Control Scheme Research Articles

Integration Traffic Signal Control From Synchro to SUMO

This study investigates the feasibility and challenges of transferring traffic signal control schemes from the macroscopic signal timing optimization tool Synchro to the microscopic traffic simulator SUMO, focusing on Downtown Seattle as a case study. The research assesses the process of sharing and importing traffic signal timing plans, a crucial aspect of transportation simulations, between these two platforms. We conduct a detailed analysis of the traffic signal characteristics and data formats unique to each simulator and identify elements suitable for conversion. Subsequently, a four-stage framework is developed for semi-automatic integration of traffic signal control between the two. Our results indicate a successful conversion rate of approximately 85% of signalized intersections from Synchro to SUMO. This research not only illustrates the challenges and solutions in converting signal control across different platforms but also paves the way for future studies aimed at improving the interoperability of various traffic simulation tools.

A fixed-time nonsingular fast terminal sliding mode control scheme with time-varying sliding mode surface

This article proposes a fixed-time nonsingular fast terminal sliding mode control scheme with time-varying sliding mode surface for a class of second-order uncertain system. The control scheme makes the tracking error converge to the origin in fixed time without singularity problem and guarantees fast convergence rate given any initial states. In addition, the proposed control scheme ensures system states to converge to the origin instead of a neighbourhood of the origin which is so called practical fixed-time convergence. As a result, high precision of the trajectory tracking is guaranteed by the control scheme. A comparative simulation with a two-link manipulator is given and the results show that the proposed control scheme can achieve a good performance.

Observer‐based optimal fault‐tolerant tracking control for input‐constrained interconnected nonlinear systems with mismatched disturbances

SummaryThis paper investigates an observer‐based optimal fault‐tolerant tracking control problem for interconnected nonlinear systems with input constraints and mismatched disturbances via adaptive dynamic programming (ADP). Firstly, an augmented system consisting of tracking error and the reference trajectory is constructed, and then the original optimal tracking control problem is transformed into the optimal regulation control problem of the augmented system. An integral sliding‐mode‐based optimal fault‐tolerant control scheme is developed to eliminate the effect of actuator faults and guarantee the optimal control performance. Furthermore, a neural network‐based observer is designed to identify the completely unknown system dynamics based on the input‐output data, thereby relaxing the restriction on system dynamics. Subsequently, the modified Hamilton‐Jacobi‐Bellman equations are solved by using the ADP algorithm under a critic network framework. According to the Lyapunov approach, all signals in the closed‐loop augmented system are uniformly ultimately bounded. Finally, the effectiveness of the developed control scheme is demonstrated via simulation results.

Robust Control Design of Under-Actuated Nonlinear Systems: Quadcopter Unmanned Aerial Vehicles with Integral Backstepping Integral Terminal Fractional-Order Sliding Mode

In this paper, a novel robust finite-time control scheme is specifically designed for a class of under-actuated nonlinear systems. The proposed scheme integrates a reaching phase-free integral backstepping method with an integral terminal fractional-order sliding mode to ensure finite-time stability at the desired equilibria. The core of the algorithm is built around proportional-integral-based nonlinear virtual control laws that are systematically designed in a backstepping manner. A fractional-order integral terminal sliding mode is introduced in the final step of the design, enhancing the robustness of the overall system. The robust nonlinear control algorithm developed in this study guarantees zero steady-state errors at each step while also providing robustness against matched uncertain disturbances. The stability of the control scheme at each step is rigorously proven using the Lyapunov candidate function to ensure theoretical soundness. To demonstrate the practicality and benefits of the proposed control strategy, simulation results are provided for two systems: a cart–pendulum system and quadcopter UAV. These simulations illustrate the effectiveness of the proposed control scheme in real-world scenarios. Additionally, the results are compared with those from the standard literature to highlight the superior performance and appealing nature of the proposed approach for underactuated nonlinear systems. This comparison underscores the advantages of the proposed method in terms of achieving robust and stable control in complex systems.

Online learning fuzzy echo state network with applications on redundant manipulators.

Redundant manipulators are universally employed to save manpower and improve work efficiency in numerous areas. Nevertheless, the redundancy makes the inverse kinematics of manipulators hard to address, thus increasing the difficulty in instructing manipulators to perform a given task. To deal with this problem, an online learning fuzzy echo state network (OLFESN) is proposed in the first place, which is based upon an online learning echo state network and the Takagi-Sugeno-Kang fuzzy inference system (FIS). Then, an OLFESN-based control scheme is devised to implement the efficient control of redundant manipulators. Furthermore, simulations and experiments on redundant manipulators, covering UR5 and Franka Emika Panda manipulators, are carried out to verify the effectiveness of the proposed control scheme.

Predefined-Time Adaptive Neural Tracking Control for a Single Link Manipulator with an Event-Triggered Mechanism.

This paper introduces an adaptive trajectory-tracking control method for uncertain nonlinear systems, leveraging a time-varying threshold event-triggered mechanism to achieve predefined-time tracking. Compared to conventional time-triggering approaches, the employment of a time-varying threshold event-triggered mechanism significantly curtails communication resource wastage without compromising the system's performance. Furthermore, a novel adaptive control algorithm with predefined timing is introduced. This method guarantees that tracking errors converge to within a small vicinity of the origin within a predefined timeframe, ensuring all signals in the closed-loop system remain bounded. Moreover, by adjusting a controller-related parameter, we can predefine the upper bound of the convergence time. Finally, the efficacy of the control scheme is corroborated by simulation results obtained from a nonlinear manipulator system.

Distributed safe formation tracking control of multiquadcopter systems using barrier Lyapunov function.

Coordinating the movements of a robotic fleet using consensus-based techniques is an important problem in achieving the desired goal of a specific task. Although most available techniques developed for consensus-based control ignore the collision of robots in the transient phase, they are either computationally expensive or cannot be applied in environments with dynamic obstacles. Therefore, we propose a new distributed collision-free formation tracking control scheme for multiquadcopter systems by exploiting the properties of the barrier Lyapunov function (BLF). Accordingly, the problem is formulated in a backstepping setting, and a distributed control law that guarantees collision-free formation tracking of the quads is derived. In other words, the problems of both tracking and interagent collision avoidance with a predefined accuracy are formulated using the proposed BLF for position subsystems, and the controllers are designed through augmentation of a quadratic Lyapunov function. Owing to the underactuated nature of the quadcopter system, virtual control inputs are considered for the translational (x and y axes) subsystems that are then used to generate the desired values for the roll and pitch angles for the attitude control subsystem. This provides a hierarchical controller structure for each quadcopter. The attitude controller is designed for each quadcopter locally by taking into account a predetermined error limit by another BLF. Finally, simulation results from the MATLAB-Simulink environment are provided to show the accuracy of the proposed method. A numerical comparison with an optimization-based technique is also provided to prove the superiority of the proposed method in terms of the computational cost, steady-state error, and response time.

Disturbance Observer-Based Adaptive Fault Tolerant Control with Prescribed Performance of a Continuum Robot

This paper studies an adaptive fault tolerant control (AFTC) scheme for a continuum robot subjected to unknown actuator faults, dynamics uncertainties, unknown disturbances, and prescribed performance. Specifically, to deal with uncertainties, a function approximation technique (FAT) is employed to evaluate the unknown actuator faults and uncertain dynamics of the continuum robot. Then, a nonlinear disturbance observer (DO) is developed to estimate the unknown compounded disturbance, which contains the unknown disturbances and approximation errors of the FAT. Furthermore, the prescribed error bound is treated as a time-varying constraint, and the controller design method is based on an asymmetric barrier Lyapunov function (BLF), which is operated to strictly ensure the steady-state and transient performance of the continuum robot. Afterwards, the simulation results validate the effectiveness of the proposed AFTC in dealing with the unknown actuator faults, uncertainties, unknown disturbances, and prescribed performance. Finally, the effectiveness of the proposed AFTC scheme is verified through experiments.

Red gold: Copper and the U.S. mobilization campaign, 1950–3

AbstractThis paper examines U.S. planners’ persistent efforts to secure an adequate supply of a vital metal, copper, during the Korean War, and the numerous obstacles that plagued them. Their initial efforts to mobilize without imposing a disruptive, draconian control scheme quickly proved inadequate, all but forcing planners to adopt a much more regimented Controlled Materials Plan (CMP). The inability of U.S. planners to secure a strong supranational control regime through the International Materials Conference undercut efforts to coordinate international mineral allocation, exacerbated U.S. shortages, and spawned ill‐conceived compromises that eroded the effectiveness of the CMP. Finally, Truman's domestic opponents, never entirely reconciled to a rigid control regime, used the copper shortage to further weaken mobilization planning. In the end, the preparedness campaign, despite some remarkable successes, failed to achieve many of its goals due, in part, to a global copper shortage that Senator Burnet Maybank's Joint Committee on Defense Production called ‘perhaps the most serious material limiting factor on fulfillment of defense goals’.

Model Predictive Control with Variational Autoencoders for Signal Temporal Logic Specifications.

This paper presents a control strategy synthesis method for dynamical systems with differential constraints, emphasizing the prioritization of specific rules. Special attention is given to scenarios where not all rules can be simultaneously satisfied to complete a given task, necessitating decisions on the extent to which each rule is satisfied, including which rules must be upheld or disregarded. We propose a learning-based Model Predictive Control (MPC) method designed to address these challenges. Our approach integrates a learning method with a traditional control scheme, enabling the controller to emulate human expert behavior. Rules are represented as Signal Temporal Logic (STL) formulas. A robustness margin, quantifying the degree of rule satisfaction, is learned from expert demonstrations using a Conditional Variational Autoencoder (CVAE). This learned margin is then applied in the MPC process to guide the prioritization or exclusion of rules. In a track driving simulation, our method demonstrates the ability to generate behavior resembling that of human experts and effectively manage rule-based dilemmas.

ANN Enhanced Hybrid Force/Position Controller of Robot Manipulators for Fiber Placement

In practice, most industrial robot manipulators use PID (Proportional + Integral + Derivative) controllers, thanks to their simplicity and adequate performance under certain conditions. Normally, this type of controller has a good performance in tasks where the robot moves freely, performing movements without contact with its environment. However, complications arise in applications such as the AFP (Automated Fiber Placement) process, where a high degree of precision and repeatability is required in the control of parameters such as position and compression force for the production of composite parts. The control of these parameters is a major challenge in terms of quality and productivity of the final product, mainly due to the complex geometry of the part and the type of tooling with which the AFP system is equipped. In the last decades, several control system approaches have been proposed in the literature, such as classical, adaptive or sliding mode control theory based methodologies. Nevertheless, such strategies present difficulties to change their dynamics since their design consider only some set of disturbances. This article presents a novel intelligent type control algorithm based on back-propagation neural networks (BP-NNs) combined with classical PID/PI control schemes for force/position control in manipulator robots. The PID/PI controllers are responsible for the main control action, while the BP-NNs contributes with its ability to estimate and compensate online the dynamic variations of the AFP process. It is proven that the proposed control achieves both, stability in the Lyapunov sense for the desired interaction force between the end-effector and the environment, and position trajectory tracking for the robot tip in Cartesian space. The performance and efficiency of the proposed control is evaluated by numerical simulations in MATLAB-Simulink environment, obtaining as results that the errors for the desired force and the tracking of complex trajectories are reduced to a range below 5% in root mean square error (RMSE).

Power Trading and Access Control Scheme Based on IPFS and Blockchain

In recent years, a large number of renewable energy power plants have been built all over the country, which has led to a sharp increase in the pressure on the current centralized electricity trading platform. There are data storage bottlenecks and data privacy problems in the current blockchain-based power transaction and access control system. In this paper, the interstellar file system is proposed to store the data of the new distributed power trading platform in a distributed way, so as to improve the storage capacity of the physical nodes; An improved ciphertext strategy attribute encryption scheme, which is lightweight, traceable and supports outsourced decryption combined with state secret algorithm, is adopted to integrate the user’s unique identity into the user’s private key and part of the expensive decryption calculation is outsourced to the cloud server to realize fine-grained data access control in the electricity market. The experimental results show that compared to the basic CP-ABE scheme, when the encryption attribute is 100, the decryption time of the proposed scheme is reduced from 556[Formula: see text]ms to 1.1[Formula: see text]ms; Compared to blockchain-based solutions alone, when storing data of 15[Formula: see text]MB, GAS consumption decreases from 2968738133 to 89360. The scheme can greatly improve the storage capacity of the system, improve the operational efficiency and meet the actual performance requirements.

Efficient voltage regulation: An RW-ARO optimized cascaded controller approach

This work introduces novel advancements in automatic voltage regulator (AVR) control, addressing key challenges and delivering innovative contributions. The primary motivation lies in enhancing AVR performance to ensure stable and reliable voltage output. A crucial innovation in this work is the introduction of the random walk aided artificial rabbits optimizer (RW-ARO). This novel optimization strategy incorporates a random walk approach, enhancing the efficiency of AVR control schemes. The proposed cascaded RPIDD2-PI controller, fine-tuned using the RW-ARO, stands out as a pioneering approach in the AVR domain. It demonstrates superior stability, faster response times, enhanced robustness, and improved efficiency compared to existing methods. Comparative analyses with established controller approaches reaffirm the exceptional performance of the proposed method. The new approach results in shorter rise times, quicker settling times, and minimal overshoot, highlighting its effectiveness and speed in achieving desired system responses. Moreover, the novel approach attains higher phase and gain margins, showcasing its superior performance in the frequency domain. The disturbance rejection and harmonic analysis are performed in order to demonstrate the efficacy of the proposed approach for potential real-world applications. The latter analyses further cement the superior capability of the proposed approach for the automatic voltage regulation.

A novel nonsingular fixed-time sliding mode control for morphing aircraft with actuator faults and saturation

This paper proposes a fixed-time anti-saturation adaptive sliding mode control (FxASASMC) methodology to solve the challenge of attitude tracking control in morphing aircraft under actuator faults, saturation and external disturbances. The proposed control algorithm consists of three key components: a novel nonsingular fixed-time sliding mode surface, a fixed-time anti-saturation compensator, and a continuous fixed-time adaptive control law. The novel nonsingular fixed-time sliding mode surface is developed based on the arctan function, enabling not only the confinement and independence of the sliding modes' convergence time from the initial states but also the benefit of a smaller initial value, thereby assisting in saturation avoidance. To mitigate the saturation issue, a fixed-time anti-saturation compensator is employed. Moreover, throughout the control process, the anti-saturation compensator ensures that the output is bounded. And once the actuator saturation stops, its output converges to zero in a fixed time, which effectively minimizes the impact of the anti-saturation compensator on the control system performance in the absence of system saturation. Based on the sliding mode surface and anti-saturation compensator, the continuous fixed-time adaptive control law ensures that the attitude tracking errors converge toward the neighborhood of the origin in a fixed time, even if the boundaries of the complex disturbance are not known in advance. Moreover, the convergence time boundary remains unaffected by the initial errors. In parallel, the control command remains continuous and free of chattering. Ultimately, the proposed control scheme's effectiveness and enhanced performance are demonstrated through various numerical simulations.

Optical Control of Adaptive Nanoscale Domain Networks.

Adaptive networks can sense and adjust to dynamic environments to optimize their performance. Understanding their nanoscale responses to external stimuli is essential for applications in nanodevices and neuromorphic computing. However, it is challenging to image such responses on the nanoscale with crystallographic sensitivity. Here, the evolution of nanodomain networks in (PbTiO3)n/(SrTiO3)n superlattices (SLs) is directly visualized in real space as the system adapts to ultrafast repetitive optical excitations that emulate controlled neural inputs. The adaptive response allows the system to explore a wealth of metastable states that are previously inaccessible. Their reconfiguration and competition are quantitatively measured by scanning x-ray nanodiffraction as a function of the number of applied pulses, in which crystallographic characteristics are quantitatively assessed by assorted diffraction patterns using unsupervised machine-learning methods. The corresponding domain boundaries and their connectivity are drastically altered by light, holding promise for light-programable nanocircuits in analogy to neuroplasticity. Phase-field simulations elucidate that the reconfiguration of the domain networks is a result of the interplay between photocarriers and transient lattice temperature. The demonstrated optical control scheme and the uncovered nanoscopic insights open opportunities for the remote control of adaptive nanoscale domain networks.

Optical Control of Adaptive Nanoscale Domain Networks.

Adaptive networks can sense and adjust to dynamic environments to optimize their performance. Understanding their nanoscale responses to external stimuli is essential for applications in nanodevices and neuromorphic computing. However, it is challenging to image such responses on the nanoscale with crystallographic sensitivity. Here, the evolution of nanodomain networks in (PbTiO3)n/(SrTiO3)n superlattices (SLs) is directly visualized in real space as the system adapts to ultrafast repetitive optical excitations that emulate controlled neural inputs. The adaptive response allows the system to explore a wealth of metastable states that are previously inaccessible. Their reconfiguration and competition are quantitatively measured by scanning x-ray nanodiffraction as a function of the number of applied pulses, in which crystallographic characteristics are quantitatively assessed by assorted diffraction patterns using unsupervised machine-learning methods. The corresponding domain boundaries and their connectivity are drastically altered by light, holding promise for light-programable nanocircuits in analogy to neuroplasticity. Phase-field simulations elucidate that the reconfiguration of the domain networks is a result of the interplay between photocarriers and transient lattice temperature. The demonstrated optical control scheme and the uncovered nanoscopic insights open opportunities for the remote control of adaptive nanoscale domain networks.

Response analysis of NMRG system considering Rb-Xe coupling

Abstract The dynamic range of the nuclear magnetic resonance gyroscope can be effectively improved through the closed-loop control scheme, which is crucial to its application in inertial measurement. This paper presents the analytical transfer function of Xe closed-loop system in the nuclear magnetic resonance gyroscope considering Rb-Xe coupling effect. It not only considers the dynamic characteristics of the system more comprehensively, but also adds the influence of the practical filters in the gyro signal processing system, which can obtain the accurate response characteristics of signal frequency and amplitude at the same time. The numerical results are compared with a experimentally verified simulation program, which indicate great agreement. The research results of this paper are of great significance to the practical application and development of the nuclear magnetic resonance gyroscope.

Hierarchical Coordinated Predictive Control of Multiagent Systems for Process Industries

Focusing on the requirements for efficient and accurate control in large-scale process industries with integrated “distributed-decentralized” characteristics, a novel hierarchical coordinated predictive control strategy for process industries is proposed with a multiagent system as the computational paradigm. This approach comprehensively considers the overall state of the system, the interactions of control actions among agents, the constraints of processes and energy consumption to solve the problems of poor flexibility of agent decision-making in the narrow consensus strategy and strong interaction of parts of the system. The proposed hierarchical control strategy requires each agent to perform three tasks at each time step. First, each agent iteratively obtains a consistent basis for closed-loop prediction in a distributed way. Then, each agent independently proposes a control scheme and determines its own priority by playing games based on the economic performance of the scheme. Next, each agent calculates its own optimal dynamic predictive control sequence in order of priority based on the system’s dynamic process model. Finally, by considering the temperature-control process of heating an alumina ceramic block in a high-power microwave reactor with six microwave sources, the effectiveness of the proposed hierarchical coordinated predictive control strategy is verified under different communication topologies by comparing it with the centralized model predictive control strategy.

Dynamic event-based cooperative control of virtually coupled high-speed trains: an ADP-based optimal control scheme

This paper is concerned with the cooperative control issue of virtually coupled high-speed trains (VCHSTs) under an adaptive dynamic programming (ADP) framework, where a dynamic event-triggered mechanism is employed to govern the communication between sensors and observers. A learning-based observer, equipped with a robust term, is exploited to mitigate the effect of the unknown nonlinear dynamics of the VCHSTs. Furthermore, to realise the distributed design, an auxiliary control signal is employed to simply the closed-loop system and also integrated into the cost function to handle the coupled nature of the investigated systems. The Hamilton–Jacobi–Bellman (HJB) equation is approximated by actor and critic networks in the ADP framework, which iteratively acquires the desired control strategy. By resorting to the common Lyapunov stability, some sufficient conditions are derived to achieve the desired observer gain and learning rates of NNs. Ultimately, the control scheme's efficiency is confirmed by simulations of VCHSTs.

Dynamic event-based cooperative control of virtually coupled high-speed trains: an ADP-based optimal control scheme

This paper is concerned with the cooperative control issue of virtually coupled high-speed trains (VCHSTs) under an adaptive dynamic programming (ADP) framework, where a dynamic event-triggered mechanism is employed to govern the communication between sensors and observers. A learning-based observer, equipped with a robust term, is exploited to mitigate the effect of the unknown nonlinear dynamics of the VCHSTs. Furthermore, to realise the distributed design, an auxiliary control signal is employed to simply the closed-loop system and also integrated into the cost function to handle the coupled nature of the investigated systems. The Hamilton–Jacobi–Bellman (HJB) equation is approximated by actor and critic networks in the ADP framework, which iteratively acquires the desired control strategy. By resorting to the common Lyapunov stability, some sufficient conditions are derived to achieve the desired observer gain and learning rates of NNs. Ultimately, the control scheme's efficiency is confirmed by simulations of VCHSTs.