulation performance with less robustness. This research proposes an improved VSG strategy with adaptive inertia and damping coefficients to increase the flexibility of VSG. To this end, a mathematical model is first established to analyze the impact of various parameters on the characteristics of VSG, and then the root trajectory is used to explore the impacts of various rotational inertia and damping coefficients on the stability of the VSG system. Second, an improved control strategy with adaptive inertia and damping coefficients is proposed based on the characteristics of the second-order system and the system frequency deviations. Finally, a simulation system with the VSG is constructed based on MATLAB/Simulink. The effectiveness of the proposed control strategy is verified by comparing the simulation results to the conventional control strategy with the fixed control coefficients under over-frequency and under-frequency disturbances.

Control of the maglev system with a low stiffness elastic track beam by pole assignment and a reduced order state observer

A maglev train system is an open loop unstable and strongly nonlinear system. Self-excited coupling vibration between the magnet and the track beam can occur even when the train is stationary. To avoid this, the track beam is generally designed with high stiffness, which directly increases the construction cost of the maglev line. In this research, a new control method for the maglev system with a low stiffness elastic track is proposed based on full state feedback theory and pole assignment theory. In the newly designed controller, track beam DOF is introduced into the control loop with the use of the reduced order state observer (ROSO) to make the control system have the ability to suppress track vibration actively, thus reducing the over dependence of the system stability on track properties. Effectiveness of the proposed control strategy is verified through experiments. Results show that it exhibits a better response compared to the traditional control method, with smaller fluctuations in levitation air gap and magnet acceleration. Furthermore, it can maintain system stability with relatively lower track stiffness, reducing the minimum requirements for track stiffness by 53.4% in the test.

Variable evaporating temperature control strategy for a VRF system based on continual estimation of cooling capacity demand of rooms

A variable refrigerant flow system has multiple indoor units for cooling different rooms, and the cooling capacity supply of indoor units depends on evaporating temperature. Existing control strategies of VRF systems generally adopt a constant evaporating temperature which should be low enough to meet the large cooling capacity demand under full load condition, resulting in low energy efficiency. In order to improve energy efficiency, a variable evaporating temperature control strategy is developed, in which evaporating temperature is permitted to rise with the decrease of cooling capacity demand under part load conditions. During the control process, cooling capacity demand of rooms is continually estimated based on indoor thermal balance and room temperature response, and the target evaporating temperature is predicted based on the balance between the cooling capacity supply of indoor units and the cooling capacity demand of rooms. The proposed control strategy has been applied in a VRF system, and its energy efficiency and room temperature variation are tested and compared with those of a same type VRF system that adopts the constant evaporating temperature control strategy. It is shown that the proposed control strategy can improve energy efficiency by 12.6 % and control room temperature fluctuations within ± 0.5 °C.

A Novel Bilevel Electromechanical Compound Braking Coordinated Control Strategy for Electric Vehicles

Due to the difference of response time and braking type between the motor and the pneumatic braking system, it is still difficult to coordinate the motor braking and the pneumatic braking to ensure the vehicle stability and maximal energy regeneration. To address this challenge, a bilevel electromechanical compound braking coordinated control strategy for electric vehicles is proposed considering general and emergency braking state. First, in general braking state, considering the delay characteristics of the pneumatic braking system, a Lagrange quadratic interpolation prediction algorithm is designed to start the pneumatic braking system in advance. Second, in emergency braking state, a model predictive control method is proposed to optimize the braking torque distribution while controlling the wheel slip ratio in a stable range. In order to obtain the optimal control effect, a modified adaptive cuckoo search algorithm is put forward, in which three adaptive impact factors are added. Finally, the proposed control strategy is verified under three road conditions and compared with the conventional control strategy. The results demonstrate significant improvements under gravel road condition, including a 7% increase in energy recovery efficiency, a 92.1% enhancement in the following effect of pneumatic braking torque, and a 43.5% reduction in wheel fluctuation.

Fixed-time tracking control of manipulators based on non-singular fast terminal sliding surface

As for the problem of trajectory tracking of a multi-joint serial manipulator, a novel fixed-time control scheme is proposed based on non-singular fast terminal sliding mode control. By employing a fast terminal sliding mode surface, we solve the singularity problem existed in traditional terminal sliding mode surface. In the meantime, in order to improve the rapidity of the system, the fixed-time control is incorporated with the fast terminal sliding mode surface control. Theoretical analysis proves that the proposed control scheme guarantees that better tracking performance is obtained, and its convergence time upper limit is not affected by the initial states. In addition, a reaching law with the exponential approach characteristic is added to the control law, which effectively reduces the chattering phenomenon in the controller design. Finally, the effectiveness and feasibility of the designed controller are verified through a numerical simulation.

Backstepping method tracking control for underactuated AUV with unknown dynamics based on action-critic networks based ADP

This work investigates trajectory-tacking control problem for underactuated autonomous underwater vehicles (AUV) with unknown dynamics. Due to the unknown dynamics, an action-critic networks based adaptive dynamic programming (ADP) scheme combined with backstepping approach is designed, which can achieve high-level system stability and tracking control accuracy. Firstly, the backstepping approach is introduced into the kinematic model of underactuated AUV and produces a virtual velocity control which is taken as the desired velocity input of the dynamic model of underactuated AUV. Secondly, the error tracking system is constructed according to the dynamic model of underactuated AUV. Thirdly, the critic neural network and the action neural network are employed to transform the trajectory-tracking control problem into optimal control problem based on policy iteration algorithm. At last simulation results are given to verify the effectiveness of the proposed control scheme.

Direct yaw moment control of eight-wheeled distributed drive electric vehicles based on super-twisting sliding mode control

This paper proposed a novel direct yaw moment control (DYC) system to enhance vehicle stability and handling performance in various driving conditions and overcome the chattering problem of traditional sliding mode control. Accordingly, a DYC strategy is developed for eight-wheeled DDEVs by utilizing a super-twisting sliding mode (STSM) algorithm. Initially, a three-degrees-of-freedom model, nonlinear tire model, and motor model are established for vehicles. Subsequently, the reference yaw rate is obtained based on the reference model of the vehicle to serve as a control target. The DYC strategy is then established using the error between the actual yaw rate and the reference yaw rate as the input. Moreover, a traditional sliding mode (SM) controller is developed to enhance vehicle stability. A second-order SM controller is designed by incorporating a STSM control algorithm to address the chattering problem associated with traditional SM controllers. The algorithm adaptively adjusts the sliding surface and controls the gains based on the dynamic state of the vehicle. The effectiveness of the proposed control strategy is validated via hardware-in-the-loop simulations.

FTESO-adaptive neural network based safety control for a quadrotor UAV under multiple disturbances: algorithm and experiments

PurposeQuadrotors have been applied in various fields. However, because the quadrotor is subject to multiple disturbances, consisting of external disturbances, actuator faults and parameter uncertainties, it is difficult to control the unmanned aerial vehicle (UAV) to achieve high-precision tracking performance. This paper aims to design a safety controller that uses observer and neural network method to improve the tracking performance of UAV under multiple disturbances. The experiments prove that this method is effective.Design/methodology/approachFirst, to actively estimate and compensate the synthetic uncertainties of the system, a finite-time extended state observer is investigated, and the disturbances are transformed into the extended state of the system for estimation. Second, an adaptive neural network controller that does not accurately require the dynamic model knowledge is designed based on the estimated value, where the weights of the neural network can be dynamically adjusted by the adaptive law. Furthermore, the finite-time bounded convergence of the proposed observer and the stability of the system are proved through homogeneous theory and Lyapunov method.FindingsThe figure-“8” climbing flight simulation and real flight experiments illustrate that the proposed safety control strategy has good tracking performance.Originality/valueThis paper proposes the safety control structure of the UAV, which combines the extended state observer with the neural network method. Numerical simulation results and actual flight experiments demonstrate the effectiveness of the proposed control strategy.

DSP based space vector PWM control for solar electric vehicle

Solar Electric Vehicles (SEV) are considered the future vehicles to solve the issues of air pollution, global warming, and the rapid decreases of the petroleum resources facing the current transportation technology. However, SEV are still facing important technical obstacles to overcome. They include batteries energy storage capacity, charging times, efficiency of the solar panels and electrical propulsion systems. Solving any of those problems and electric vehicles will compete-complement the internal combustion engines vehicles. In the present work, we propose an electrical propulsion system based on three phase induction motor in order to obtain the desired speed and torque with less power loss. Because of the need to lightweight nature, small volume, low cost, less maintenance and high efficiency system, a three phase squirrel cage induction motor (IM) is selected in the electrical propulsion system. The IM is fed from three phase inverter operated by a constant V/F control method and Space Vector Pulse Width Modulation (SVPWM) algorithm. The proposed control strategy has been implemented on the texas instruments TM320F2812 Digital Signal Processor (DSP) to generate SVPWM signal needed to trigger the gates of IGBT based inverter. The inverter used in this work is a three phase inverter IRAMY20UP60B type. The experimental results show the ability of the proposed control strategy to generate a three-phase sine wave signal with desired frequency. The proposed control strategy is experimented on a locally manufactured EV prototype. The results show that the EV prototype can be propelled to speed up to 60km/h under different road conditions.

An individualized robust stability control strategy for active front steering vehicles

To improve the vehicle stability and driver steering performance, this paper presents an individualized yaw stability control strategy based on H∞ robust control for active front steering (AFS) vehicles. A driver-vehicle system, including a driver steering model and a vehicle dynamics model with AFS, is formed. To analyze the steering characteristics of different drivers, a set of driving data of 36 drivers is collected, and the driver’s characteristics parameters are identified by using the particle swarm optimization (PSO) algorithm. A general evaluation function considering the trajectory tracking performance, vehicle stability, driver workloads, and driver’s characteristics parameters are established to evaluate the comprehensive steering performance. To accomplish the personalized control of vehicle yaw stability, an individualized H∞ robust yaw stability controller is presented by adjusting the gain of the weighting function according to the general evaluation of each driver. Driver-in-the-loop experiment is conducted based on the Matlab/Simulink-CarSim®-Prescan co-simulation platform, and the results demonstrates that the proposed control strategy can provide driver with individualized driving assistance while improving the overall driving performance and reducing the driver’s workloads.

A unified barrier function-based trajectory tracking control for hovercraft with asymmetric dynamic safety constraints

This paper addresses a trajectory tracking control scheme based on a unified barrier function for hovercraft subject to asymmetric dynamic safety constraints. Firstly, a non-affine model of hovercraft has been built and aims to estimate the lumped disturbance, an extended state observer is utilized. Secondly, for the sake of position constraints, virtual velocity control law is obtained by utilizing a unified barrier function (UBF). Thirdly, a constraint control scheme according to UBF is presented to address asymmetric safety constraint problems of hovercraft. The proposed method can transform the original constrained hovercraft control into an equivalent “non-constrained” one and guarantee the three velocities of hovercraft within asymmetric boundaries with requirements of safe performance during motion respectively. In addition, aims to handle the issue of the virtual control law's differential explosion, and considering that the output torque of the controller should be limited to a certain range, a bioinspired neurodynamic model (BNDM) is adopted. The total system is ultimately uniformly bounded (UUB) via Lyapunov stability theories and the stability lemma of the cascade system. Finally, obtained simulation results prove the effectiveness of the proposed control scheme.

Coopetition-dependent controller design for bipartite synchronization of cooperation–competition delayed memristive neural networks

In this paper, the bipartite synchronization problem for cooperation–competition delayed memristive neural networks (CCDMNNs) is addressed via designing a coopetition-dependent controller. Based on the coordinate transformation technique, an easy-to-analyze error system is constructed in the presence of cooperation–competition interactions. Two control strategies are proposed respectively to realize bipartite synchronization. First of all, a coopetition-dependent controller that can eliminate the impact of cooperation–competition interactions as well as the time-varying state-dependent memristive connection weights is designed. In combination with inequality techniques, the Lyapunov theory and the interval matrix method, an LMI-form criterion is established to ensure the bipartite synchronization of CCDMNNs. Moreover, an adaptive controller that can automatically adjusts the control parameters to achieve appropriate control gains is proposed to further improve the control performance. Finally, a numerical example is given to verify the correctness and superiority of the proposed control schemes.

Nonprehensile Manipulation for Rapid Object Spinning via Multisensory Learning from Demonstration.

Dexterous manipulation concerns the control of a robot hand to manipulate an object in a desired manner. While classical dexterous manipulation strategies are based on stable grasping (or force closure), many human-like manipulation tasks do not maintain grasp stability and often utilize the dynamics of the object rather than the closed form of kinematic relation between the object and the robotic hand. Such manipulation strategies are referred as nonprehensile or dynamic dexterous manipulation in the literature. Nonprehensile manipulation often involves fast and agile movements such as throwing and flipping. Due to the complexity of such motions and uncertainties associated with them, it has been challenging to realize nonprehensile manipulation tasks in a reliable way. In this paper, we propose a new control strategy to realize practical nonprehensile manipulation. First, we make explicit use of multiple modalities of sensory data for the design of control law. Specifically, force data are employed for feedforward control, while position data are used for feedback control. Secondly, control signals (both feedback and feedforward) are obtained through multisensory learning from demonstration (LfD) experiments designed and performed for specific nonprehensile manipulation tasks of concern. To prove the concept of the proposed control strategy, experimental tests were conducted for a dynamic spinning task using a sensory-rich, two-finger robotic hand. The control performance (i.e., the speed and accuracy of the spinning task) was also compared with that of classical dexterous manipulation based on force closure and finger gaiting.

Parameters self‐turning controller fused with an adaptive nominal model for disturbance observer: Application to direct drive manipulator with significant payload changes

SummaryThe direct drive manipulator, which directly links the joint shaft to the motor rotor, is susceptible to external disturbances and model perturbations. When the direct drive manipulator operates with varying payloads, the disturbance observer (DOB) with a constant nominal model struggles to achieve satisfactory performance. To minimize estimation errors caused by disturbance, enhance trajectory tracking accuracy, and improve system robustness, this article proposes a controller parameter self‐tuning strategy with settling time, fused with a nominal model adaptive disturbance observer. Specifically, by optimizing the gain coefficient of the model reference adaptive identification (MRAI), the significant changes in payload at the manipulator's end joint can be quickly and accurately estimated. The inertia introduced by the payload is then employed to continuously update the nominal model of the manipulator, incorporating information about the inertia of the coupling joints. In addition, the controller parameters are updated using a self‐tuning link with settling time, which guarantees that the closed‐loop characteristics of the system remain unaffected by payload changes. Finally, the proposed method was validated using a two‐joint direct drive manipulator. The results demonstrate the robustness of the proposed method in the presence of significant payload changes, and the accuracy of disturbance estimation and the tracking performance of the proposed control strategy is significantly improved.

Adaptive backstepping control for a class of uncertain systems with actuator delay and faults

Unknown actuator failures are inevitable in practical systems. At the same time, time delay exists in many physical actuators, and the system performance will be affected by such actuator delay and faults. However, the results of studies that attempt to compensate for unknown failures of actuators with time delay are still very limited. In this paper, such a problem is studied, and an adaptive control scheme is proposed based on backstepping approaches. First, the input delay of actuator faults and output disturbances are transformed into unknown effects on the output signal. In the backstepping recursive design, these unknown effects will accumulate to the last step of the controller design. Then, a new Lyapunov function is constructed by introducing auxiliary signals to prove the stability of the system. It is shown that the proposed control scheme can compensate for the effects caused by unknown actuator failures and input delays. The stability of the closed-loop system can be guaranteed by this adaptive controller. Finally, simulation studies are used to verify the effectiveness of the proposed scheme.