IEEE Transactions on Control Systems Technology最新文献

{"title":"New Fixed-Time Observer-Based Model-Free Fixed-Time Sliding Mode of Joint Angle Commanded NAO Humanoid Robot","authors":"Mahmoud Farhat;Yassine Kali;Maarouf Saad;Mohammad Habibur Rahman;Roberto E. Lopez-Herrejon","doi":"10.1109/TCST.2024.3469051","DOIUrl":"https://doi.org/10.1109/TCST.2024.3469051","url":null,"abstract":"In this article, we present a new fixed-time observer (FTO) with time delay estimation (TDE)-based model-free fixed-time sliding mode for the problem of robust walking of the NAO robot. The proposed technique ensures convergence in fixed time, regardless of initial conditions, thereby enhancing both convergence speed and robustness. This method allows for precise tracking of the joint angles’ positions without depending on the robot’s dynamic models while reducing the chattering via a modified exponential reaching law (MERL). To address the complexities of stabilizing the walking dynamics of the NAO robot, which include highly nonlinear dynamics and limited computational process, the proposed strategy utilizes the TDE technique for system model estimation. To mitigate estimation errors, a novel observer with guaranteed fixed-time stability is proposed. This last helps to enhance the tracking performance. Using the Lyapunov theory and experimental validation, within the proposed composite control method, the proposed nonsingular terminal sliding surface’s fixed-time stability along with the system state’s stability is verified. Significantly improved stability and accuracy in the robot’s joint movements are demonstrated through experimental results, validating the efficacy of the tracking trajectory for robotic systems such as the NAO humanoid robot.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 1","pages":"304-315"},"PeriodicalIF":4.9,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decision-Making for Autonomous Vehicles With Interaction-Aware Behavioral Prediction and Social-Attention Neural Network","authors":"Xiao Li;Kaiwen Liu;H. Eric Tseng;Anouck Girard;Ilya Kolmanovsky","doi":"10.1109/TCST.2024.3460650","DOIUrl":"https://doi.org/10.1109/TCST.2024.3460650","url":null,"abstract":"Autonomous vehicles need to accomplish their tasks while interacting with human drivers in traffic. It is thus crucial to equip autonomous vehicles with artificial reasoning to better comprehend the intentions of the surrounding traffic, thereby facilitating the accomplishments of the tasks. In this work, we propose a behavioral model that encodes drivers’ interacting intentions into latent social-psychological parameters. Leveraging a Bayesian filter, we develop a receding-horizon optimization-based controller for autonomous vehicle decision-making which accounts for the uncertainties in the interacting drivers’ intentions. For online deployment, we design a neural network architecture based on the attention mechanism which imitates the behavioral model with online estimated parameter priors. We also propose a decision tree search algorithm to solve the decision-making problem online. The proposed behavioral model is then evaluated in terms of its capabilities for real-world trajectory prediction. We further conduct extensive evaluations of the proposed decision-making module, in forced highway merging scenarios, using both simulated environments and real-world traffic datasets. The results demonstrate that our algorithms can complete the forced merging tasks in various traffic conditions while ensuring driving safety.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 4","pages":"1285-1300"},"PeriodicalIF":4.9,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Gear-Shift Optimization for an Autonomous Wheel Loader","authors":"Sencheng Yu;Xingyong Song;Zongxuan Sun","doi":"10.1109/TCST.2024.3460648","DOIUrl":"https://doi.org/10.1109/TCST.2024.3460648","url":null,"abstract":"Off-road vehicles, such as wheel loaders, consume a significant amount of fuel during the transportation of materials. The gear-shifting process is crucial in fuel savings for transportation, and therefore, optimization of gearshifts is important for vehicle control. For an autonomous off-road vehicle, there is potential for more fuel savings by proper coordination of gearshift optimization and optimization of other control inputs. This brief proposes a new method for integrating gear-shifting into transportation optimization for an autonomous wheel loader to minimize fuel consumption. Tests conducted on short loading cycles show that this method can save around 10%–20% of fuel on average compared with a conventional gearshift-scheduling method.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 1","pages":"384-391"},"PeriodicalIF":4.9,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Layer-to-Layer Melt Pool Control in Laser Powder Bed Fusion","authors":"Dominic Liao-McPherson;Efe C. Balta;Mohamadreza Afrasiabi;Alisa Rupenyan;Markus Bambach;John Lygeros","doi":"10.1109/TCST.2024.3464118","DOIUrl":"https://doi.org/10.1109/TCST.2024.3464118","url":null,"abstract":"Additive manufacturing (AM) processes are flexible and efficient technologies for producing complex geometries. However, ensuring reliability and repeatability is challenging due to the complex physics and various sources of uncertainty in the process. In this work, we investigate closed-loop control of the melt pool dimensions in a 2-D laser powder bed fusion (LPBF) process. We propose a trajectory optimization-based layer-to-layer (L2L) controller based on a linear parameter-varying (LPV) model that adjusts the laser power input to the next layer to track a desired melt pool depth and validate our controller by placing it in closed-loop high-fidelity multilayer smoothed particle hydrodynamics simulator of the 2-D LPBF process. Detailed numerical case studies demonstrate successful regulation of the melt pool depth on brick and overhang geometries and provide first of its kind results on the effectiveness of L2L input optimization for the LPBF process as well as detailed insight into the physics of the controlled process. Computational complexity and process performance results illustrate the method’s effectiveness and provide an outlook for its implementation onto real systems.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 1","pages":"207-218"},"PeriodicalIF":4.9,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistically Oriented Optimal Control and Disturbance Prediction for Piezoelectric Semi-Active Vibration Suppression","authors":"Mizuki Abe;Koyo Mishima;Yushin Hara;Keisuke Otsuka;Kanjuro Makihara","doi":"10.1109/TCST.2024.3463333","DOIUrl":"https://doi.org/10.1109/TCST.2024.3463333","url":null,"abstract":"This study proposes a novel predictive input optimization and disturbance prediction strategy for semi-active vibration control using a piezoelectric transducer, leveraging the statistically oriented prediction and optimization scheme and Gaussian process (GP) enhanced disturbance prediction. Here, semi-active switching, which manipulates the piezoelectric charge, is executed to suppress vibration. State-dependent discontinuity of the semi-active input trajectory owing to the switching makes it challenging to formulate a prediction-based optimization strategy in conventional methods. Incorporating the dynamical model of disturbance is also crucial to compensate for those undesired effects effectively. The proposed GP-enhanced disturbance predictor, which combines the empirical model and GP regression, provides the disturbance trajectory over a finite time horizon. The empirical model approximates the periodic disturbance with known frequencies, while the GP regression model handles other unknown components, enabling the predictor to function under different conditions. Furthermore, the predicted disturbance trajectory is applied for control optimization, allowing the controller to determine the optimal control input trajectory that compensates for future disturbances. The proposed strategy employs the tree-based scheme to formulate a prediction and optimization algorithm that determines the optimal semi-active input trajectory. Further, it includes a statistically oriented switching criterion that reduces the computational costs by selecting the trajectory to be predicted. This criterion utilizes kernel density estimation to learn the probability density function of suppression performance, allowing the controller to adaptively select trajectories to predict online while not excluding promising ones. The effectiveness of the proposed statistically oriented predictive switching vibration control with tree-based formulation and optimization (S-PSTFO) strategy was validated through experiments, demonstrating superior suppression performance over the conventional method.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 1","pages":"165-180"},"PeriodicalIF":4.9,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10703149","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safe Control Architecture via Model Predictive Control","authors":"Maryam Nezami;Ngoc Thinh Nguyen;Georg Männel;Robin Kensbock;Hossam Seddik Abbas;Georg Schildbach","doi":"10.1109/TCST.2024.3461173","DOIUrl":"https://doi.org/10.1109/TCST.2024.3461173","url":null,"abstract":"Ensuring the safe operation of autonomous systems is a critical challenge that demands the development of sophisticated control strategies. This article proposes a safe control architecture (SCA) that employs a supervisor model predictive control (MPC) (supervisor) strategy to ensure the persistent satisfaction of state and input constraints. The supervisor continuously monitors the safety of potentially unsafe inputs generated by an operating controller (OC). If an input is predicted to lead the system to a future state where constraint violations are inevitable, it is deemed unsafe and thus blocked from the system. Instead, a backup input, generated by the supervisor in the previous time step, is applied to the system. However, uncertainties in system dynamics are unavoidable and can lead to incorrect decisions by the supervisor, which is based on MPC with a nominal model. This article proposes to enhance the robustness of the SCA by the integration of tube MPC. The resulting robust SCA (RSCA) has the capability to ensure safe operation of autonomous systems under model uncertainties, making it a practical solution for safety-critical autonomous systems, such as vehicles, drones, or medical robots. This article also proves the recursive feasibility and stability of the RSCA. The effectiveness of the approach is validated for an autonomous vehicle in IPG CarMaker, a high-fidelity simulation environment with realistic data on roads, vehicle dynamics, and obstacles.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 4","pages":"1207-1220"},"PeriodicalIF":4.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temperature Control of Nonlinear Uncertain Systems via Model-Free Cascaded Controller: Application to an Infrared Emissometer","authors":"J. Gabirondo-López;I. Arredondo;J. M. Igartua","doi":"10.1109/TCST.2024.3464111","DOIUrl":"https://doi.org/10.1109/TCST.2024.3464111","url":null,"abstract":"This article introduces a cascade control system designed to precisely manage complex heating processes characterized by two actuator saturations, the absence of any cooling system, variable properties of the sample being heated, and a wide temperature span. The proposed control system combines an industrial PID for furnace heating element control with a model-free controller to control the temperature of the heated sample. We clearly describe the procedure to tune the controller and to accurately model the whole heating process. We have performed simulations to compare the performance of the control system we propose to the performance of conventional cascade controllers; our control system is superior in terms of accuracy and adaptability. We have successfully implemented the control system in an experimental instrument: an infrared emissometer. We have proved that the implementation is effective in controlling both tuned plants and unknown plants, without the need to modify the previous control configurations. The tests we have done also demonstrate that the achieved final model represents the whole experimental system with a high degree of accuracy, as the results from the simulations match with the experimental results.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 1","pages":"219-228"},"PeriodicalIF":4.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10701453","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart Traffic Manager for Speed Harmonization and Stop-and-Go Waves Mitigation Dedicated to Connected Autonomous Vehicles","authors":"Léa Prade Njoua Dongmo;Jean Auriol;Alessio Iovine","doi":"10.1109/TCST.2024.3456675","DOIUrl":"https://doi.org/10.1109/TCST.2024.3456675","url":null,"abstract":"The present article introduces a smart traffic manager, conceived as an algorithm tasked with supervising vehicles. It gathers and disseminates macroscopic traffic flow data among Connected Autonomous Vehicles (CAVs) to facilitate their attainment of String Stability (SS). This method combines machine-learning techniques and physics-based traffic flow models, establishing a crucial connection between microscopic and macroscopic modeling levels. It enables CAVs to utilize mesoscopic controllers that effectively mitigate stop-and-go waves while ensuring speed harmonization, thereby proving their Disturbance String Stability (DSS). Simulation results demonstrate the efficacy of this proposed solution.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 4","pages":"1447-1462"},"PeriodicalIF":4.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Sensitivity Analysis of Wave Energy Converter Model Predictive Control Systems With Wave Excitation Force Estimation and Prediction","authors":"Zechuan Lin;Xuanrui Huang;Xi Xiao;John V. Ringwood","doi":"10.1109/TCST.2024.3456689","DOIUrl":"https://doi.org/10.1109/TCST.2024.3456689","url":null,"abstract":"Understanding the sensitivity of energy-maximizing control for wave energy converters (WECs), to various model errors, is crucial for application. Many advanced WEC controllers, especially model predictive control (MPC)-like controllers, require estimation and prediction of wave excitation force (WEF). However, previous studies only focus on the controller in isolation, without considering the error coupling effects when a complete estimation-prediction loop is involved. In this study, it is revealed through numerical analysis that the complete MPC system has sensitivity behavior completely different from the isolated MPC; under certain model errors, the system can become particularly unpredictable, exhibiting potential instability and self-locking phenomena, which cannot be observed from the examination of control sensitivity alone. Meanwhile, different tuning options for the WEF estimator and predictor are examined, where the accuracy-robustness tradeoff is shown to be critical for performance amelioration under errors. Based on the analysis, this study challenges the widely assumed “separation principle” of WEF estimation/prediction and WEC control, highlights the importance of incorporating a complete estimation-prediction loop in sensitivity examination, and draws practical guidelines for WEC control application.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 1","pages":"136-147"},"PeriodicalIF":4.9,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing the Force Transparency of the Energy-Reflection-Based Time-Domain Passivity Approach","authors":"Michael Panzirsch;Harsimran Singh;Xiao Xu;Alexander Dietrich;Thomas Hulin;Eckehard Steinbach;Alin Albu-Schaeffer","doi":"10.1109/TCST.2024.3458831","DOIUrl":"https://doi.org/10.1109/TCST.2024.3458831","url":null,"abstract":"The time-domain passivity approach (TDPA) was developed and applied to tackle a variety of control challenges such as noncollocated force sensing, authority scaling, or delayed coupling in robotic applications. Specifically for delay, recently, the energy-reflection-based TDPA (TDPA-ER) was proposed to improve position tracking and force-feedback quality. In contrast to the conventional TDPA, the TDPA-ER intrinsically prevents position drift, thus substantially increasing the coupling rigidity. Here, we extend the TDPA-ER to further enhance the force transparency perceived by the operator in teleoperation scenarios. The extension is based on two independent control strategies that, among others, reorganize the energy distribution of TDPA-ER and ensure more continuous force profiles through the deflection-domain passivity approach (DDPA). Experiments confirm the improvement of force-feedback quality and force continuity with regard to TDPA-ER. Furthermore, it is shown that interactions with dynamic objects and active environments can be handled robustly with the proposed teleoperation control strategies.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 1","pages":"181-188"},"PeriodicalIF":4.9,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}