Cooperative Control Strategy Research Articles

Two-parameter cooperative intelligent optimal control of chaotic motion for a vibro-impact system with soft constraint

For the chaotic motion control of a single-degree-of-freedom vibro-impact system with soft constraint, two-parameter cooperative intelligent optimal control method based on LRS-QPSO (i.e., Local Rotational Search Quantum Particle Swarm Optimization) algorithm optimization SVM (i.e., Support Vector Machines) was proposed. Firstly, in the parameter plane (ω,ζ), the motion distribution of the system and the change of the maximum Lyapunov exponent is obtained by numerical simulation, and the correlation between parameters change and the transition from chaotic motion to periodic motion is analyzed. Secondly, by introducing ω and ζ into the construction of the input variables of the SVM controller, a chaotic motion two-parameter cooperative intelligent optimal control strategy is proposed to meet the requirements of expected control objectives. Therefore, by the virtue of nonlinear mapping characteristics of SVM, the combined effects of ω and ζ on the dynamic state transition of the system can be reflected in the output variables of the controller. Finally, with single parameter ω and single parameter ζ as the research object, the bifurcation diagram, Lyapunov exponential spectrum, and controlled periodic motion orbit diagram are analyzed, and the advantages of two-parameter cooperative intelligent optimal control are compared and analyzed.

Cooperative Game-Based Driver Assistance Control for Vehicles Suffering Actuator Faults

To assist different styles of drivers to maintain vehicle stability in the event of actuator function failures, this paper proposes a cooperative game-based robust fault-tolerant control (FTC) strategy for four-wheel independent actuated electric vehicles. The Takagi-Sugeno fuzzy structure is used to model a driver-vehicle system to describe driver characteristics in the FTC scheme and deal with the nonlinearity among states of the complex system. Viewing the FTC process as a dynamic cooperative game so that the actuators of the driver-vehicle system are described as the players. The interactions among players are modeled by the distributed model predictive control scheme. Finally, an adaptive compensation control mechanism is developed to make the cooperative game paradigm more robust. Simulation verification is used to reveal the influence and the inherent characteristics of task weights in the cooperative game. Besides, two severe failure cases are studied via the hardware-in-the-loop test to prove that the proposed method can maintain the stability of the failed vehicle driving by different drivers.

Distributed cooperative secondary control based on discrete consensus for DC microgrid

Aiming at overcoming the drawbacks of bus voltage deviation and current sharing accuracy degradation caused by conventional v-i droop control in primary control of DC microgrid, a distributed cooperative secondary control strategy is proposed in this paper. The strategy includes voltage restoration control and current sharing control. Discrete consensus is applied in the proposed method which considers the discrete nature of measurement, controller implementation, and communication. The controller on each converter exchanges data with only its neighbors on a sparse network of communication across the DC microgrid, further dynamically tracking the output voltage and current. In addition, the proposed control strategy can reduce the impact of communication noise on consensus convergence and accuracy by introducing the consistency gain function. The effectiveness of the proposed method is verified by a grid-connected DC microgrid system based on the MATLAB/Simulink.

A Control Strategy for Unmanned Surface Vehicles Flocking

Decentralized control allows each unmanned surface vehicle (USV) in a flock to be autonomous to some extent. In this paper, the flocking algorithm put forth by Olfati-Saber was improved and applied in the multi-leader scenario based on its original flocking movement. Repulsion was also taken into account in the formation. Subsequently, the concept of neighborhood in finite-range communications was borrowed to control the communication distance of each USV. In this way, USVs could satisfy the restriction of a given communication distance, and avoid collision in motion. USVs might have collaborative tracking of a virtual leader and obtain the distributed cooperative control strategy. Based on the simulation results, the improved flocking control algorithm allowed every USV to accurately track the speed of the virtual leader they followed, respectively. The test results also demonstrated the effectiveness of the algorithm in safe flock navigation, and proved that the proposed control algorithm must be an effective way to ensure the safe flocking of multiple USVs.

Generalized demand-side resource hierarchical control strategy based on multi-agent consensus of historical data online transfer

To achieve large-scale generalized demand-side resources to participate in the “peak-shaving and valley-filling” of the power system, a distributed cooperative control strategy for discrete-time linear multi-agents considering historical data is proposed, which comprehensive consider the changes in load commands, topology, multi-objective function coefficients, communication delays and input delays. This paper combines the distributed multi-agent system with the hierarchical architecture to solve the problems of extensive data calculations in centralized control, which may easily cause communication channel blockage and delay. Considering the cost of changing the generalized demand-side resources and the user’s comfort, the pre-learning historical data storage strategy based on K-means clustering is adopted before the actual task, and the idea of transfer learning is introduced into the consensus algorithm. Finally, through experimental statistics, the number of iteration steps of the consensus algorithm is reduced and the effectiveness of the proposed algorithm is proved.

Multi-agent cooperative structural vibration control of three coupled flexible beams based on value decomposition network

Multi-coupled flexible body (MCFB) structure has the characteristics of small damping, close modes and coupled vibration, and the structural vibration is difficult to freely attenuate, which will lead to rapid fatigue failure of the structure. In order to suppress the vibration of the MCFB system quickly, a system identification scheme and a multi-agent reinforcement learning (MARL) scheme are proposed to obtain the cooperative controller. The mathematical model of the three-coupled flexible beam (TCFB) system is identified by wavelet analysis method, particle swarm optimization (PSO) and sinusoidal excitation response method. Based on value decomposition network (VDN) and deterministic policy gradient (DPG), a cooperative MARL framework is constructed to obtain the controller (VDN-DPG controller). The cooperative VDN-DPG controller is applied to the vibration control simulation and experiments of the TCFB system, and compared with proportional–derivative (PD) control without cooperation. The results show that the performance of the VDN-DPG controller is better than that of the PD controller, which verifies the effectiveness of the multi-agent cooperative control scheme based on VDN-DPG.

Cooperative robust adaptive control of multiple trains based on RBFNN position output constraints

AbstractThe stability of each train, high control accuracy, and minimum safe separation distance are important indexes to measure the performance of the cooperative control system of multiple trains. In this article, aiming at the problem of low accuracy of multiple trains cooperative control with nonlinear running resistance and external disturbance, the distributed cooperative robust adaptive control scheme for multiple trains with RBFNN position output constraints based on train running curve tracking is proposed. Multiple different control techniques are offered for different trains, and that they are based on local knowledge of position, speed, and acceleration. The leading train's speed and position precisely match the planned operation curve, while the following train keeps the tracking interval at the minimum safe distance between two trains. In order to reduce the influence of the uncertainty of basic resistance parameters and external interference on the cooperative control of multiple trains, the parameter uncertainties are compensated by adding a robust adaptive law to the multiple trains control based on position output constraints. The lumped exogenous disturbances (additional resistance, external interference, measurement noise, etc.) are estimated using an RBFNN approximator for the unknown term of the cooperative system. The stability of the cooperative operation of multiple trains is confirmed using the Lyapunov stability theorem. The performance of the proposed scheme was evaluated by the cooperative control system of multiple trains in predecessor following (PF) and bidirectional control (BC) modes.

Synchronization tracking control of networked multi-axis motion systems: A cooperative distributed model predictive control approach

Synchronization tracking control is an essential technique in networked multi-axis motion systems (NMAMSs). However, the uncertainties induced by network, such as delay and packet disordering, have great influence on control performance of the motion systems. To improve the influence of the uncertainties induced by network on synchronization tracking control performance in NMAMSs, a cooperative distributed model predictive control (CDMPC) strategy for NMAMSs is proposed. Firstly, the synchronization tracking control of the NMAMS is designed in a networked control framework. By introducing the synchronization error into performance index as a coupling term, a local controller adopting a CDMPC strategy is designed for each subsystem. Then, a Nash optimization iteration strategy is presented to deal with the CDMPC optimization problem, and the convergence and stability of the system are also analyzed. Finally, experiments are carried out to demonstrate the practicability and effectiveness of the proposed control strategy.

Dynamic hierarchical modeling and control strategy of high temperature proton exchange electrolyzer cell system

High temperature proton exchange membrane electrolyzer cells (HT-PEMECs) show faster reaction kinetics than the low temperature PEMECs (LT-PEMECs) and are suitable for utilizing waste heat from the industry. However, dynamic modeling and control of HT-PEMECs are still lacking, which is critical for integrating the HT-PEMECs with fluctuating renewable power. In this study, hierarchical models are developed to investigate the transient behavior of the HT-PEMEC system with hydrogen recirculation. It is observed that the maximum efficiency point of the reference power can be reached by cooperatively adjusting the current density and anode inlet gas flow rate, and the application of artificial neural networks can accurately predict the operating conditions at the points of maximum efficiency. Moreover, the proposed cooperative model predictive control strategy not only improves the efficiency (about 1.2%) during dynamic processes but also avoids the problem of reactant starvation. This study provides useful information to understand the dynamic behaviors of HT-PEMECs driven by excess renewable power.

Development of a Parallel Dual-Stage Compliant Nanopositioning System

This paper presents a novel parallel dual-stage compliant nanopositioning system (PDCNS), aimed at nanoscale positioning for microscale manipulation. In the developed PDCNS, the coarse stage actuated by the voice coil motor and the fine stage driven by the piezoelectric actuator are integrated in a parallel manner by a specially devised A-shaped compliant mechanism, which leads to many excellent performances, such as good resolution and large stroke and broadband. To enhance the closed-loop-positioning capability of the proposed PDCNS, a double-servo cooperative control (DSCC) strategy is specially constructed. The performance of the proposed PDCNS is evaluated by analytical model, finite element analysis, and experimental research. Results show that the first-order resonance frequency of the designed A-shaped compliant mechanism can reach 99.7 Hz. Combined with the designed DSCC, the developed PDCNS prototype is demonstrated to provide a stroke of 1.49 mm and a positioning resolution of ≤50 nm.

A two‐loop group formation tracking control scheme for networked tri‐rotor UAVs using an ARE‐based approach

AbstractCooperative control of networked unmanned aerial vehicles (UAVs) has received significant research interest over the last decade due to its potential applications in military security and surveillance, search and rescue, planetary exploration, precision agriculture, and so on. Many of these practical activities can be formulated as a group formation tracking problem with multiple targets to track. This paper aims to address such problems via designing a cooperative control scheme for networked tri‐rotor UAVs connected with directed graph topology. The proposed methodology consists of a two‐loop control scheme—the inner loop applies a robust feedback linearization technique to linearize the coupled, nonlinear dynamics of the tri‐rotor UAVs; while the outer loop facilitates an ARE‐based cooperative group formation tracking scheme. Tri‐rotor UAVs are considered in this paper instead of quad‐rotor UAVs, which are more common in drone applications, to conquer a major limitation of the quad‐rotor UAVs that it cannot alter its attitude independently while hovering at a particular height. A rigorous theoretical proof is given to establish the two‐loop control scheme exploiting the Lyapunov stability approach and algebraic Riccati equation (ARE)‐based optimal control policy. An in‐depth case study on a multitarget surveillance mission has been performed in this paper using a virtual reality software simulation platform to demonstrate the usefulness and efficacy of the proposed scheme.

Distributed cooperative automatic generation control and multi-event triggered mechanisms co-design for networked wind-integrated power systems

The participation of wind energy brings new challenges to the networked power systems. Now, more network nodes compete for limited communication resources. Considering the utilization efficiency of network transmission resources, a novel multi-event triggered mechanisms based distributed cooperative automatic generation control scheme is investigated for networked wind-integrated power systems (NWIPSs). The multi-event triggered mechanisms based distributed wind-integrated power systems modeling method is proposed. By utilizing the Lyapunov-Krasovskii functional method, the sufficient conditions for system asymptotical stability are obtained. Concerning the previous event-triggered method the proposed multi-event triggered method widespread considered the coupling relationship between interconnected subsystems and make full use of each subsystem state information. The numerical example shows that the proposed scheme is feasible and the design of multi-event triggered mechanisms based distributed cooperative automatic generation controller provides less conservatism and stronger robustness.

Cooperative yaw control of wind farm using a double-layer machine learning framework

An appropriate cooperative yaw control strategy, in which the accurate power prediction and efficient optimization method play an essential role, can mitigate the significant power loss owing to the wake interference. The present study develops a novel double-layer machine learning framework, which combines an artificial neural network (ANN) yawed wake model and a Bayesian machine learning algorithm. The former undertakes the power prediction as the 1st layer, which is fed to the 2nd layer of the optimization system. Using computational fluid dynamics (CFD) simulation result as a reference, the performance of the proposed framework is evaluated compared with the framework based on a Gaussian-based analytical wake model by Qian and Ishihara. Furthermore, parametric studies have been conducted on the inflow conditions to explore its applicability. The results show that the proposed framework can realize more accurate power prediction with a mean error of 1.13% and 0.96% for different inflow velocities and turbulence intensities respectively, in contrast with the one based on the analytical model, 7.61% and 1.66%. Moreover, more notable power improvements can be achieved by the proposed framework, seeing a mean rise of 5.59% and 2.22% under four inflow velocities and turbulence intensities respectively, compared with 3.55% and 1.02% based on the analytical model. Specifically, its superiority becomes more obvious under relatively low inflow velocity and turbulence intensity.

Hardware Implementation of Cooperative Multitasking Control and Stability Analysis for DC Off-Grid System

In this article, the design and implementation of a cooperative multitasking control strategy for standalone photovoltaic (SPV) system is presented. A double-loop control strategy based on the fuzzy logic controller is developed to achieve high performance from the SPV without using the maximum power point technique, balance the power in the system, regulate the common dc-link voltage, and maintain system operation stability under disturbance. To ensure uninterruptible power supply to the critical loads during diminution of generated power from the SPV, turned <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> the diesel generator and the dc dump load, power management technique based on load scheduling technique is developed and implemented using the state-flow approach. Furthermore, the stability analysis in open and closed-loop using the average model of the dc–dc buck-boost converter is investigated. The performance of the dc off-grid configuration based on a single dc–dc converter and their control strategies are performed under load and solar irradiation change using MATLAB/Simulink and in real-time through hardware prototyping with DSP controller TMDS28379D.

Fixed‐time backstepping distributed cooperative control for multiple unmanned aerial vehicles

AbstractThis paper proposes a fixed‐time backstepping distributed cooperative control scheme based on fixed‐time extended state observer (FxTESO) for multiple unmanned aerial vehicles (UAVs). A fixed‐time ESO, which is convergent independently of initial conditions, is designed to estimate and compensate the external disturbances in tracking process. Moreover, to eliminate the “explosion of complexity” in the traditional backstepping architecture, a nonlinear first‐order filter is adopted to construct the distributed fixed‐time control scheme. Based on the local information of neighboring UAVs, a fixed‐time backstepping cooperative controller is designed. The proposed formation algorithm can be shown practicable for the UAV control system by using of Lyapunov stability theory and graph theory. Simulation results are given to demonstrate the effectiveness of the proposed control scheme.

A Distributed Fault Diagnosis and Cooperative Fault-Tolerant Control Design Framework for Distributed Interconnected Systems

This paper investigates a design framework for a class of distributed interconnected systems, where a fault diagnosis scheme and a cooperative fault-tolerant control scheme are included. First of all, fault detection observers are designed for the interconnected subsystems, and the detection results will be spread to all subsystems in the form of a broadcast. Then, to locate the faulty subsystem accurately, fault isolation observers are further designed for the alarming subsystems in turn with the aid of an adaptive fault estimation technique. Based on this, the fault estimation information is used to compensate for the residuals, and then isolation decision logic is conducted. Moreover, the cooperative fault-tolerant control unit, where state feedback and cooperative compensation are both utilized, is introduced to ensure the stability of the whole system. Finally, the simulation of intelligent unmanned vehicle platooning is adopted to demonstrate the applicability and effectiveness of the proposed design framework.

A Cooperative Shared Control Scheme Based on Intention Recognition for Flexible Assembly Manufacturing.

Human–robot collaboration (HRC) has been widely utilized in industrial manufacturing and requires a human to cooperate with a robot at the same workspace. However, as HRC focuses on workspace sharing along with independent work, it is not a real collaboration between a human and a robot and, thus, cannot guarantee a smooth cooperation and synchronous operation. To this end, a cooperative shared control scheme based on intention recognition is proposed in this study by sharing workspace and time. In the proposed method, a classification algorithm based on three-dimensional (3D) point cloud is utilized to recognize the human operation intention. Then, the robot can select a suitable tool to match the human's operation. A robot motion control algorithm is developed to detect the obstacles in the HRC process. A cooperative control strategy is introduced to achieve synchronous operation. A simple assembly task is also performed to demonstrate the proposed scheme's effectiveness. The proposed HRC method with shared control can be extended to more complicated and delicate flexible tasks in assembly manufacturing.

Fault-tolerant cooperative navigation of networked UAV swarms for forest fire monitoring

Coordination of unmanned aerial vehicle (UAV) swarms has received significant attention due to its wide practical applications including search and rescue, cooperative exploration and target surveillance. Motivated by the flexibility of the UAVs and the recent advancement of graph-based cooperative control strategies, this paper aims to develop a fault-tolerant cooperation framework for networked UAVs with applications to forest fire monitoring. Firstly, a cooperative navigation strategy based on network graph theory is proposed to coordinate all the connected UAVs in a swarm in the presence of unknown disturbances. The stability of the aerial swarm system is guaranteed using the Lyapunov approach. In case of damage to the actuators of some of the UAVs during the mission, a decentralized task reassignment algorithm is then applied, which makes the UAV swarm more robust to uncertainties. Finally, a novel geometry-based collision avoidance approach using onboard sensory information is proposed to avoid potential collisions during the mission. The effectiveness and feasibility of the proposed framework are verified initially by simulations and then using real-world flight tests in outdoor environments.

Frequency control strategy for coordinated energy storage system and flexible load in isolated power system

The isolated power system has a simple structure with small inertia and no support from the large-scale power system, so the frequency stability problem is more prominent. A frequency control method based on coordinated control of flexible loads (FL) and energy storage systems (ESS) is proposed in this paper. The ESS adopts the droop control considering the state of charge (SOC) to quickly respond to the system frequency deviation and provide fast frequency support. Based on the load power–voltage sensitivity, the FL participates in frequency control by adjusting the load voltage and thus the load demand. Based on the two frequency regulation resources, the active–reactive cooperative control strategy is proposed to adopt the fast response characteristics of ESS to cooperate with the synchronous unit to prioritize frequency regulation and enable the FL reactive control strategy when the system disturbance is larger than the primary frequency regulation capacity of the synchronous unit. The effectiveness of the proposed control strategy for isolated power systems is verified by building a modified IEEE33 node islanding system simulation in the real-time digital simulator (RTDS).

Cooperative overload control strategy of power grid-transformer considering dynamic security margin of transformer in emergencies

With the integration of large-scale renewable energy resources and growth of electricity demand, the peak-to-valley load difference increases constantly. The overload risk of transformer in emergencies is enlarging, which urgently requires cooperative control of power grid-transformer to avoid catastrophic accidents. Thus, a cooperative control strategy of power grid-transformer considering the dynamic security margin of the transformer in emergencies is proposed in this paper. First, the safety domain model of the transformer is constructed based on the temperature rising model as well as insulation deterioration pattern, and the dynamic security margin of the transformer is evaluated accordingly. Then, targeted counter measures are selected according to the safety status of the transformer, and different control schemes such as emergency overload, topology adjustment, and load shedding are generated by solving the optimization model. Finally, the effectiveness of the proposed strategy is validated in various scenarios. The results show that when the security margin of transformer is sufficient, the strategy can fully exploit its overload capability to support grid regulation and avoid premature load shedding; when the security margin is insufficient, load shedding is coordinated to ensure the security of transformer. The proposed strategy can realize the synergistic control and mutual support between power grid safety and equipment safety.