Design Of Control Strategies Research Articles

Design of optimal control strategy for range extended electric vehicles considering additional noise, vibration and harshness constraints

Extended-range electric vehicles are considered to be the most promising pure electric vehicles that can achieve lower fuel consumption and emissions. However, during vehicle operation, range extenders cause an increase in noise, vibration and sound vibration harshness (Noise, Vibration, Harshness, NVH) during startup and operation, which can significantly reduce passenger comfort. Therefore, this study proposes a multi-objective predictive energy management strategy (MPC-NVH-Rapid DP) that can find a balance between the range extender fuel consumption, power cost, and range extender NVH. First, the multi-objective energy management problem is formulated as a total operating cost minimization problem, and an optimization strategy for range extender point selection considering NVH is designed. Subsequently, the accuracies of Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Convolutional Neural Network-LSTM (CNN-LSTM) for speed prediction in model predictive control (MPC) are compared, and a multi-objective algorithm based on MPC and NVH considerations is proposed as an energy management optimizer. Finally, the real-time performance of the proposed MPC-NVH-Rapid DP strategy is verified by MATLAB/Simulink and hardware-in-the-loop tests, and the results show that the proposed strategy achieves better NVH performance and improves driving comfort at the expense of partial fuel economy.

Reinforcement learning-based robust formation control for Multi-UAV systems with switching communication topologies

This paper introduces a novel optimal robust formation method for quadcopter multiple unmanned aerial vehicle (multi-UAV) systems. Firstly, a reinforcement learning (RL) algorithm based on a unique gradient descent training approach is proposed to solve the Hamilton–Jacobi–Bellman (HJB) equation, which can effectively eliminate the requirement of the Persistent Excitation (PE) condition. Secondly, the robustness of the controlled system is emphasized, and an Uncertainty and Disturbance Estimator (UDE) observer is developed to suppress model uncertainty and external disturbances through filtering techniques. Furthermore, a switched sliding mode control technique according to the average dwell time (ADT) is employed to convert switching communication topology between UAVs dynamically, and the stability analysis of the corresponding closed-loop control systems is then performed by the use of Lyapunov analysis. Finally, the simulation examples are provided to verify the effectiveness of the designed control strategy.

Optimized Inverse Dead‐Zone Control Using Reinforcement Learning for a Class of Nonlinear Systems

ABSTRACTIn this article, an optimized inverse dead‐zone control using reinforcement learning (RL) is developed for a class of nonlinear dynamic systems. The dead‐zone is frequently occurred in the nonlinear control system, and it can affect the control performance and even cause the system instable. Hence, it is very requisite to consider the effect of dead‐zone in the design of control strategy. In this proposed optimized inverse dead‐zone control, the basic idea is to find the optimized control as input and the adaptive algorithm to estimate the unknown parameters for the inverse dead‐zone function, so that the available dead‐zone input for system control can be derived. Comparing with traditional methods, on the one hand, the proposed dead zone inverse method is with fewer adaptive parameters, on the other hand, the RL under identifier‐critic‐actor architecture is with the simplified algorithm. Finally, theoretical and simulation results manifest the feasibility of the proposed method.

Gabapentin: An impurity profiling approach with hydrogen peroxide induced thermal oxidation in autoclave

Predicting unconventional pathways in long term stability studies can be very challenging, especially in solid state degradation. This is of vital importance for enhancement in the level of degradation and to further enable the isolation of unknown degradation products. Predicting the mechanism or pathway also help in designing control strategies to minimise the potential chances of impurity formation. In this context, the lagtime of most oxidative degradation mechanisms is extremely important, which allows enough requisite of free radicals etc., one of the unconventional pathways. This is difficult to replicate in degradation studies involving usual solution form. In this investigation, we demonstrate that employing autoclave degradation with hydrogen peroxide conditions provide an enhanced level of an unknown degradation product, which was found to be futile in traditional forced degradation studies. The conditions used probably bypass the lag time. This impurity has been isolated using preparative liquid chromatography (Prep-HPLC) method and spectroscopic techniques like high-resolution mass spectrometry (HRMS) and nuclear magnetic resonance NMR (1H, 13C, DEPT-135, NOESY, APT) for elucidation of the molecular structure. The impurity was identified as a dimer of gabapentin impurity-A (impurity (2,2′-diaza[l,2′-bispiro[4.5]decane]-3,3′-dione) and named it as impurity-U. A plausible mechanism for the formation of isolated impurity is proposed. HPLC methods have been developed and reported for the determination of gabapentin and its related substances using UV detection.

Research on Energy-Saving Control Strategies for Single-Effect Absorption Refrigeration Systems

The automatic control device is a critical component of absorption refrigeration systems. Its functional enhancement can reduce operating costs, improve energy efficiency, and ensure long-term stable unit operation. Given that absorption refrigeration systems operate under various dynamic conditions, the rational design of control strategies is particularly important. This study analyzes the influence of changes in the cooling water and heat source water flow rates on the outlet temperature of chilled water in the unit based on the open-loop response characteristics of absorption refrigeration systems. It proposes a dual-loop energy-saving control strategy for single-effect hot water lithium bromide absorption refrigeration systems based on the setpoint comprehensive optimization algorithm. Considering the multiple variables, strong coupling, large inertia, long time delay, and nonlinear characteristics of absorption refrigeration systems, as well as the difficulties in modeling these systems, this study applies a model-free adaptive control algorithm to the system’s control. It derives both SISO and MIMO model-free control algorithms with time-delay components. Through simulations comparing MFAC, improved MFAC, and traditional PID control, the dual-loop energy-saving control strategy is demonstrated to effectively reduce system heat consumption by approximately 20%, decrease power consumption by about 10%, and enhance the system’s SCOP by approximately 19.3%.

Research on Active Trailer Steering Control Strategy of Tractor Semitrailer under Medium-/High-Speed Conditions

Abstract: The study proposes an active trailer steering control method for tractor semitrailers to promote the path tracking effect of the trailer portion as well as lateral stability during lane changing. Firstly, a simplified model of a tractor semitrailer is constructed, and the MAP map is formed based on the genetic algorithm for the identification of the key parameters, which improves the model’s accuracy. Then the tractor and trailer’s yaw rate and sideslip angle at CG are tracked as the control objective and the trailer angle distribution strategy is given. Then the LQR-based corner controller is designed to control the steering actuators of each axle of the trailer. Finally, the effectiveness of the designed control strategy is verified based on the Trucksim/Simulink joint simulation platform and the semi-physical HiL test platform. The simulation results show that the designed controller can effectively improve the path tracking effect of the tractor and the trailer, and at the same time, the lateral stability parameters of the tractor and the trailer are also significantly improved, which improves the driving stability of the tractor semitrailer.

Topography control and component design strategies to enhance electrochemical performance of O3-Type cathode materials for Sodium-ion batteries

Layered transition metal oxides (NaxTMO2, 0 < x ≤ 1) are considered one of the most promising cathodes for Sodium-ion batteries (SIBs) due to their high theoretical capacity and operability. However, NaxTMO2 still faces significant challenges, including poor kinetic performance and low energy efficiency. Herein, in order to enhance the kinetics of NaNi1/3Fe1/3Mn1/3O2 (NaNFMO), topography control and component design were proposed: First, Cu doping (Cu-NaNFMO) was used to expand lattice channels and improve “flat sheet” of layered oxide, which synergistically enhanced transport kinetics of Na+. Second, Co coating (NaNFMO@Co) reacted with surface residue to form a fast-ionic conductor NaxCoO2 (0 < x ≤ 1), which inhibited interfacial side reactions and enhanced transport kinetics of Na+. Furthermore, the presence of strong Cu-O bonds in Cu-NaNFMO and fast-ionic conductor NaxCoO2 in NaNFMO@Co led to better electrochemical redox performance, the initial charge specific capacity of NaNFMO was increased from 141.5 mAh g⁻1 to 145.8 mAh g⁻1 and 144 mAh g⁻1, with capacity retention improving by nearly 10 % after 100 cycles. Furthermore, NaNFMO@Co improves ICE from 90.56 % to 93.57 %. With the improved kinetics, the DCR of pouch cells decreased, and the energy efficiency increased from 93.3 % to 94 % and 95.7 %.

Application of Artificial Intelligence Technology in Optimizing Control Parameters of Traffic Signal Group Systems

On the basis of introducing the concept of intersection signal state phase difference, the author proposes a proportional signal phase difference design scheme for traffic signal system control, on the basis of the HCM signal delay calculation formula, a standardized signal cycle and effective green signal ratio optimization design model were compiled, thus forming a complete set of optimization design modes for the control parameters of the urban main road traffic signal group system. The experimental results indicate that, the entire simulation experiment took 6000 seconds to compare the traditional fuzzy algorithm with the controller implemented by the author's fuzzy neural network, each case was simulated 5 times, the data in the Table is the average of 5 times, compared with traditional fuzzy algorithms, the author's method shows an improvement of 20.8% to 45.67% in average vehicle delay time, especially when the traffic flow is different in the east-west and north-south directions, the improvement is more significant.

RSSR Mechanism Design and Motion Control Strategy of a Carbon-Free Vehicle for Obstacle Avoidance Competition

RSSR Mechanism Design and Motion Control Strategy of a Carbon-Free Vehicle for Obstacle Avoidance Competition

Molecular prevalence, epidemiology, and phylogenetic analysis of Babesia microti in dogs with a note on its impact on host hematological profile

Babesia (B.) microti is an intra-erythrocytic protozoan parasite that infects humans as well as domestic and wild animals. Prevalence of B. microti was investigated in 654 apparently healthy dogs belonging to 55 different breeds from three districts in Punjab province (Muzaffargarh, Bahawalpur and Jhang) and two districts in Khyber Pakhtunkhwa province (Dir Upper and Charsadda) in Pakistan. The hematological profile of dogs, risk factors associated with the infection and phylogenetic diversity of the detected isolates were also evaluated. In total, 29 blood samples (4 %) scored PCR positive. Sanger sequencing of partial 18S rRNA gene confirmed the presence of B. microti. The phylogenetic analysis of the sequences based on the 18S rRNA gene displayed global phylogenetic similarity with the isolates that were previously documented from Russia, France, Poland, Spain, China, Japan and USA. The infection rate was consistent across different sampling sites and dog breeds. Sex or presence of ectoparasites on dog was also not associated with B. microti prevalence. Babesia microti infected dogs had elevated red cell distribution width-coefficient of variation (%) than uninfected animals. This study presents updated data about the prevalence of B. microti among local Pakistani dogs and will be helpful in designing control strategies against this tick-borne pathogen as the tick infesting a B. microti infected dog may transmit this parasites to human as well.

Physics-Informed Machine Learning for Calibrating Macroscopic Traffic Flow Models

Well-calibrated traffic flow models are fundamental to understanding traffic phenomena and designing control strategies. Traditional calibration has been developed based on optimization methods. In this paper, we propose a novel physics-informed, learning-based calibration approach that achieves performances comparable to and even better than those of optimization-based methods. To this end, we combine the classical deep autoencoder, an unsupervised machine learning model consisting of one encoder and one decoder, with traffic flow models. Our approach informs the decoder of the physical traffic flow models and thus induces the encoder to yield reasonable traffic parameters given flow and speed measurements. We also introduce the denoising autoencoder into our method so that it can handle not only with normal data but also corrupted data with missing values. We verified our approach with a case study of Interstate 210 Eastbound in California. It turns out that our approach can achieve comparable performance to the-state-of-the-art calibration methods given normal data and outperform them given corrupted data with missing values. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference. Funding: This study was supported by the National Science Foundation [Grant CMMI-1949710] and the C2SMART Research Center, a Tier 1 University Transportation Center.

Fuzzy logic based sliding surface adjustment of second-order sliding mode controllers

&lt;p&gt;This research work designs a variant of second-order sliding mode control scheme, making use of varying sliding surface inferred using a fuzzy inference system. The varying sliding surface is an effective strategy to improve controller performance. A surface with a relative degree of two is first built by accounting for the uncertainties and perturbances of the system. Thereafter, in order to enhance the dynamics of the system being controlled, a varying sliding surface based on a straightforward double input-single output fuzzy logic inference architecture is proposed. The controller ensures system's reaching conditions, and also the stability and robustness. The designed control scheme is studied in comparison with a sliding mode controller of second order having a constant surface of sliding using SIMULINK based simulation for a nonlinear system. The comparison shows that the proposed strategy exhibits an improved dynamic performance than the conventional sliding mode control of second order having a constant surface of sliding.&lt;/p&gt;

Model-driven design and validation of glucose supply control in a tubular photobioreactor operated under oxygen-balanced mixotrophy

Scaling up bioprocesses remains challenging due to the partially unpredictable and suboptimal behavior of microbes during scale transition. Mathematical modelling serves as a valuable tool in navigating these challenges. In this study, we developed and validated a kinetic model based on the tank-in-series approach for a novel bioprocess known as oxygen-balanced mixotrophy with the microalga Galdieria sulphuraria. This model aims to facilitate the design of an effective glucose feeding control strategy tailored for two-phase tubular photobioreactors (TPBRs). The residence time distribution of our reference reactor was investigated by means of a tracer experiment. Qualitative analysis of the results indicated that our system was optimally represented by simulations with a 100 stirred-tank reactors (STRs).The controlled variable, oxygen concentration in the gas phase, showed a downward trend in simulations with simplified conditions: fixed oxygen production and balanced glucose supply. The decrease was caused by dominant net heterotrophic activity and accumulation of CO2 in the gas phase. Different configurations of a proportional-integral-derivative (PID) controller were designed by means of the ultimate gain method and compared under these simplified conditions. The most effective PID controller was implemented and refined in simulations with outdoor weather conditions. The model was validated successfully by simulating over time in a lab-scale STR the spatial fluctuations happening in a TPBR. The empirical oxygen consumption and production were faster and steeper than in the model, probably due to inaccuracies in parameter estimation or biomass adaptation.

Network Modeling and Control of Dynamic Disease Pathways, Review and Perspectives.

Dynamic disease pathways are a combination of complex dynamical processes among bio-molecules in a cell that leads to diseases. Network modeling of disease pathways considers disease-related bio-molecules (e.g. DNA, RNA, transcription factors, enzymes, proteins, and metabolites) and their interaction (e.g. DNA methylation, histone modification, alternative splicing, and protein modification) to study disease progression and predict therapeutic responses. These bio-molecules and their interactions are the basic elements in the study of the misregulation in the disease-related gene expression that lead to abnormal cellular responses. Gene regulatory networks, cell signaling networks, and metabolic networks are the three major types of intracellular networks for the study of the cellular responses elicited from extracellular signals. The disease-related cellular responses can be prevented or regulated by designing control strategies to manipulate these extracellular or other intracellular signals. The paper reviews the regulatory mechanisms, the dynamic models, and the control strategies for each intracellular network. The applications, limitations and the prospective for modeling and control are also discussed.

Hopf bifurcation control for the traffic flow model considering the tail light effect

The research on traffic congestion control has seen rapid development in recent years. Investigating the bifurcation characteristics of traffic flow and designing control schemes for unstable bifurcation points can offer new methods for alleviating traffic congestion. This paper focuses on studying the bifurcation characteristics and nonlinear control of traffic flow based on the continuous model and the taillight effect. Firstly, the traffic flow model is transformed into a stability model suitable for branching analysis through the use of the traveling wave transform. This transformation facilitates the analysis of stability that reflects unstable traffic characteristics such as congestion. Based on this stability model, the existence condition of Hopf bifurcation is proved and some bifurcation points of the traffic system are identified. Secondly, the congestion and stability mutation behaviors near equilibrium and branching points are studied to understand the formation mechanism of traffic congestion. Finally, control schemes are designed using Chebyshev polynomial approximation and stochastic feedback control to delay or eliminate unstable bifurcation points and relieve traffic congestion. This improved traffic flow model helps explain changes in system stability through bifurcation analysis and identify unstable bifurcation points. It can also effectively manage these points by designing a feedback controller. It is beneficial for controlling sudden changes in traffic system stability behavior and mitigating traffic congestion, with important theoretical significance and practical application value.

Reflux Power Optimization of a Dual-Active Hybrid Full-Bridge Converter Based on Active Disturbance Rejection Control

The dual-active hybrid full-bridge (H-FDAB) DC–DC converter has great potential in medium-voltage high-power photovoltaic power station applications by introducing a three-level bridge arm to increase the output voltage range. However, its mathematical model and optimum modulation schemes have not been fully explored. Under the traditional PI control, the H-FDAB DC–DC converter will produce significant reflux power, which will lead to a decrease in converter efficiency and output voltage fluctuation. On this basis, this paper proposes a reflux power optimization strategy for an H-FDAB DC-DC converter based on active disturbance rejection control (ADRC). Firstly, the structure and power characteristics of the H-FDAB DC–DC converter are analyzed, and the relationship among the reflux power, the transmission power, and the phase shift angle is derived. Secondly, to reduce the complexity of the control calculation, upon the foundation of dual phase-shifting modulation, the Karush–Kuhn–Tucker (KKT) condition is used to solve for the phase shift angle that corresponds to the minimum reflux power. Simultaneously, we develop an ADRC loop utilizing an extended state observer (ESO) for the real-time estimation of system states. We also consider the sudden changes in input voltage, load switching, and transmission power fluctuations caused by reflux power optimization strategies as system disturbances and compensate for them accordingly. Finally, the experiments conclusively validate the designed control strategy’s correctness and feasibility.

Development of new framework for order abatement and control design strategy

Development of new framework for order abatement and control design strategy

Counter-rotating synchronization theory and experiment of tri-rotor actuated with dual-frequency considering adaptive global sliding mode control strategy

In petroleum drilling engineering, self-synchronous vibration screen with single-frequency actuation is difficult to implement the scheduled synchronous behavior caused by its invariance and instability of dynamic characteristics, which usually leads to reduction of the production efficiency. Hence, to solve the problem mentioned above, a dual-frequency counter-rotating synchronization control method among three exciters is presented by combining adjacent cross-coupling control (ACCC) structure with global sliding mode control (GSMC) theory. Firstly, motion differential equation of the vibration system in each direction is derived according to mathematical model of the tri-rotor vibration machine. Secondly, considering exponential reaching law and adaptive control algorithm, the adjacent cross-coupling controller and the vector controller of each induction motor are investigated to achieve the stable zero phase difference synchronization between double-frequency rotors. Subsequently, Runge-Kutta method is introduced to establish the electromechanical coupling control model and prove validity of the control theory. Meanwhile, it is also discussed that the rotors cannot obtain an ideal self-synchronization state since velocity of high-frequency motor is not strictly equal to twice that of low-frequency motor. Finally, based on the experimental test scheme, an experimental prototype related to controlled multi-rotor synchronization vibration system is designed independently, which further verifies the validity of theory and computer simulation. Research shows that the desirable synchronous state of the vibration system can be accurately controlled via the designed control strategy, and the control system can implement diversity of the dynamical characteristics, due to the existence of strong robustness.

Distributed cooperative control for global power sharing management of interconnected microgrids in flexible multi-terminal DC connection scheme

Flexible multi-terminal direct current (MTDC) connection scheme is an ideal way to integrate multiple adjacent microgrids and form interconnected microgrids (IMGs). However, under the traditional control mode, MTDC will lead to frequency/voltage decoupling among all the microgrids. Moreover, improper control scheme design of bidirectional interlinking converters (BICs) in MTDC will make global active/reactive power sharing management impossible, which is considered as the significant task in IMGs. Therefore, a novel distributed cooperative control strategy is designed in this article. In the proposed control strategy, frequency/voltage regulations and local active/reactive power sharing management are achieved through distributed secondary control within each MG. Accurate global active/reactive power sharing management is realized by coordinating the power exchange instructions of each BIC in a distributed manner. The control dynamics of these two levels are designed to be decoupled. The stability of the proposed control strategy is demonstrated by the Lyapunov method, and its effectiveness is verified by simulating a test IMG system in MATLAB/Simulink.

Control of DC-DC boost converter in discontinuous conduction mode feeding a constant power load

This article presents a new control strategy for a DC-DC boost converter used to supply a constant power load. The designed controller allows regulates the converter output voltage to a required reference value. By designing the controller based on the reduced order averaged model of the converter and using a nonlinear control technique based on feedback linearization, a simple-to-implement and stable controller for both operating modes of the converter (continuous and discontinuous conduction modes respectively) is obtained. The designed control strategy is validated through simulation and experimental results, demonstrating good dynamic performance and stability in both conduction modes, under variations in reference values and significant load changes. In addition, a comparison of the performance and implementation costs between the control strategy proposed in this paper and two strategies proposed by other authors is included.