Design Of Control Strategies Research Articles

Reinforcement Learning with Decoupled State Representation for Robot Manipulations

Deep reinforcement learning has significantly advanced robot manipulations by providing an alternative solution for designing control strategies using raw images as direct inputs. While images offer additional environmental information, the end-to-end policy training manner (from image to action) requires simultaneous representation and task learning by the agent. This often necessitates a substantial number of interaction samples to achieve satisfactory policy performance. Previous works has attempted to address this challenge by learning a visual representation model that encodes the entire image into a low-dimensional vector before the policy training. However, since this vector contains both robot and object information, it inevitably introduces coupling within the state, which can mislead the policy training process. In this study, a novel method called Reinforcement Learning with Decoupled State Representation is proposed to effectively decouple robot and object information within the state representation. Experimental results demonstrate that the proposed method exhibits faster learning speed and achieves superior performance compared to previous methods across various robot manipulation tasks. Moreover, with only 3096 offline images, the proposed method successfully applies to real-world robot pushing tasks, which demonstrates its high practicability.

Development of a high-fidelity digital twin using the discrete element method for a continuous direct compression process. Part 1. Calibration workflow

In this work, a high-fidelity digital twin was developed to support the design and testing of control strategies for drug product manufacturing via direct compression. The high-fidelity digital twin platform was based on typical pharmaceutical equipment, materials, and direct compression continuous processes. The paper describes in detail the material characterization, the Discrete Element Method (DEM) model and the DEM model parameter calibration approach and provides a comparison of the system’s response to the experimental results for stepwise changes in the API concentration at the mixer inlet. A calibration method for a cohesive DEM contact model parameter estimation was introduced. To assure a correct prediction for a wide range of processes, the calibration approach contained four characterization experiments using different stress states and different measurement principles, namely the bulk density test, compression with elastic recovery, the shear cell, and the rotating drum. To demonstrate the sensitivity of the DEM contact parameters to the process response, two powder characterization data sets with different powder flowability were applied. The results showed that the calibration method could differentiate between the different material batches of the same blend and that small-scale material characterization tests could be used to predict the residence time distribution in a continuous manufacturing process.

Finite-time output-feedback fault tolerant adaptive fuzzy control framework for a class of MIMO saturated nonlinear systems

This paper presents a finite-time fault-tolerant fuzzy adaptive controller for uncertain interconnected nonlinear systems in strict-feedback form. The controller addresses input saturation, state-dependent actuator faults, external disturbances, and unavailable states for measurement. To avoid any unexpected alteration due to failures, the proposed control scheme design addresses all types of actuator faults that are bias, drift, and loss of accuracy as additive faults, as well as a multiplicative fault that results in loss of effectiveness with nonaffine state-dependency. Restrictions on control gains and unmatched interconnections are eliminated, and the explosion complexity caused by recursive back-stepping designs is avoided. Fuzzy Systems approximate the unknown ideal control laws along side the acutator faults and disturbances. The stability analysis guarantees that tracking errors converge to a small compact set around the origin in finite time. Simulation results on a quad-rotor UAV and a mathematical system confirm the effectiveness of the proposed approach.

Adaptive sliding mode control of petrochemical flare combustion process based on radial basis function network

Steam-assisted combustion elevated flares are currently the most widely used type of petrochemical flares. Due to the complex and variable composition of the waste gas they handle, the combustion environment is severely affected by meteorological conditions. Key process parameters such as intake composition, flow rate, and real-time data of post-combustion residues are difficult to measure or exhibit lag in data availability. As a result, the control methods for these flares are limited, leading to poor control effectiveness. To address this issue, this paper proposes an adaptive sliding mode control method based on the Radial Basis Function (RBF) network. Firstly, the operational characteristics of the petrochemical flare combustion process are analyzed, and a control model for the combustion process is established based on carbon dioxide detection. Secondly, an RBF neural network-based unknown function approximator is designed to identify the nonlinear part of the actual operating system. Finally, by combining the control model of the petrochemical flare combustion and designing the RBF sliding mode controller with its adaptive control law, fast and stable control of the flare combustion state is achieved. Simulation results demonstrate that the designed control strategy can achieve tracking control of the petrochemical flare combustion state, and the adaptive law also accomplishes system identification.

A cooperative distributed droop gains adjustment in DC microgrids

Direct Current (DC) microgrids are a very promising solution to integrate renewable energy sources, energy storage systems and loads. The main goals of a microgrid controller are to achieve voltage regulation and load sharing among multiple distributed generators (DGs). Distributed control techniques are widely adopted to address these two objectives. Most of the existing distributed methods in the literature adjust the deviation caused by the conventional droop control by adding two additional terms. In this work, a new cooperative distributed controller to adjust the droop gains of DGs is proposed. The proposed method achieves average voltage regulation and proportional current sharing in a unified manner. It does not require additional corrective terms, resulting then in a simple control structure. Moreover, it relies only on exchanging one communication variable among neighboring DGs, which reduces the communication overhead. The stability of the proposed controller is analyzed using a small signal model of the system. The effectiveness of the proposed controller is evaluated under common load changes, local load variations, communication link failures and communication delays. The designed control scheme is verified through Hardware-In-the-Loop (HIL) tests using NI PXIe real-time simulator.

Adaptive robust fault-tolerant control of spinning tether system for space debris removal

The Spinning Tether System (STS) is currently recognized as one of the most effective and ideal platforms for removing space debris. It offers several advantages, including a non-contact mechanism, rapid deorbit capabilities, stable spinning, and a short preparation time for subsequent missions. The system primarily depends on the thrusters of the main spacecraft and the capture device for its spin-up process. Although modern technology and quality management methods strive to minimize thruster failures, the capture of debris significantly affects the STS. This impact can induce tether oscillations and cause actuator failures, thereby destabilizing the spin-up process. Given these challenges, this paper explores the design of fault-tolerant control strategies to enhance system reliability during the post-capture spin-up phase. Initially, a dynamic model using quaternions as generalized coordinates was established based on the constrained Lagrange equation. This model facilitated a detailed analysis of potential thruster failure modes. Subsequently, the optimal trajectory for the spin-up process was planned using the pseudo-spectral method, ensuring efficiency and safety in control execution. To address the identified vulnerabilities, the study introduces an adaptive robust fault-tolerant controller. This controller, based on a BLF and integrated with a nonlinear neural network disturbance observer, is designed to operate effectively under fault conditions. Numerical simulations were conducted to validate the performance of the controller, demonstrating its capability to maintain stable control of the system, both after detecting a fault signal and following an actuator failure.

Aerodynamic Behavior of a Wind Turbine Under Blade Pitching Motion

It is essential for advanced turbine design and wind-farm control strategy formulation to acquire an improved understanding of the aerodynamic behavior in the rotor plane and the wake downstream of a wind turbine under blade pitching motion, reacting to highly stochastic atmospheric conditions. Using an unsteady Reynolds-averaged Navier–Stokes simulation, the effects of the dynamic variations in blade pitch are investigated on the unsteady aerodynamic behavior of an NREL-5 MW wind turbine. The results show that the wake after a pitching maneuver exhibits a local contraction region, resulting in the leapfrogging phenomenon of tip vortices. The influences of different pitch ranges and pitch rates on the aerodynamic behavior of the wind turbine are studied. The degree of wake contraction depends strongly on the pitch range, whereas an increase in pitch rate is mainly dominated by the shed vorticity, indicating a larger deviation from the quasi-state behavior. In addition, interaction between the shed vorticity and the neighboring tip vortex is observed in the near wake under the condition at a fast pitch rate, which may have an influence on the mutual inductance instability.

Wolbachia infection in natural mosquito populations from Argentina.

The increasing spread of mosquito vectors has made mosquito-borne arboviral diseases a global threat to public health, leading to the urgent need for effective population control methods. Strategies based in the intracellular bacterium Wolbachia Hertig, 1936 are considered environmentally friendly, safe for humans, and potentially cost-effective for controlling arboviral diseases. To minimize undesirable side effects, it is relevant to assess whether Wolbachia is present in the area and understand the diversity associated with native infections before implementing these strategies. With this purpose, we investigated Wolbachia infection status, diversity, and prevalence in populations of Aedes albifasciatus (Macquart, 1838), Aedes fluviatilis (Lutz, 1904), and hybrids of the Culex pipiens (Linnaeus, 1758) complex from Argentina. Aedes albifasciatus and C. pipiens complex samples were collected in the province of Buenos Aires, and A. fluviatilis in the province of Misiones. Aedes albifasciatus was found to be uninfected, while infections with strains wFlu and wPip were detected in A. fluviatilis and hybrids of the C. pipiens complex, respectively. All strains were fixed or close to fixation and clustered within supergroup B. These finding provides valuable information on Wolbachia strains found in natural mosquito populations in Argentina that might be used in heterologous infections in the future or be considered when designing control strategies based on Wolbachia infection.

The Effect of Reynolds Numbers on Flow-Induced Vibrations: A Numerical Study of a Cylinder on Elastic Supports

In the field of fluid dynamics, the Reynolds number is a key parameter that influences the flow characteristics around bluff bodies. While its impact on flow around stationary cylinders has been extensively studied, systematic research into flow-induced vibrations (FIVs) under these conditions remains limited. This study utilizes numerical simulations to explore the FIV characteristics of smooth cylinders and passive turbulence control (PTC) cylinders supported elastically within a Reynolds number range from 0.8 × 104 to 1.1 × 105. By comparing the vibration responses, lift coefficients, and wake structures of these cylinders across various Reynolds numbers, this paper aims to elucidate how Reynolds numbers affect the flow and vibration characteristics of these structures. The research employs images of instantaneous lift changes and vortex shedding across multiple sections to visually demonstrate the dynamic changes in flow states. The findings are expected to provide theoretical support for optimizing structural design and vibration control strategies in high-Reynolds-number environments, emphasizing the importance of considering Reynolds numbers in structural safety and design optimization.

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.