Search Results (7)

Temperature control plays a critical role in mitigating the lifespan degradation mechanisms and ensuring thermal safety of lithium-ion batteries (LIBs) and proton exchange membrane fuel cells (PEMFCs). However, current energy management strategies (EMS) for fuel cell hybrid electric vehicles (FCHEVs) generally lack comprehensive thermal effect modeling and thermal runaway fault diagnosis, leading to irreversible aging and thermal runaway risks for LIBs and PEMFCs stacks under complex operating conditions. To address this challenge, this paper proposes a thermo-electrical co-optimization EMS incorporating thermal runaway fault diagnosis actuators, with the following innovations: firstly, a dual-layer framework integrates a temperature fault diagnosis-based penalty into the EMS and a real-time power regulator to suppress heat generation and constrain LIBs/PEMFCs output, achieving hierarchical thermal management and improved safety; secondly, the distributional soft actor–critic (DSAC)-based EMS incorporates energy consumption, state-of-health (SoH) degradation, and temperature fault diagnosis-based constraints into a composite penalty function, which regularizes the reward shaping and guides the policy toward efficient and safe operation; finally, a thermal safe constriction controller (TSCC) is designed to continuously monitor the temperature of power sources and automatically activate when temperatures exceed the optimal operating range. It intelligently identifies optimized actions that not only meet target power demands but also comply with safety constraints. Simulation results demonstrate that compared to DDPG, TD3, and SAC baseline strategies, DSAC-EMS achieves maximum reductions of 39.91% in energy consumption and 29.38% in SoH degradation. With the TSCC implementation, enhanced thermal safety is achieved, while the maximum energy-saving improvement reaches 25.29% and the maximum reduction in SoH degradation attains 20.32%. Full article

This paper presents a cascade predictive control structure based on field-oriented control (FOC) in the dq rotor reference frame for the synchronous reluctance machine (SynRM). The constant d-axis current control strategy was used, and thus, the electromagnetic torque was directly controlled by the q-axis current. Because the model of the two axes of currents from the inner loop is a coupled non-linear multivariable one, to control in a non-interaction and linear way the two currents, their decoupling was achieved through feedforward components. Following the decoupling, two independent monovariable linear systems resulted for the two current dynamics that were controlled using model predictive control (MPC) algorithms, considering their ability to automatically handle the state bounds. The most important bounds for SynRM are the limits imposed on currents and voltages, which in the dq plane correspond to a circular limit. To avoid computational effort, linear limitations were adopted through polygonal approximations, resulting in rectangular regions in the dq plane. For the outer loop that controls the angular speed with a constrained MPC algorithm, the q-axis current closed-loop dynamics and the torque linear equation were considered. To evaluate the performance of the proposed cascade predictive control structure, a simulation study using MPC controllers versus PI ones was conducted. Full article

The paper extends the earlier work entitled “Making the PI and PID Controller Tuning Inspired by Ziegler and Nichols Precise and Reliable”, to higher-order controllers and a broader range of experiments. The original series PI and PID controllers, based on automatic reset calculated by filtered controller outputs, are now augmented by higher-order output derivatives. This increases the number of degrees of freedom that can be used to modify the resulting dynamics, accelerates transient responses, and increases robustness to unmodeled dynamics and uncertainties. The fourth order noise attenuation filter used in the original work allows for the addition of an acceleration feedback signal, thus resulting in a series PIDA controller or even a jerk feedback that leads to a PIDAJ series controller. Such a design can further use the original process and filter approximation of the step responses through the integral-plus-dead-time (IPDT) model, while allowing experimentation with disturbance and setpoint step responses of the series PI, PID, PIDA and PIDAJ controllers, and thus, evaluating the role of output derivatives and noise attenuation from a broader perspective. All controllers considered are tuned using the Multiple Real Dominant Pole (MRDP) method, which is complemented by a factorization of the controller transfer functions to achieve the smallest possible time constant for automatic reset. The smallest time constant is chosen to improve the constrained transient response of the considered controller types. The obtained excellent performance and robustness allow the proposed controllers to be applied to a wider range of systems with dominant first-order dynamics. The proposed design is illustrated on a real-time speed control of a stable direct-current (DC) motor, which is approximated (together with a noise attenuation filter) by an IPDT model. The transient responses obtained are nearly time-optimal, with control signal limitations active for most setpoint step responses. Four controllers with different degrees of derivative with generalized automatic reset were used for comparison. It was found that controllers with higher-order derivatives may significantly improve the disturbance performance and virtually eliminate overshoots in the setpoint step responses in constrained velocity control. Full article

Multi-cell cooperative control can be competent for the current increasingly complex biomedical experiments, greatly improving the efficiency of cell manipulation experiments. At present, this kind of multi-cell cooperative control algorithm is becoming more and more important. In this study, holographic optical tweezers are used to capture multiple cells, and a cell manipulation controller is designed based on the Fuzzy Brain Emotional Learning (FBEL) neural network. Firstly, the dynamic model of trapping yeast cells by optical tweezers is analyzed. The distance between the trap position and the cell position is constrained to avoid cell detachment due to the trap moving too fast. Then, the design cell manipulation controller is relied upon to realize single transport trajectory tracking control. Finally, a multi-cell cooperative control algorithm is designed, and combined with the cell manipulation controller, a multi-cell cooperative controller based on the FBEL neural network is formed. The error between the cell position and the desired trajectory is the input of the multi-cell cooperative controller. The output of the multi-cell cooperative controller is the optical trap position, which is used to realize the cooperative control of multiple cells by holographic optical tweezers. The simulation results showed that the multi-cell cooperative controller based on the FBEL neural network can effectively control multiple yeast cells and quickly converge the cell formation, while ensuring a higher control accuracy than other traditional cell manipulation controllers. It provides a new solution for the efficient and precise automatic manipulation of multiple cells. Full article

For an electro-hydrostatic actuator (EHA), the position, speed, and current cascade three-loop control architecture are dominant in existing active disturbance rejection control (ADRC). However, this architecture suffers from many problems, such as severe noise sensitivity of the extended state observer (ESO), excessive complexity of control structure, and too many tuned parameters, which makes the controller not easy to implement in practice. Aiming at the above drawbacks, a novel cascade double-loop ADRC strategy that is automatic current-constrained is proposed, which makes the whole ADRC architecture simplified to the position and the integrated speed–current double-loop architecture. Firstly, for the position control loop, the singular perturbation theory is used to reasonably reduce the order of the model for the position subsystem of EHA. A reduced order ADRC controller (ROADRC) is synthesized, which not only effectively reduces the noise sensitivity of ESO, but also circumvents use of the actuation acceleration information in the controller design process. Secondly, the integrated speed–current ADRC controller is designed by taking the speed and current subsystems of EHA into synthesis, which avoids the problem of excessive current loop bandwidth in conventional three-loop control architecture, and the number of tuned parameters is significantly reduced. Additionally, an uncomplicated and effective automatic current-constrained ADRC controller (CACADRC) is designed to solve the problem in the integrated speed–current ADRC that the current cannot be automatically constrained. Finally, by comparing the three-loop PI controller with the traditional three-loop ADRC, a detailed simulation analysis is carried out to verify the effectiveness and merits of the proposed controller. The simulation results show that the proposed controller not only inherits the advantages of high precision and strong disturbance rejection performance of the conventional ADRC, but can also efficiently decrease the noise sensitivity of ESO and effectively achieve the current-constrained control. Full article

Recently, there have been many cases in which direct current (DC) facilities have been placed in alternating current (AC) systems for various reasons. In particular, in Korea, studies are being conducted to install a back-to-back (BTB) voltage-sourced converter (VSC) high-voltage direct current (HVDC) to solve the fault current problem of the meshed system, and discussions on how to operate it have been made accordingly. It is possible to provide grid services such as minimizing grid loss by changing the HVDC operating point, but it also may violate reliability standards without proper HVDC operation according to the system condition. Especially, unlike the AC system, DC may adversely affect the AC system because the operating point does not change even after a disturbance has occurred, so strategies to change the operating point after the contingency are required. In this paper, a method for finding the operating point of embedded HVDC that minimizes losses within the range of compliance with the reliability criterion is proposed. We use the Power Transfer Distribution Factor (PTDF) to reduce the number of buses to be monitored during HVDC control, reduce unnecessary checks, and determine the setpoints for the active/reactive power of the HVDC through system total loss minimization (STLM) control to search for the minimum loss point using Powell’s direct set. We also propose an algorithm to search for the operating point that minimizes the loss automatically and solves the overload occurring in an emergency through security-constrained loss minimization (SCLM) control. To verify the feasibility of the algorithm, we conducted a case study using an actual Korean power system and verified the effect of systematic loss reduction and overload relief in a contingency. The simulations are conducted by a commercial power system analysis tool, Power System Simulator for Engineering (PSS/E). Full article

With the incremental introduction of solar photovoltaic (PV) generators into existing power systems, and their fast-growing share in the gross electricity generation, system voltage stability has become a critical issue. One of the major concerns is voltage fluctuation, due to large and random penetration of solar PV generators. To suppress severe system voltage deviation, reactive power control of the photovoltaic system inverter has been widely proposed in recent works; however, excessive use of reactive power control would increase both initial and operating costs. In this paper, a method for efficient allocation and control of reactive power injection using the sparse optimization technique is proposed. Based on a constrained linearized model describing the influence of reactive power injection on voltage magnitude change, the objective of this study is formulated as an optimization problem, which aims to find the best reactive power injection that minimizes the whole system voltage variation. Two types of formulations are compared: the first one is the conventional least-square optimization, while the second one is adopted from a sparse optimization technique, called the constrained least absolute shrinkage and selection operator (LASSO) method. The constrained LASSO method adds ${\ell }^1$ -norm penalty to the total reactive power injection, which contributes to the suppression of the number of control nodes with non-zero reactive power injection. The authors analyzed the effectiveness of the constrained LASSO method using the IEEE 39-bus and 57-bus power network as benchmark examples, under various PV power generation and allocation patterns. The simulation results show that the constrained LASSO method automatically selects the minimum number of inverters required for voltage regulation at the current operating point. Full article