Intelligent Cooperative Control Research Articles

Intelligent cooperative control for flexible landing on small celestial bodies

In view of the potential landing risks in small celestial body landing missions, the concept of flexible landing has been proposed in recent years. By employing a flexible lander with structural compliance to complex surface topography, the risks of rebound or overturning upon touchdown can be reduced. During flexible landing, the overall motion of the flexible lander is controlled by several nodes embedded in the flexible structure. Due to the flexible deformation, the motion among the nodes is intricately coupled, posing challenges to the control algorithm design. To address this problem, an intelligent cooperative control method for flexible landing is proposed in this paper. By decomposing the flexible internal force into a low-order principal term with specific physical interpretation and an unmodeled high-order residual term, the coupling relationship among the nodes is characterized. On this basis, the low-order dynamics system is reconstructed into a piecewise affine system, upon which a multi-node cooperative optimal controller is designed. A long-short-term memory recurrent neural network is further established to compensate for the unmodeled high-order term. Through the combination of the low-order principal control and the high-order intelligent compensation, a feasible approach for achieving flexible landing is obtained. Finally, the effectiveness of the proposed control method is verified via 433 Eros-based flexible landing simulations.

Event-driven intelligent cooperative control of unmanned autonomous systems in complex environment

This paper addresses the event-driven cooperative tracking control problem of unmanned autonomous systems with unknown dead zones, unknown disturbances, and output constraints, which are induced by complex environment. The nonlinear functions of system model are estimated by using the neural networks. Furthermore, a state transformation technique is presented to solve the problem of the constrained control. Unlike most existing methods that only consider unilateral factor, the intelligent cooperative control method designed in this paper can deal with multiple factors in complex environment at the same time, and make the unmanned autonomous systems save more energy and improve endurance through event-driven mechanism. Detailed analysis is given based on Lyapunov stability theory which guarantees that the consensus tracking errors are uniformly ultimately bounded. Finally, results of a numerical example and a practical example of marine surface vehicles are shown to verify the effectiveness of the proposed intelligent control protocol.

Adaptive-Critic-Based Event-Triggered Intelligent Cooperative Control for a Class of Second-Order Constrained Multiagent Systems

An event-based intelligent cooperative control policy is developed in this paper for a class of second-order multiagent systems (MASs). The followers are subject to external disturbances and output constraints, in which the constraint ranges are asymmetric and time-varying. By using one-to-one nonlinear mapping technique to obtain equivalent unconstrained systems and designing a distributed observer to estimate the leader's states, a simplified unconstrained tracking control task is established for each follower. After that, event-driven distributed optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> controllers are designed to mitigate the effects of external disturbances, which are obtained from a set of decoupled Hamilton-Jacobi-Isaacs (HJI) equations, where the proposed event-triggered strategy is dynamic. Aiming at solving the HJI equations more efficiently, an event-based adaptive dynamic programming (ADP) learning algorithm applying only critic neural networks (NNs) is developed. And the critic NN weights are convergent by using stored data under finite excitation condition. Finally, the validity of the developed control scheme is demonstrated by multiple single-link robot arm systems.

A novel brain-inspired approach based on spiking neural network for cooperative control and protection of multiple trains

The ongoing challenge of addressing critical issues related to intelligent cooperative control and active protection persists due to the absence of a comprehensive and efficient integrated solution. To address this challenge, this paper introduces a brain-inspired controller that emulates the collaborative functionalities of various brain regions, harnessing the power of spiking neural networks. The controller’s primary tasks include reference velocity tracking, cooperative control, and active protection, with a special focus on cooperative protection within distinct operational modes. Furthermore, the fundamental principles of incorporating spiking neural networks into train control, such as coding and decoding mechanisms, are expounded. The overarching controller is partitioned into two principal functional segments. The first segment involves emulating the prefrontal cortex (PFC) for reference velocity tracking and active protection against overspeed and collisions through motor control and movement planning. The second segment employs a cerebellum-inspired network for cooperative control. Additionally, the brain-inspired network introduced in this study undergoes training utilizing biologically-inspired mechanisms, incorporating dopamine and pertinent teaching signals to facilitate realistic synaptic modifications. Simulation results in several scenarios validate the proposed approach.

Special Issue on “Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms”

Swarm intelligence technology is a high and new technology combining unmanned system technology, network information technology and artificial intelligence technology, which has become a research hotspot [...]

Design and operation of smart hybrid microgrid

Abstract Renewable energy resources based hybrid microgrid has emerged as a significant component of the power system. Nevertheless, the control and management of power, voltage and frequency under transition between grid-connected mode (GCM) and islanded mode (IM) is a major concern. This paper proposes a self-sustained and flexible novel cooperative control strategy (CCS) for smart hybrid microgrid (SHMG) under GCM and IM mode operations. In islanded operation of SHMG, conventional droop offers ease of control but its execution with seamless transition between operating modes poses poor voltage regulation and frequency deviation. For this purpose, an intelligent cooperative controller is proposed. It consists of a modified P/f-Q/V droop control (MDC) algorithm along with soft computing based VF-controller unit (VFCU). The proposed control scheme is implemented to regulate voltage and frequency. It also enhances the power quality with unbalanced non-linear load and source intermittencies. The absence of a communication link reduces cost and also eliminates the risk of single point breakdown. The SHMG has been simulated in MATLAB and implemented in real-time. The obtained experimental results demonstrate the competency and high robustness of the proposed scheme.

Intelligent cooperative control of hydraulic support inspired by driveless car

Intelligent cooperative control of hydraulic support inspired by driveless car

Intelligent Cooperative Control for Urban Tracking

We introduce an intelligent cooperative control system for ground target tracking in a cluttered urban environment with a team of autonomous Unmanned Air Vehicles (UAVs). We extend the work of Yu e...

Intelligent Cooperative Control Architecture: A Framework for Performance Improvement Using Safe Learning

Planning for multi-agent systems such as task assignment for teams of limited-fuel unmanned aerial vehicles (UAVs) is challenging due to uncertainties in the assumed models and the very large size of the planning space. Researchers have developed fast cooperative planners based on simple models (e.g., linear and deterministic dynamics), yet inaccuracies in assumed models will impact the resulting performance. Learning techniques are capable of adapting the model and providing better policies asymptotically compared to cooperative planners, yet they often violate the safety conditions of the system due to their exploratory nature. Moreover they frequently require an impractically large number of interactions to perform well. This paper introduces the intelligent Cooperative Control Architecture (iCCA) as a framework for combining cooperative planners and reinforcement learning techniques. iCCA improves the policy of the cooperative planner, while reduces the risk and sample complexity of the learner. Empirical results in gridworld and task assignment for fuel-limited UAV domains with problem sizes up to 9 billion state-action pairs verify the advantage of iCCA over pure learning and planning strategies.

An Intelligent Cooperative Decoupling Controller for Coagulation Bath in Polyacrylonitrile Carbon Fiber Production

The production of polyacrylonitrile carbon fiber (PANCF) consists of a set of industrial processes with high complexity. A key process is the coagulation of the PAN as-spun fiber in the coagulation bath which has a lot of variables coupling with each other. In this paper, an intelligent cooperative decoupling controller (ICDC) based on the neuroendocrine regulation principle of human body is proposed and applied to the coagulation bath in the PANCF production. Three important variables including the liquid-level, the temperature, and the concentration are considered. The ICDC consists of a control center unit, several control and decoupling units and their corresponding output units. The control center coordinates each control and decoupling unit and determines their control schemes. Each control and decoupling unit takes effect independently, and the control information is exchanged among them for decoupling. The control signals are then integrated at the output units and finally sent to the plant. Simulation results demonstrate that the ICDC can rapidly response to the variation of control variables, completely eliminate the coupling influence and make smooth regulation without overshoot, which has a better performance than the conventional control schemes.

Physiological hemostasis based intelligent integrated cooperative controller for precise fault-tolerant control of redundant parallel manipulator

This paper focuses on precise fault-tolerant control for actual redundant parallel manipulator. Based on kinematic redundancy, some unnoticed influences such as mechanical clearance have been considered to design a more precise and intelligent fault-tolerant plan for actual plants. According to regulation principles in human hemostasis system, a bio-inspired intelligent integrated cooperative controller (BIICC) is developed including system structure, algorithm and step in parameter tuning. The proposed BIICC optimises partial error signal and improves control performance in each sub-channel. Moreover, the new controller transfers and disposes cooperative control signals among different sub-channels to achieve an intelligent integrated fault-tolerant system. The proposed BIICC is applied to an actual 2-DOF (degrees of freedom) redundant parallel manipulator where the feasibility of the new controller is demonstrated. The BIICC is beneficial to control precision and fault-tolerant capability of redundant plant. The improvements are more obvious in cases where extra actuators of redundant manipulator are broken.

A Bioinspired Multilayered Intelligent Cooperative Controller for Stretching Process of Fiber Production

The stretching process is one of the key sections in fiber production, which is decisive to the quality of the final fiber products. Such a process raises high requirements on the control of the rollers with proper stretching ratios, and the large number of rollers with their special characteristics and the demand for synchronous running usually make the design of a good control scheme difficult. In this paper, a novel bioinspired multilayered intelligent cooperative controller (BMLICC) is proposed to provide a control plan for the interlinked rollers by organizing them into unified stretching units. Based on the multilayer regulation networks of neuroendocrine system in the human body, a networked controller structure is established. It consists of several components like rollers, distributed controllers, communication paths, and conversion units. The rollers in the same unit can exchange the working information rapidly to implement simultaneous response and cooperation. The stretching ratio can be kept stable and has strong resistance against the external disturbances on the stretching system. Both computer-simulation- and device-based experimental results demonstrate that the stretching unit with the proposed BMLICC can maintain its stretching ratio and effectively resist the external disturbances. This is beneficial to improve the performance of the stretched precursors and, furthermore, produce fibers with high quality. The proposed BMLICC can be easily extended to productions with multiple stretching units or industrial processes with similar mechanical structures for better control quality.

Hierarchical Fuzzy Cooperative Control and Path Following for a Team of Mobile Robots

In this paper, an intelligent cooperative control and path-following algorithm is proposed and tested for a group of mobile robots. The core of this algorithm uses a fuzzy model, which mimics human thought to control the robot's velocity, movement, and group behavior. The designed fuzzy model employs two behaviors: path following and group cooperation. Hierarchical controllers have also been developed based on fuzzy and proportional integral derivative to instruct the robots to move in a group formation and follow specific paths. As the robots move along individual predetermined paths, the designed algorithm adjusts their velocities so that the group arrives at their target points within the same time duration regardless of the length of each individual path. The fuzzy rules applied to the robots are defined by the kinematics limitation, which is bounded by both linear and angular velocities and the length and curvature of the individual paths. The experimental results of three mobile robots traveling on different paths are presented to show the accuracy of obtaining control and cooperation by using the fuzzy algorithm.