Self-adaptive Control Research Articles

Detection of Wheat Lodging by Binocular Cameras during Harvesting Operation

Wheat lodging provides important reference information for self-adaptive header control of a combine harvester. Aimed at real-time detection of wheat lodging, this paper proposed a detection method of wheat lodging location and area based on binocular vision. In this method, the angle relationship between the stem and vertical direction when wheat is upright, inclined, and lodging was determined by mechanical analysis. The discrimination condition of the wheat lodging degree was proposed based on the height of the visual point cloud on the surface of wheat crops. The binocular camera was used to obtain the image parallax of wheat within the harvesting region. The binocular camera optical axis parallel model was used to calculate the three-dimensional coordinate of wheat. Then, the height of the wheat stem was obtained by further analysis and calculation. According to the wheat stem height detected by vision, the location and area of wheat lodging within the combine harvester’s harvesting region were analyzed. A field experiment showed that the detection error of the wheat stem height was 5.5 cm and the algorithm speed was under 2000 milliseconds, which enabled the analysis and calculation of the wheat lodging location, contour, and area within the combine harvester’s harvesting region. This study provides key information for adaptive header control of combine harvesters.

Flow-drag reduction performance of a resident electrolytic microbubble array and its mechanisms

This work examines the problem of stable high-efficiency flow drag reduction for underwater vehicles using microbubble arrays. To this end, test pieces for micropore arrays capable of forming self-stabilizing arrays of microbubbles via electrolysis have been prepared. The quantitative relationships between the electrolysis voltage, micropore size, and flow velocity (v) and the growth behaviors and residence stability of electrolyzed microbubbles were examined by experimental and numerical methods. The flow drag reduction performance and mechanisms of microbubble arrays were investigated. It has been shown that an electrode-wall micropore array can be used to achieve self-adaptive start-stop control of microbubble electrolysis. A micropore diameter-to-depth ratio of k = 2 was found to be optimal for generating arrays of electrolytic microbubbles with good stability in flow. Two types of microbubbles are generated: the air-film type exhibits greater resident stability than the protruding type. The average measured flow-drag-reduction rate of a microbubble array with 250 μm micropores was found to be around 26%. The energy-loss region of free-fluid flow with a solid wall was 0.037 mm thicker than that with a microbubble-array wall (v = 0.5 m/s), meaning that more energy is consumed, leading to greater flow drag. Significant up-thrown flow induced by elastic deformation of microbubbles and air/water interface forces can suppress turbulent coherent structural bursts and reduce near-wall Reynolds shear stress. The mean values of turbulence-related characteristic parameters (vorticity = 890.55 s−1, kinetic energy = 0.08096 m2/s2, shear stress = 17.75 Pa, and v = 0.5 m/s) of the boundary-layer flow over the microbubble-array wall were found to be greatly reduced. This demonstrates the good turbulent friction drag reduction performance of the resident microbubble array.

Cultivation of microbial cultures at high cell density in microbioreactor fermentations to increase protein yield.

Microbioreactors have been proven to be a useful tool in high-throughput applications, such as clone screening, synthetic library testing, and media optimization. Most were designed for low cell density applications, where the optical density of the cultures typically does not exceed an OD600 of 10. In microbial applications, where protein is to be expressed, such a scale is not sufficient to produce material for extensive target molecule testing. Here, we present a method for growing high-cell density Escherichia coli cultures in milliliter-scale bioreactors, to produce milligram quantities of target protein. We used a micro-Matrix system with a starting volume of 3ml per culture. A combination of defined medium, a fed-batch feeding strategy at low temperature, and an advanced self-adapting control algorithm achieved up to 0.7g of wet cell weight (WCW) in a 5.7ml final culture volume, which corresponds to 123g/L WCW. This translates to an estimated protein yield of 1150mg of target protein per liter final volume.

Online Detection of Impurities in Corn Deep-Bed Drying Process Utilizing Machine Vision.

Online detection of impurities content in the corn deep-bed drying process is the key technology to ensure stable operation and to provide data support for self-adapting control of drying equipment. In this study, an automatic approach to corn image acquisition, impurity classification and recognition, and impurities content detection based on machine vision technology are proposed. The multi-scale retinex with colour restore (MSRCR) algorithm is utilized to enhance the original image for eliminating the influence of noise. HSV (Hue, saturation, value) colour space parameter threshold is set for image segmentation, and the classification and recognition results are obtained combined with the morphological operation. The comprehensive evaluation index is adopted to quantitatively evaluate the test results. Online detection results show that the comprehensive evaluation index of broken corncobs, broken bracts, and crushed stones are 83.05%, 83.87%, and 87.43%, respectively. The proposed algorithm can quickly and effectively identify the impurities in corn images, providing technical support and a theoretical basis for monitoring impurities content in the corn deep-bed drying process.

Automatic Cutting Speed Control System of Boom-Type Roadheader Based on Geological Strength Index

The boom-type roadheader is the foremost mining equipment in coal mines. At present, the automatic cutting technology is still immature for adjusting cutting speed automatically in accordance with rock strength, resulting in energy dissipation. In this study, we put forward a method with respect to detecting the geological strength index of coal seam profile through visual inspection, as well as characterize the geological strength index and control the cutting head for adjusting speed automatically based on inspecting fracture features on coal rock’s surface, aiming at achieving energy conservation control of boom-type roadheader. The image processing algorithm is adopted for detecting joint characteristics of palisades fracture, and a quantitative model of the geological strength index is established. The fractal dimension is used to obtain the distribution of geological strength indicators of a coal seam, and the heading machine’s cutting head is controlled for adjusting speed automatically. A vision control platform of boom-type roadheader is built in the laboratory to perform ground simulation experiments. According to experimental results, the difference between the geological strength index of the coal seam detected through visual inspection and the set value in the geological strength index chart is up to 3.5%, and the results are basically consistent, so the quantification of geological strength index can be performed rapidly and effectively. The average energy consumption of boom-type roadheader decreases by 5.4% after adopting self-adaptation control, realizing energy conservation and consumption reduction as well as intelligent control of coal mine machinery.

Design of Smart Control System of Wing Anti-icing

A self-adaptive neural network-based control method in aircraft anti-ice systems is introduced. To get better accuracy and stability in wing anti-ice system control, a self-adaptive PID control is applied to drive the heater groups distributed around the wing and powered by remote power distribution units. Unlike the traditional system, the type of heater command is substituted from constant frequency to vary the frequency, and the control signals are given by PID controller whose three parameters propagation, integral and derivative, are given by backpropagation neural networks. Furthermore, in the tuning process, the plant’s predictive output is used to modify the weights of neural networks. The plant’s output will also be predicted by BP neural networks, and it is a nonlinear prediction that improves the predictive accuracy. The implementation and simulation program was written by MATLAB/Simulink.

An innovative spinning-rolling process and its intelligent FE modelling for big-tapered profiled ring disk

The big-tapered profiled ring disk is mainly employed by rocket rear joint, which has a typical geometric feature of big taper and profiled section shape. Aiming at challenges for its manufacturing, an innovative spinning-rolling process based on typical ring rolling mill is proposed in this work. The principle of this new process is expounded, which combines the characteristics of spinning and profiled ring rolling. In view of difficulties of controlling the motions of the guide rolls, an intelligent control method, which combines the flexible force control with instantaneous position control of the guide rolls, is proposed considering the transformation of deformation patterns from spinning bending to profiled ring rolling of the process. Based on this, an intelligent 3D FE model for the proposed spinning-rolling process, which can realize self-adaptive control of the guide rolls, is developed. The actual spinning-rolling experiment has been performed under the same deformation conditions as the FE simulations. The results fully demonstrate that the newly proposed spinning-rolling process is feasible and the developed FE model is reliable. Then the developed FE model is further applied to explain the deformation principle and characteristics, predict the force and power parameters and find the problem on process window in the spinning-rolling process. And lastly the future work mainly on process window and deformation rules has been pointed out. • A newly proposed spinning-rolling process combining spinning bending and profiled ring rolling • Control method and model of guide rolls with flexible force and instantaneous position control • An intelligent FE model of spinning-rolling process which realizes adaptive control for guide rolls • An actually performed experiment on spinning-rolling process which proves the process feasibility and FE model reliability • The roles of the developed FE model in process innovation, equipment upgrades and process window establishment

A multi-objective differential evolutionary algorithm for optimal sustainable pavement maintenance plan at the network level

Sustainable highway pavement maintenance is important for achieving sustainability in the transportation sector. Because the three aspects included in sustainability metrics (environment, economy, and society) often contradict each other, maximising the sustainability performance of highway pavements is difficult, especially at the network level. This study developed a novel multi-objective heuristic algorithm to formulate sustainable highway pavement network maintenance plans considering carbon emissions (CE), life cycle agency cost (LCAC), and pavement long-term performance (LTP). The proposed algorithm is a new variant of multi-objective differential evolution (MODE) that incorporates self-adaptive parameter control and hybrid mutation strategies embedded in its framework (MOSHDE). Three state-of-the-art multi-objective heuristics, namely, the non-dominated sorting genetic algorithm II(NSGA-II), classic MODE, and multi-objective particle swarm optimisation (MOPSO), as well as the proposed MOSHDE, were applied to an existing highway pavement network in China for performance evaluation. Compared with other heuristic algorithms, the proposed self-adaptive parameter control strategy enables the automatic adjustment of the control parameters, avoiding the time-consuming process of selecting them and enhancing the robustness and applicability of differential evolution. The hybrid mutation strategy uses both exploration and exploitation operators for the mutation operations, thus leveraging both global and local searches. The results of the numerical experiment demonstrate that MOSHDE outperforms the other tested heuristics in terms of efficiency and quality and diversity of the obtained approximate Pareto set. The optimal solutions obtained by the proposed method correspond to a proactive maintenance policy, as opposed to the reactive maintenance policy commonly adopted in current practice. In addition, these solutions are more cost-effective and environmentally friendly and can provide better pavement performance to highway users over the project life cycle. Therefore, the proposed MOSHDE may help practitioners in the transportation sector make their highway infrastructure more sustainable.

Exploring self-organization and self-adaption for smart manufacturing complex networks

Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch, short-cycle, and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments, which poses great challenges to manufacturing enterprises. Fortunately, recent advances in the Industrial Internet of Things (IIoT) and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber—physical systems for smart, flexible, and resilient manufacturing systems. In this context, this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes. Specifically, a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels. Moreover, the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology, which can be added to or removed from the networks in a plug-and-play manner. Materials, information, and financial assets are passed through interactive links across the networks. Subsequently, analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices. Consequently, an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions. The simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method, reducing manufacturing cost, manufacturing time, waiting time, and energy consumption, with reasonable computational time. This work potentially enables managers and practitioners to implement active perception, active response, self-organization, and self-adaption solutions in discrete manufacturing enterprises.

Flatfish: A Reinforcement Learning Approach for Application-Aware Address Mapping

The DRAM performance has become a critical bottleneck of modern computing systems. Prior studies have proposed various optimization techniques on address mapping to bridge the gap between real performance and the peak performance. Nevertheless, these techniques have some common limitations. First, most of them focus on an indirect metric (e.g., bitwise flip ratio) and fail to address the effects of complicated organization hierarchy and timing constraints of DRAM. Second, these approaches do not leverage application-specific information and may not generate the proper address mapping schemes for modern applications. In this article, we propose Flatfish as a comprehensive solution to address these challenges. Flatfish is a self-adaptive memory controller that is able to generate address mapping schemes according to the memory access pattern. Different from prior approaches, Flatfish considers complicated memory hierarchy, including channel, rank, and bank group and addressed critical timing constraints. By mining the characteristics from the memory access traces, Flatfish integrates a reinforcement learning model to generate a binary invertible matrix (BIM) as the address mapping scheme. Flatfish can work in either offline mode or online mode to meet various requirements in different scenarios. The experimental results show that Flatfish can achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.91\times $ </tex-math></inline-formula> speedup in the offline mode on GPU, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.63\times $ </tex-math></inline-formula> speedup in the online mode on CPU, over the commonly used Hynix address mapping scheme.

Seamless Transition for Parallel Inverters With Novel Self-Adaptive Hybrid Controller and Presynchronization Unit

This article proposes a seamless transition strategy between grid-connected (GC) and stand-alone (SA) states for a microgrid (MG) consisting of parallel inverters. Most inverters in MG operate with novel self-adaptive hybrid controllers, and one inverter near the point of common coupling (PCC) acts as presynchronization (PS) unit. When islanding happens, the inverters based on self-adaptive hybrid control can inherently transfer from grid current control in the GC state to droop control in the SA state, critical islanding detection is needless. When the grid restores, the PS unit can regulate its reactive power to influence the output voltage phase of other inverters in MG and realize the PS of MG and grid. The main advantages are concluded into three points. First, seamless transition of parallel inverters is realized without reconfiguration of control structure, and power-sharing among parallel inverters is achieved without communication lines. Second, unlike droop control, the self-adaptive hybrid control can regulate the grid current of the inverter accurately when the grid fluctuates or is distorted. Third, the PS unit can save the complexity and cost of remote communication for PS. The effectiveness of the proposed seamless transition strategy is verified by simulation and experiment results.

Soft Conductive Hydrogel-Based Electronic Skin for Robot Finger Grasping Manipulation.

Electronic skin with human-like sensory capabilities has been widely applied to artificial intelligence, biomedical engineering, and the prosthetic hand for expanding the sensing ability of robots. Robotic electronic skin (RES) based on conductive hydrogel is developed to collect strain and pressure data for improving the grasping capability of the robot finger. RES is fabricated and assembled by the soft functional materials through a sol–gel process for guaranteeing the overall softness. The strain sensor based on piezoresistive hydrogel (gauge factor ~9.98) is integrated onto the back surface of the robot finger to collect the bending angle of the robot finger. The capacitive pressure sensor based on a hydrogel electrode (sensitivity: 0.105 kPa−1 below 3.61 kPa, and 0.0327 kPa−1 in the range from 4.12 to 15 kPa.) is adhered onto the fingertip to collect the pressure data when touching the objects. A robot-finger-compatible RES with strain and pressure sensing function is designed for finger gesture detection and grasping manipulation. The negative force feedback control framework is built to improve grasping manipulation of the robot finger with RES, which would provide a self-adaptive control method to determine whether the objects are grasped successfully or not. Robot fingers integrated with soft sensors would promote the development of sensing and grasping abilities of the robot finger and interaction with human beings.

Self-Adapted Model Predictive Current Control for Five-Phase Open-End Winding PMSM With Reduced Switching Loss

This article proposed a self-adapted model predictive current control (SA-MPCC) method to reduce the switching loss of the five-phase open-end winding permanent-magnet synchronous motor (OW-PMSM). The proposed SA-MPCC method regards the OW-PMSM drive system as two separate parts by utilizing the flexibility of the OW-PMSM system, thus generating and applying the phase voltage to armature winding independently. The virtual voltage vectors and multivector duty cycle modulation strategy is adopted to maintain a good steady-state and dynamic behavior. Then, the self-adapted decision is generated to choose one inverter work solely, or two inverters work cooperatively. When one inverter is used, another inverter is closed. The first inverter is clamped to optimal virtual voltage vectors when two inverters work together. The clamping to virtual voltage vectors or zero vectors can reduce the switching loss in both sides of the dual inverter. Finally, the experimental results verify the effectiveness of the proposed SA-MPCC method.

Control of Single-Phase Distributed PV-Battery Microgrid for Smooth Mode Transition With Improved Power Quality

In this article, multiple parallel photovoltaic (PV) inverters based microgrid is developed to enhance the reliability and accessibility of electricity in remote areas. A self-adaptive control strategy is presented for a microgrid to ensure a seamless transition between the islanded mode (IAM) and grid-connected mode (GCM) of operation with a proportional power sharing. This microgrid consists of two solar PV inverters based on local distributed generation (DG) units. Each PV-inverter unit feeds an active power to support local loads and the power demand of each local load is shared between respective DG units and microgrid. The PV-battery-interfaced main unit with a self-adaptive control strategy in parallel with a PV array supported unit-2 is considered to operate in the IAM and GCM of operation. The control strategy achieves transient free operation during load disturbances, power variation at the PV array side, and off-grid operation. Moreover, the microgrid ensures high-quality performance at the point of common coupling and provides the seamless transition between GCM and IAM to guarantee a continuous power to critical loads. The microgrid simulation and experimental results are presented under various operating conditions, such as load variation, intermittent power generation, and automated mode transition between GCM and IAM.

The first International Conference on Mechanical System Dynamics grandly convened in Nanjing, China

The first International Conference on Mechanical System Dynamics grandly convened in Nanjing, China

1D and 3D co-simulation and self-adaptive position control of electrostatic levitation in China’s Space Station

The greatest challenge of electrostatic levitation for containerless material processing is the stable control of charged material during heating. Recently, high-precision self-adaptive control of electrostatic levitation has been achieved in China’s Space Station. Based on the 1D and 3D co-simulation analysis, an optimal scheduling of control strategies of sample release and retrieval in space is developed. Both simulation results and on-orbit experiments demonstrated that the inversion of surface charge is responsible for the heating induced material instability. On-orbit experiments indicated that under laser illuminations, the net surface charge of metal Zr changed from positive to negative at 900 K and from negative to positive at 1300 K. The possible physical mechanism of the charge inversion of heated material is discussed.

Variable Turn-OFF Gate Voltage Drive for Voltage Balancing of High-Speed SiC MOSFETs in Series-Connection

Active gate drive with high accurate and self-adaptive closed-loop control is a promising approach to solving the imbalanced voltage problem of series-connected silicon carbide (SiC) <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> s. However, due to the inherent time propagation mismatching between Si- and SiC-based devices, behavior adjustment through Si-based ICs during the switching transient takes at least tens of nanoseconds, which limits the minimum allowable turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> time of each series unit. In this article, a novel active gate drive with a variable gate voltage regulator (GVR) is proposed. It uses a single P-channel <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> to determine the connection timing of a precharged capacitor in series with the input capacitance in which way the switching transient of each device in the stack is adjustable. It is more advantageous when applied to the low-power SiC <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> applications with relatively smaller external gate resistors. Two sampling and voltage balancing control circuits based on different processors are proposed for the GVR to adapt to different switching frequencies and costs. Its operational principle and design guidelines are specified and its performance on voltage balancing control is experimentally verified. Analytical assessment on switching losses and costs, compared with the previous work, is provided.

Fuzzy-interpolative control of temperatures for the intelligent rooftop greenhouse

This paper is focusing on intelligent rooftop greenhouses. An initial mathematical model implying recirculation factors for greenhouse and underneath building ventilation systems was upgraded in the sense of reducing interactions among parameters, by discarding the recirculation factors. The initial approach relied on basic fuzzy-interpolative temperature controllers working with a network of ventilation fans, adapted to the changes of the weather conditions and of the building configuration by means of a central expert adaptive rule base. This paper proposes a flexible distributed fans network, locally adapted, working under the control of temperature self-adaptive interpolative controllers. This approach enables us to adapt such buildings, that are now confined to warm temperatures, to a wide range of climates, to value a great part of renewable resources of our cities and to initiate a process of increasing the carbon offset at large scale. The new configuration is tested by simulations.

Multi-stage Ignition System With Self-adaptive Control for Lean Combustion in SI Natural Gas Engine

Multi-stage Ignition System With Self-adaptive Control for Lean Combustion in SI Natural Gas Engine

Self-adaptive opposition-based differential evolution with subpopulation strategy for numerical and engineering optimization problems

Opposition-based differential evolution (ODE) is a well-known DE variant that employs opposition-based learning (OBL) to accelerate the convergence speed. However, the existing OBL variants are population-based, which causes many shortcomings. The value of the jumping rate is not self-adaptively adjusted, so the algorithm easily traps into local optima. The population-based OBL wastes fitness evaluations when the algorithm converges to sub-optimal. In this paper, we proposed a novel OBL called subpopulation-based OBL (SPOBL) with a self-adaptive parameter control strategy. In SPOBL, the jumping rate acts on the individual, and the subpopulation is selected according to the individual’s jumping rate. In the self-adaptive parameter control strategy, the surviving individual’s jumping rate in each iteration will participate in the self-adaptive process. A generalized Lehmer mean is introduced to achieve an equilibrium between exploration and exploitation. We used DE and advanced DE variants combined with SPOBL to verify performance. The results of performance are evaluated on the CEC 2017 and CEC 2020 test suites. The SPOBL shows better performance compared to other OBL variants in terms of benchmark functions as well as real-world constrained optimization problems.