Automatic Control Scheme Research Articles

Dynamic Obstacle Avoidance Strategy for High-Speed Vehicles Via Constrained Model Predictive Control and Improved Artificial Potential Field

Abstract The paper proposes an automatic planning and control strategy to keep the automatic driving high-speed vehicles collision-free based on the improved artificial potential field (APF) and constrained model predictive control (MPC). Firstly, the road potential field satisfying the constraints of vehicle safe driving is constructed according to the driving characteristics of vehicles and road boundary conditions. Focusing on the influence of the speed and direction of the obstacle vehicle on the potential field, the dynamic obstacle vehicle potential field is established. Secondly, the road potential field and the dynamic obstacle vehicle potential field are incorporated into the objective function of the path planning module to establish a real-time path planner. Thirdly, the yaw stability constraints of the vehicle are established, which are added to the QP solver and updated in real time according to the current vehicle states, so as to establish the constrained MPC controller. In the end, the safety of this planning and control strategy for obstacle avoidance overtaking and the effectiveness of constrained MPC in improving vehicle stability are verified by comparative simulation analysis in multi-obstacle vehicles scenarios.

Automatic control strategy of electronic and electrical engineering based on FPGA

Field Programmable Gate Array (FPGA) have demonstrated significant potential in the field of automation control due to their flexibility, programmability, and high performance. This study designs an FPGA-based automation control strategy for motor control systems, encompassing four core modules: signal acquisition, signal processing, control algorithm, and output drive. The signal acquisition module employs the AD9280 high-speed, high-precision ADC chip to achieve rapid and accurate signal acquisition. The signal processing module utilizes digital filtering and FFT analysis within the FPGA to extract key information such as motor speed. The control algorithm module adopts an adaptive fuzzy control strategy that fine-tunes control rules in real-time to ensure precise control. The output drive module generates PWM signals via the FPGA to drive the motor, guaranteeing efficient and stable operation. Experimental results indicate that the FPGA-based control strategy significantly outperforms traditional methods in terms of response speed and stability, capable of meeting the high-performance requirements of modern industrial motor control systems. This research provides new technical insights and practical references for the field of automation control in electrical and electronic engineering.

Enhancing tapping mode atomic force microscopy using AutoResonance control and a hybrid dynamic model

We present a novel method for analyzing tapping mode atomic force microscopy (AFM), enhancing topography reconstruction’s accuracy and speed. Our approach integrates an automatic resonance-tracking control scheme with a hybrid dynamic model that considers interactions between the AFM tip and the sample, and the oscillating sensor dynamics. A key aspect of our method is a simplified model that captures intricate dynamic behavior as a function of the distance from the topography. This model enables the immediate approximation of the distance from the topography, eliminating the necessity to lock onto the nominal distance as done with classical AFMs. To validate the accuracy, we conducted numerical simulations of Van der Waals and capillary interaction forces, as well as experimental measurements of a coin’s topography. The results affirm the effectiveness of our approach in enhancing AFM imaging and characterization capabilities.

CAPP-GPT: A computer-aided process planning-generative pretrained transformer framework for smart manufacturing

Smart manufacturing (SM) constitutes the backbone of Industry 4.0 (I4.0), allowing for heightened autonomy of the various interacting cyber-physical systems, making the various entities on the production floor. Connectivity, a vital enabler, plays a crucial role through state-of-the-art Digital Twinning (DT) technologies driven by underlying innovations like the industrial Internet of Things, Cloud Computing, and advancements in sensory devices. DT, which plays a vital role in the various planning functions under the production and operations management umbrella, is being used in the developed combined CAPP-GPT (Computer-Aided Process Planning-Generative Pretrained Transformer) and production scheduling approach to address disruptions on the shopfloor and in self-healing of the manufacturing processes at a micro-CAPP level by optimally adapting the process parameters and the developed toolpath on the fly based on online process signature measurements. In a leap commensurate with that which has taken place in Natural Language Processing-Large Language Models (Chat-GPT), similar efforts are currently being undertaken to parse CAD data structures and blueprints, fusing operations research and predictive analytics algorithms to carry out setup planning as well as sequencing and grouping manufacturing sub-operations. A hybridized Optimization and Machine Learning (ML) approach is employed where Logical Analysis of Data is used to solve the problem heuristically, exploiting various generative and variant methods at heart. Another extension of this macro-CAPP problem is being tackled by integrating the problem with delayed product differentiation, lot-sizing, and transfer line balance for futuristic batch-production shops employing Hybrid Manufacturing (HM) and Smart Assembly. At the micro-CAPP level, HM process parameters are optimized using a comprehensive approach employing the Taguchi loss function to assess surface roughness, internal failure costs, and other criteria, including greenhouse gas emissions and expended energy. Online measurements of the process signatures are also employed to adapt the initial set of process parameters using different automatic control schemes. ML is used to identify the process parameters carrying simulations on Simulink before the system is deployed.

Model‐based reinforcement learning control of reaction‐diffusion problems

AbstractMathematical and computational tools have proven to be reliable in decision‐making processes. In recent times, in particular, machine learning‐based methods are becoming increasingly popular as advanced support tools. When dealing with control problems, reinforcement learning has been applied to decision‐making in several applications, most notably in games. The success of these methods in finding solutions to complex problems motivates the exploration of new areas where they can be employed to overcome current difficulties. In this article, we explore the use of automatic control strategies to initial boundary value problems in thermal and disease transport. Specifically, in this work, we adapt an existing reinforcement learning algorithm using a stochastic policy gradient method and we introduce two novel reward functions to drive the flow of the transported field. The new model‐based framework exploits the interactions between a reaction‐diffusion model and the modified agent. The results show that certain controls can be implemented successfully in these applications, although model simplifications had to be assumed.

Unmanned Floating Object Transportation Applying Multiple Dynamic Positioning Tugboats: A Novel Cooperative Control Algorithm

Abstract To promote the automation and intelligence of marine floating object transportation, this paper proposes a cooperative consensus control algorithm for multiple unmanned dynamic positioning (DP) tugboats to transport an unactuated floating structure object using cables. The major concept of present algorithm is to implement a leader-follower formation strategy among distributed tugboats, with the aim of effectively maneuvering the object by adjusting the relative position between the virtual leader and the object in a reasonable manner. A cooperative consensus controller is integrated into the feedback controller of each tugboat local DP system in order to facilitate cooperative coordination among tugboats in a formation. A customized DP system, which incorporates the radial basis function neural network supervisor control into the DP controller design and introduces nonlinear disturbance observer into the low-frequency motion state observer design, is proposed to address cable tension disturbances, and thus, the tracking performance of the DP tugboat is improved. Full-scale nonlinear time domain simulations are performed under variable environmental conditions, verifying the effectiveness of the proposed automatic towing control scheme.

A method for predicting needle insertion deflection in soft tissue based on cutting force identification

The deflection modeling during the insertion of bevel-tipped flexible needles into soft tissues is crucial for robot-assisted flexible needle insertion into specific target locations within the human body during percutaneous biopsy surgery. This paper proposes a mechanical model based on cutting force identification to predict the deflection of flexible needles in soft tissues. Unlike other models, this method does not require measuring Young’s modulus ( E ) and Poisson’s ratio ( ν ) of tissues, which require complex hardware to obtain. In the model, the needle puncture process is discretized into a series of uniform-depth puncture steps. The needle is simplified as a cantilever beam supported by a series of virtual springs, and the influence of tissue stiffness on needle deformation is represented by the spring stiffness coefficient of the virtual spring. By theoretical modeling and experimental parameter identification of cutting force, the spring stiffness coefficients are obtained, thereby modeling the deflection of the needle. To verify the accuracy of the proposed model, the predicted model results were compared with the deflection of the puncture experiment in polyvinyl alcohol (PVA) gel samples, and the average maximum error range predicted by the model was between 0.606 ± 0.167 mm and 1.005 ± 0.174 mm, which showed that the model can successfully predict the deflection of the needle. This work will contribute to the design of automatic control strategies for needles.

Dynamic Analysis of the De-Ballasting Operations of a Floating Dock with a Malfunctioning Pump

AbstractDuring normal de-ballasting operations for floating docks, each ballast pump independently manages a specific group of ballast tanks. However, when a pump malfunctions, a connection valve between the two groups of ballast water systems is opened. This allows the adjacent pump to serve as a helper pump, simultaneously controlling two groups of ballast water systems. This study explores a full-scale floating dock’s dynamic behaviours during the de-ballasting operations under this situation through a numerical model. In the developed numerical model, the dock is described as a six-degree-of-freedom rigid body which is subjected to hydrostatic, hydrodynamic, and mooring loads. A hydraulic model of the piping network of the malfunctioning pump and the helper pump is proposed. A modified P-controller regulates opening angles of all tank valves for minimal pitch and roll. Two configurations of the floating dock, i. e., a single floating dock and a floating dock with an onboard vessel, are considered. The numerical results show that the optimal helper pumps can be identified regarding the pumps’ total de-ballasting capacity and the dock’s stability. The most severe scenarios can be determined in term of the dock’s maximum draught differences caused by its roll and pitch. The observed maximum draught differences remain small relative to the dock’s width, indicating the effectiveness of employing helper pumps and the proposed automatic ballast control strategy for one-pump malfunction scenarios.

Dynamic and structural analyses of floating dock operations considering dock-vessel coupling loads

Floating docks are important for increasing the capacity of land-based shipyards and serve as essential platforms for vessel construction, maintenance, and repair. As the size of modern vessels increases, ensuring the structural safety of the floating docks becomes challenging. To enhance the operational safety of the floating docks, this study aims to develop an in-house code for a comprehensive global response assessment of a full-scale floating dock. The code is developed under a quasi-static assumption and enables dynamic and structural response assessments for the floating dock and docked vessel during the vessel-docking operations. The floating dock and docked vessel are represented as rigid bodies with six degrees of freedom and subjected to hydrostatic, hydrodynamic, mooring, and contact loads. The piping network of the floating dock's ballast water system is modelled to calculate the flow rates out of all ballast tanks. A modified proportional controller (P-controller) is employed to achieve automatic ballast control, regulating opening angles of ballast tank valves to minimize roll and pitch motions of the floating dock and docked vessel. For structural response assessment, a bending model is proposed to evaluate the bending deformation of the floating dock based on the load distribution along the dock's length. Results show that the present code can successfully simulate the vessel-docking process. The automatic ballast control strategy effectively minimizes roll and pitch for both the floating dock and docked vessel, ensuring stable control of the entire system. Minimal discrepancies are observed in the dynamic responses and bending of the floating dock between one- and two-way couplings. The longitudinal shift of the docked vessel's centre of gravity (CoG) increases the relative motions between the docked vessel and the floating dock, especially in surge, sway and pitch. A significant shift distance might cause the mooring rope breakage. Positioning the vessel's CoG closer to the dock's CoG proves safer for vessel-docking operations. In general, the developed code can support the decision-making for dock masters and optimize the docking plan.

Fuzzy-based thermal management control analysis of vehicle air conditioning system

Automotive air conditioning (ACC) system designers faced unique challenges in satisfying customer demands for efficient and comfortable operation across a broad temperature range. The AAC system's effective or fixed temperature setting makes this possible. Many climate control models, like the state flow switching controller in MATLAB control, must handle heating and cooling due to a single Simulink/environment software and frequently contain steady-state error (SSE). The present research proposes to design an automatic temperature control scheme and fuzzy logic control (FLC) for an automotive air-conditioned (AAC) system. In addition, it employs MATLAB Simulink/environment software to model the fuzzy control technique to analyze the AAC system's response. A simulation has been set up to evaluate the suggested system performance to match a selection of user-specified reference temperatures and compressor speeds. Compared with a PID-controlled AAC system, the proposed FLC-based system reduced the undershoot to only 2.30% from 33.30% respectively, and was robust, quicker, and better at controlling temperature.

A flexible and deep peak shaving scheme for combined heat and power plant under full operating conditions

Improving the flexible and deep peak shaving capacity of combined heat and power (CHP) plant under full operating conditions to facilitate renewable energy consumption is the main choice of novel power system. Accordingly, a dry/wet state automatic conversion control scheme fuses the precise mechanism structure, reinforcement learning and multi-objective model predictive control (MPC) algorithms is designed to promote the deep peak shaving ability of CHP plant in this paper. Firstly, the detailed mechanism models of once-through boiler at dry and wet state are presented by analyzing the dynamic characteristics of steam-water flow. Secondly, the large-scale unknown parameters in mechanism models are identified via the Takagi-Sugeno fuzzy modeling, reinforcement learning algorithms and actual dry/wet state conversion data. Thanks to the proposed hybrid modeling strategy, the high-precision boiler-steam unit models at dry and wet state are rapid obtained. Then, to meet different peak shaving demands, the multi-objective MPC algorithm is respectively constructed under dry and wet state with the comprehensive consideration of operational constraints, load tracking error, algorithm stability, power generation cost, CO2 and NOx emission costs. Aiming at maximizing the peak shaving efficiency and flexible operation ability of plant, a dry/wet automatic control scheme combined the designed multi-objective MPC algorithms and identified dry/wet state models is proposed. Finally, the load variation rate of 5 % and 2 % RCM/min is respectively achieved in the dry/wet state conversion tests on a 350 MW CHP plant based on the proposed control scheme. Thus, the rapid and thorough dry/wet state conversion performance of once-through boiler has successfully improved the operational flexibility of CHP plant under full operating conditions.

Design of MFAC greenhouse temperature controller based on LESO with disturbance estimation and fuzzy controller with parameter tuning

The proper temperature is essential for the growth and development of greenhouse crops. Therefore, it is necessary to determine an appropriate greenhouse temperature automatic control strategy. In this paper, a model-free adaptive greenhouse temperature control strategy is proposed by combining a linear extended state observer and a fuzzy controller. Specifically, the linear extended state observer is introduced to estimate model approximation error generated by pseudo gradient estimation. The fuzzy controller is used to correct the system parameter ρ online. The simulation results show that when there is no disturbance, compared with the typical model-free adaptive scheme, the MAE and RMSE of the greenhouse temperature control scheme proposed in this paper are reduced by 57.55% and 5.40% respectively. In the presence of custom disturbances, the proposed control scheme still has a faster response speed, stronger robustness, and higher control accuracy. And the proposed method can reduce the frequent action of the actuator, thus effectively reducing the energy cost.

Optimal Control Allocation-Based Automatic Transmission Upshift Control Strategy for Inertia Phase

Optimal Control Allocation-Based Automatic Transmission Upshift Control Strategy for Inertia Phase

Non-linear Model Predictive Control to Improve the Mineralogical Efficiency of Flotation Circuits

Flotation optimisation strategies often trade off grade for recovery, without attempting to improve the mineralogical efficiency of the flotation process as part of the automatic control and optimisation strategy. Non-linear flotation characteristics such as air recovery complicate the implementation of such an optimisation strategy due to rapidly changing dynamics, and consequently there is a need for a non-linear controller implementation with real-time parameter estimation, to perform the optimisation. This simulation study demonstrates how the parameters of an air recovery model can be estimated dynamically from measurements commonly available on an industrial flotation circuit, and how dynamic optimisation of the flotation process - in the form of non-linear model predictive control - shifts the grade-recovery curve upwards - implying improved metallurgical efficiency through air recovery maximisation.

Automatic Tracking Control Strategy of Autonomous Trains Considering Speed Restrictions: Using the Improved Offline Deep Reinforcement Learning Method

Automatic Tracking Control Strategy of Autonomous Trains Considering Speed Restrictions: Using the Improved Offline Deep Reinforcement Learning Method

Cooperative control for automatic towing operation by multiple DP tugboats with fully unknown model parameters

This paper presents a cooperative consensus control scheme for multiple unmanned dynamic positioning (DP) tugboats to transport an unactuated floating object through cables, aiming to promote the automation and intelligence of marine towing operation. The scheme implements a leader-follower formation strategy among distributed tugboats, maneuvering the object by adjusting the position of the virtual leader in a reasonable manner. To ensure the formation configuration, a cooperative consensus controller is integrated into the feedback controller of each tugboat local DP system. In addition, a customized model parameter-free DP system is proposed to enhance the tracking performance of a single tugboat, addressing the challenges of full unknown model parameters and large cable tension disturbances. The proposed automatic towing control scheme is verified through full-scale nonlinear time domain simulations under variable environmental conditions and formation configurations, demonstrating its effectiveness.

Design and Optimal Pose‐Constrained Visual Servoing of a Novel Active Flexible Endoscope Holder System for Solo Laparoscopic Surgery

Laparoscopic surgery (LS) has become an effective and widely accepted therapy for patients. However, performing LS requires advanced training and skills, as it demands high precision and dexterity due to visual feedback depending on the collaboration between the surgeon and endoscope assistant, restricted field of view, and limited dexterity inside the body. Thus, a flexible endoscope holder system with an automatic control scheme is proposed to assist LS performance, allowing the surgeon to perform solo LS. A DNA‐inspired helix‐based structure with wide‐angle and constant curvature bending is presented and made of printable nylon with selective laser sintering. The proposed flexible mechanism reduces the cost of manufacturing and assembling. Also, it has a hollow design for embedded sensing and actuation. Then, an optimization method is proposed, which provides a reference for the endoscope installation, robot placement, and pose selection. In addition, aiming at the misorientation problem, a pose‐constrained visual servoing control scheme for the endoscope system is developed. Both simulation and physical experiments are conducted to verify the effectiveness and feasibility of the proposed control scheme. Experimental results demonstrate that the proposed visual servoing scheme can maintain the optimized pose and reduce misorientation during automatic view steering.

Towards a novel intelligent and fully interactive IoT framework for residential buildings

Intelligent operation of buildings plays a crucial role in enhancing energy efficiency, especially in commercial buildings. However, residential buildings suffer from lack of management in energy consumption, contributing to global carbon emissions. Challenges include limited autonomous features, lack of scalability, and costly existing solutions. This paper presents a preliminary study toward the realization of a universal, scalable, and low-cost residential control framework to improve energy performance and reduce the cost of existing building retrofitting approaches. The residential system in its envisioned form may include thermostat, dimming, shading, and window-opening controls. For this study, a preliminary simulation-based investigation of a typical house in multiple climates is performed. To this end, the occupancy profiles data to layout different levels of automation of thermostats and shades have been employed, proposing 3 levels based on a variety of thermostat setpoints and shading. Data from the American Time Use Survey is utilized to inform occupancy and form the integrated automatic control strategy. This approach paves the way for building management systems in homes that integrate IoT-enabled devices for seamless operation, supporting decarbonization and creating interactive and smart buildings. The results showed that from 15% up to 70% reduction in heating and cooling loads are obtainable. This underscores the great potential of implementation of smart control systems in residential buildings.

Vibration Control of Marine Flexible Riser with Uncertain Model

When flexible risers in the ocean are subjected to various environmental loads in the ocean, vibration will inevitably occur. Due to the nonlinearity and uncertainty of the actual riser system, it is impossible to obtain an accurate mathematical model of the riser system. The existing control algorithms designed based on the precise model of the riser system cannot meet the actual control requirements. A flexible riser boundary control method based on fuzzy control algorithm is proposed to suppress the vibration of marine flexible riser systems with uncertain models. Firstly, the dynamic models of existing marine flexible risers were studied. In response to the uncertainty of the riser system model, the control experience was transformed into an automatic control strategy in the form of fuzzy language control rules to improve the adaptability of the controller. Then, combining boundary control technology with fuzzy control, a boundary controller is constructed to stabilize the riser in a small neighborhood of its original position and reduce fatigue damage to the riser. Finally, the effectiveness of the designed fuzzy boundary control algorithm was verified through MATLAB simulation results, and the vibration suppression effect of the algorithm on the vertical tube was better than that of traditional PD control.

Automated Raman feed-back control of multiple supplemental feeds to enable an intensified high inoculation density fed-batch platform process.

Biologics manufacturing is increasingly moving toward intensified processes that require novel control strategies in order to achieve higher titers in shorter periods of time compared to traditional fed-batch cultures. In order to implement these strategies for intensified processes, continuous process monitoring is often required. To this end, inline Raman spectroscopy was used to develop partial least squares models to monitor changes in residual concentrations of glucose, phenylalanine and methionine during the culture of five different glutamine synthetase piggyBac® Chinese hamster ovary clones cultured using an intensified high inoculation density fed-batch platform process. Continuous monitoring of residual metabolite concentrations facilitated automated feed-rate adjustment of three supplemental feeds to maintain glucose, phenylalanine, and methionine at desired setpoints, while maintaining other nutrient concentrations at acceptable levels across all clones cultured on the high inoculation density platform process. Furthermore, all clones cultured on this process achieved high viable cell concentrations over the course of culture, indicating no detrimental impacts from the proposed feeding strategy. Finally, the automated control strategy sustained cultures inoculated at high cell densities to achieve product concentrations between 5 and 8.3g/L over the course of 12days of culture.