This paper investigates the fixed-time time-varying formation control problems for heterogeneous multi-agent systems (MASs) composed of multiple Unmanned Ground Vehicles (UGVs) and multiple Unmanned Aerial Vehicles (UAVs) in the presence of actuator faults, parameter uncertainties, matched and mismatched disturbances. Besides achieving the desired formation configurations, each follower can also track the position trajectory produced by the virtual leader within fixed time simultaneously. The difference dynamic characteristics between the heterogeneous agents leads to unbalanced interaction of lumped uncertainties in the communication network, which increases the difficulty of collaborative control. To estimate the mismatched disturbances and lumped uncertainties, a fixed-time observer for each follower is designed, which can guarantee the estimation errors converge to the origin in fixed settling time. Subsequently, by utilizing the backstepping technique and the fixed-time stability theory, an observer-based distributed fixed-time formation controller for each follower in the $X$ - $Y$ axes and the observer-based decentralized fixed-time tracking controllers for follower-UAVs in the $Z$ axes are presented, which are shown to be fixed-time stable even under the influence of actuator faults and mismatched disturbances. Moreover, the fixed-time results can ensure the convergence time is independent of initial conditions. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithms.

Fixed-Time Fault-Tolerant Formation Control for Heterogeneous Multi-Agent Systems With Parameter Uncertainties and Disturbances

With the ever-increasing number of blackouts in distribution systems arising from a variety of natural and manmade disasters, the frequent and necessary isolation/reconnection of loads without power deviations/fluctuations has become an important issue. Grid of microgrids (MG)s is a promising solution towards a highly resilient and efficient power grid operation. To facilitate this implementation, seamless transition with the utility grid is a key feature the today's MG control needs to possess. This transition ability is of great prominence, especially after the accelerated adaptation of distributed renewable energy sources (RES) in MGs. This ability of the MGs should ensure uninterrupted energy services to critical loads and infrastructures. Thus, the implementation of MG control strategies to enable smooth transition between grid-connected (GC) and islanded (IS) operation modes is mandatory. The control scheme implemented should therefore be capable of mitigating the stirring voltage/current deviations due to frequency/phase misalignment during the transition process. This paper provides an overview of the various MG control schemes that enable seamless transition between GC and IS modes of operation. The main purpose of this paper is to provide a generic overview of the challenges and existing techniques available in literature to mitigate the voltage and frequency ( V-f ) fluctuations at the MG's point of common coupling (PCC) and that of the utility grid; during the transition process. It aims at motivating the development of advanced control schemes in this area of MG related research.

/pdf/towards-grid-of-microgrids-seamless-transition-between-grid-4art42k0bw.pdf

Towards Grid of Microgrids: Seamless Transition between Grid-Connected and Islanded Modes of Operation

This article proposes a high-order disturbance observer (HODO) and dynamic surface control (DSC) technique-based adaptive fuzzy control scheme for nonlinear systems subjected to input saturation and external time-varying disturbances. First, based on a Sigmoid function, the saturation input is tackled by utilizing a well-defined nonlinear smooth function. Furthermore, HODO and fuzzy logic systems are used to estimate the external disturbances and to handle the lumped unknown functions, respectively. Then, by using the backstepping method and DSC technique, a HODO-based adaptive fuzzy tracking control scheme is proposed for nonlinear systems with saturation nonlinearity, uncertainties, and external disturbances. The Lyapunov analysis method is used to prove that all signals in the entire system are semiglobally uniformly ultimately bounded (SGUUB). In addition, the tracking error converges to a compact set with a tunable error bound determined by some design parameters. Finally, a numerical simulation of two-stage chemical reactor shows the effectiveness of the developed tracking control strategy.

Fuzzy Robust Constrained Control for Nonlinear Systems With Input Saturation and External Disturbances

The term carbon (C) sequestration has not just become a buzzword but is something of a siren's call to scientific communicators and media outlets. Carbon sequestration is the removal of C from the atmosphere and the storage, for example, in soil. It has the potential to partially compensate for anthropogenic greenhouse gas emissions and is, therefore, an important piece in the global climate change mitigation puzzle. However, the term C sequestration is often used misleadingly and, while likely unintentional, can lead to the perpetuation of biased conclusions and exaggerated expectations about its contribution to climate change mitigation efforts. Soils have considerable potential to take up C but many are also in a state of continuous loss. In such soils, measures to build up soil C may only lead to a reduction in C losses (C loss mitigation) rather than result in real C sequestration and negative emissions. In an examination of 100 recent peer‐reviewed papers on topics surrounding soil C, only 4% were found to have used the term C sequestration correctly. Furthermore, 13% of the papers equated C sequestration with C stocks. The review, further, revealed that measures leading to C sequestration will not always result in climate change mitigation when non‐CO2 greenhouse gases and leakage are taken into consideration. This paper highlights potential pitfalls when using the term C sequestration incorrectly and calls for accurate usage of this term going forward. Revised and new terms are suggested to distinguish clearly between C sequestration in soils, SOC loss mitigation, negative emissions, climate change mitigation, SOC storage, and SOC accrual to avoid miscommunication among scientists and stakeholder groups in future.

Carbon sequestration in soils and climate change mitigation-Definitions and pitfalls.

Exploring the ecological quality and its drivers based on annual remote sensing ecological index and multisource data in Northeast China

Coastal wetland soil organic carbon (CW-SOC) is crucial for both “blue carbon” and carbon sequestration. It is of great significance to understand the content of soil organic carbon (SOC) in soil resource management. A total of 133 soil samples were evaluated using an indoor spectral curve and were categorized into silty soil and sandy soil. The prediction model of CW-SOC was established using optimized support vector machine regression (OSVR) and optimized random forest regression (ORFR). The Leave-One-Out Cross-Validation (LOO-CV) method was used to verify the model, and the performance of the two prediction models, as well as the models’ stability and uncertainty, was examined. The results show that (1) The SOC content of different coastal wetlands is significantly different, and the SOC content of silty soils is about 1.8 times that of sandy soils. Moreover, the characteristic wavelengths associated with SOC in silty soils are mainly concentrated in the spectral range of 500–1000 nm and 1900–2400 nm, while the spectral range of sandy soils is concentrated in the spectral range of 600–1400 nm and 1700–2400 nm. (2) The organic carbon prediction model of silty soil based on the OSVR method under the first-order differential of reflectance (R′) is the best, with the Adjusted-R2 value as high as 0.78, the RPD value is much greater than 2.0 and 5.07, and the RMSE value as low as 0.07. (3) The performance of the OSVR model is about 15~30% higher than that of the support vector machine regression (SVR) model, and the performance of the ORFR model is about 3~5% higher than that of the random forest regression (RFR) model. OSVR and ORFR are better methods of accurately predicting the CW-SOC content and provide data support for the carbon cycle, soil conservation, plant growth, and environmental protection of coastal wetlands.

/pdf/estimation-of-soil-organic-carbon-content-in-coastal-k43g36tt.pdf

Estimation of Soil Organic Carbon Content in Coastal Wetlands with Measured VIS-NIR Spectroscopy Using Optimized Support Vector Machines and Random Forests

This paper proposes robust formation control laws for uncertain multiagent systems subjected to an unknown control direction and disturbances under the assumption that information about the reference trajectory is only available to some of the agents. Different from the existing results, the proposed control algorithms can enable the agents to converge to the specified points utilizing only local relative measurements, in which no control direction information and upper bounds of external uncertainties are required. To simplify and facilitate the analysis, external disturbances are roughly divided into structured and unstructured uncertainties. To attenuate the errors caused by the structured uncertainties, an adaptive control technique is applied to obtain the estimates of the unknown constant coefficients, and compensating functions with learning ability are developed to eliminate the errors caused by the unstructured uncertainties. A Nussbaum-type function is employed to settle the formation control problem in the presence of unknown signs of the control direction. Formation stability analysis for uncertain nonlinear multiagent systems is performed according to the Lyapunov stability theory. It is proved that the formation errors are bounded within a small desired accuracy by implementing the proposed robust control laws, and the desired formation is then achieved. Finally, two illustrative examples are presented to support the obtained theoretical results.

/pdf/robust-formation-control-for-uncertain-multiagent-systems-3b3w0rvx93.pdf

Robust Formation Control for Uncertain Multiagent Systems With an Unknown Control Direction and Disturbances

This paper analyzes the distributed formation control algorithms that rely on local measurements for second-order multiagent systems under the leader-follower control structure. Here, the reference states are only available to the leader agents, the follower agents have access to the information from the leaders to achieve the control objective, and the target formation is defined by their relative positions. The formation control problem with constant and time-varying reference states is studied, and two classes of practical control system problems in the presence external disturbances are also considered. In the former case, a non-saturated Proportional-integral (PI) controller is proposed to attenuate the constant disturbances, namely, the static errors are eliminated. In the latter case, a saturated distributed control algorithm is first proposed to address the formation control of multiagent systems, which, in many practical applications, is subject to bounded control inputs. To eliminate the effect of chattering caused by external bounded time-varying disturbances, the distributed formation control algorithm is then proposed. The analysis for the proposed control algorithms according to Lyapunov stability theory is provided. A sufficient collision-free condition is also presented. Finally, to demonstrate the effectiveness of the obtained theoretical results, the numerical simulation results of several illustrative examples are presented.

/pdf/distributed-formation-control-for-multiagent-systems-in-the-5fdy0qhnjj.pdf

Distributed Formation Control for Multiagent Systems in the Presence of External Disturbances

Predefined-time robust control with formation constraints and saturated controls

Maintaining the given operational area is critical in guaranteeing the safety of nonlinear second-order multiple autonomous agents. The properties of multiagent systems and several physical constraints, including bounded modeling error and actuator saturation, dramatically affect the maneuverability of multiagent systems inside the specified operational area. Moreover, the existing safety control algorithms heavily rely on the boundaries of the operational area. To overcome this issue, by constructing a novel scalable control technique, the safety area for multiagent systems can be transformed into input-constrained control barriers along each coordinate of motion for agents. It is shown that the safety of each agent and the global asymptotic stability are guaranteed under the proposed distributed control algorithm. The asymmetrical closed-form scheme for the agent's safety rule is built by applying the adjustable low and high bounds of the control signals associated with the actual control inputs, which are repeatedly computed by using new local measurements as the agents move, and the saturated control inputs with asymmetrical constraints are ensured. The absolute values of the modeling errors and external disturbances can be tracked by the proposed safety controller. Super-twisting control (STC) is employed to address the formation constraint problem of multiagent systems, where the effect that arises from uncertain nonlinear complexity of the agents and external disturbances is eliminated. Moreover, finite-time convergence, a desirable robust behavior of multiagent systems, and the formation constraint are simultaneously achieved. Furthermore, the stability of the proposed integrated control strategy for multiagent systems is analyzed, which reveals that the proposed distributed safety control can seamlessly integrate with the robust control protocol with minimum modification under the directed information interaction topology. Safety formation control calibration and tuning are carried out, and comparative simulation results are provided to illustrate the effective performance of the obtained theoretical results. 

Safety control using barrier certificates for multiagent systems with input saturation and formation constraints

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