New Distributed Consensus Schemes With Time Delays and Output Saturation

Cryptocurrencies have seen a massive surge in popularity and behind these new virtual currencies is an innovative technology called the blockchain: a distributed digital ledger in which cryptocurrency transactions are recorded after having been verified. The transactions within a ledger are verified by multiple clients or “validators,” within the cryptocurrency's peer-to-peer network using one of many varied consensus algorithms for resolving the problem of reliability in a network involving multiple unreliable nodes. The most widely used consensus algorithms are the Proof of Work (PoW) algorithm and the Proof of Stake (PoS) algorithm; however, there are also other consensus algorithms which utilize alternative implementations of PoW and PoS, as well as other hybrid implementations and some altogether new consensus strategies. In this paper, we perform a comparative analysis of typical consensus algorithms and some of their contemporaries that are currently in use in modern blockchains. Our analysis focuses on the algorithmic steps taken by each consensus algorithm, the scalability of the algorithm, the method the algorithm rewards validators for their time spent verifying blocks, and the security risks present within the algorithm. Finally, we present our conclusion and some possible future trends for consensus algorithms used in blockchains.

A large number of wind turbine generators in the large-scale offshore wind farm system poses a challenge to the wind farm control system due to the computational burden in the central control methods or the complexity of the communication network in the decentralized control strategies. A hybrid control method based on the distributed consensus control and the central model predictive control is proposed in this study to overcome the problem. Typically, the wind turbine generators in the large-scale offshore wind farm system are clustered into several groups. The consensus-based distributed reactive power coordination control is proposed to each cluster and the centralized predictive voltage control is used to manage the total reactive powers of all clusters and regulate the voltage at the point of common coupling. The gradient-descent algorithm for the optimal design of the consensus-based cluster control is presented firstly. Based on the convergence property of the consensus control, the equivalent model of the total reactive power response of each cluster is identified, which is used for the design of the centralized predictive voltage control. Eigenvalue analysis of the proposed predictive control strategy is carried out to verify the stability of the distributed and predictive control systems. The robustness of the proposed predictive controller is evaluated in the conditions of significant model errors due to the communication delay in each cluster. A comparison study with the full distributed control based on consensus algorithm is presented to demonstrate the effectiveness of the proposed control method. The feasibility of the proposed predictive controller is evaluated by the control-hardware-in-the-loop simulation using OPAL-RT Technologies. An additional comparison study in term of computation time with the central control method is also carried out. Real-time simulation results show the superior performance of the proposed hybrid method compared to the full distributed consensus controller or the central control strategies.

The purpose of this paper is two-fold. First, the smoothness of the distributed consensus control (DCC) protocols of multi-agent systems (MAS) with inherent nonlinear dynamics is investigated. Different from the traditional consensus protocols where variable structure control is considered for robust control, a continuous DCC is presented to achieve consensus tracking without control chattering. Using certain types of exponential functions, it is shown that different smooth DCC schemes can be constructed to achieve asymptotic consensus tracking with smooth control input for MAS with connected directed/undirected and fixed/switched communication topologies. Second, the formation control problem of nonholonomic wheeled mobile robots (WMR) is solved via the continuous DCC. By converting the formation problem into consensus problem using the agents’ kinematics, the formation speed-control problem in planar motion is formulated and solved. The translational and rotational speeds inputs to the WMR are computed using continuous distributed consensus protocols to reduce chattering in the control inputs of mobile robots. The distinctive feature of the proposed consensus and formation algorithms is the high tracking precision with smooth control input to agents’ dynamics. In simulation studies, different numerical examples are illustrated to demonstrate the performance and effectiveness of the proposed DCC.

An efficient Proof-of-Authority consensus scheme against cloning attacks

In general, the Internet of Things (IoT) relies on centralized servers due to limited computing power and storage capacity. These server-based architectures have vulnerabilities such as DDoS attacks, single-point errors, and data forgery, and cannot guarantee stability and reliability. Blockchain technology can guarantee reliability and stability with a P2P network-based consensus algorithm and distributed ledger technology. However, it requires the high storage capacity of the existing blockchain and the computational power of the consensus algorithm. Therefore, blockchain nodes for IoT data management are maintained through an external cloud, an edge node. As a result, the vulnerability of the existing centralized structure cannot be guaranteed, and reliability cannot be guaranteed in the process of storing IoT data on the blockchain. In this paper, we propose a multi-level blockchain structure and consensus algorithm to solve the vulnerability. A multi-level blockchain operates on IoT devices, and there is an IoT chain layer that stores sensor data to ensure reliability. In addition, there is a hyperledger fabric-based monitoring chain layer that operates the access control for the metadata and data of the IoT chain to lighten the weight. We propose an export consensus method between the two blockchains, the Schnorr signature method, and a random-based lightweight consensus algorithm within the IoT-Chain. Experiments to measure the blockchain size, propagation time, consensus delay time, and transactions per second (TPS) were conducted using IoT. The blockchain did not exceed a certain size, and the delay time was reduced by 96% to 99% on average compared to the existing consensus algorithm. In the throughput tests, the maximum was 1701 TPS and the minimum was 1024 TPS.

In this paper, a distributed consensus control strategy is introduced for a multi-UAV system with uncertain gains. A multi-UAV system is developed from unmanned aerial vehicles with gust winds over directed communication topology. The distributed consensus control strategy mainly consists a nonlinear sliding mode observer and distributed consensus controller. The nonlinear sliding mode observer is designed with a nonlinear error feedback function to estimate external disturbances from gust winds. Based on nonlinear Nussbaum functions and sliding mode manifolds, the distributed consensus controller is proposed to achieve consensus performance in the multi-UAV system with uncertain gains. Stability of the multi-UAV system is analyzed by Lyapunov methods for the distributed consensus control strategy. Effectiveness of the distributed consensus control strategy is shown with simulation results on a multi-UAV system of six UAVs under directed communication topology.KeywordsMulti-UAV systemDistributed consensus controlSliding mode observer (SMO)Directed communication topologyGust winds

Consensus algorithms are widely applied in distributed control of microgrids. With distributed control, the performance of a microgrid is significantly relevant to the time delays of the communication network. This paper mainly focuses on the time delays in the consensus-based control in microgrids. The influence of time delays is analyzed and the stability margin of the time delays is given. It is proved that the stability of the microgrid depends only on the local processing time delays of DGs, while the communication time delays between different DGs would only influence the convergence speed. Moreover, an AC microgrid with four DGs and some loads are implemented and the stability margin of time delays is verified by MATLAB/Simulink simulation results.

This paper presents a neural adaptive finite-time dynamic surface control for the permanent magnet synchronous motor system with time delays and asymmetric time-varying output constraint. The core challenge is how to address the time delays and asymmetric output constraint when designing a finite-time control scheme. Given this, a proper Lyapunov–Krasovskii functional is introduced to address time delays, and a nonlinear transformation function is considered to convert the output-constrained problem into an unconstrained one. Then, a neural adaptive finite-time dynamic surface control approach is devised in the finite-time backstepping framework, which applies neural networks to estimate the unknown nonlinear functions and introduces first-order filters to solve the “explosion of complexity” problems. Furthermore, it is demonstrated that all the signals of the resulting system are finite-time stable and the tracking error converges to a neighborhood of origin in finite time without violating the output constraint. Finally, the simulation results show that the integration of squared error results, the integral of time and absolute error results as well as the integration of absolute value error results of the proposed scheme is smaller than the tested scheme by 0.3458 , 22.2977 , and 2.2513 , respectively, when the time delays are considered. It further elucidates the availability and superiority of the developed method.

Using dynamic positioning (DP) ship maintains its position and heading by active thrusters. Several challenges exist with regard to the DP controller design, such as uncertain system model, actuator with saturation hard limitations, state or output (ship position or orientation) constraints. A robust receding horizon dual-mode control method is introduced into the design of DP controller using the property of the invariant ellipsoid. Outside the ellipsoid, the receding horizon control is employed .Once the state enters the ellipsoid, the control is switched to a linear state feedback control, which makes the state stay in the ellipsoid forever and stabilizes the system while satisfying the input, the state and the output constraints. The DP control is obtained by using semi-definite programming (SDP) which can be easily solved by means of linear matrix inequalities (LMI). At last, the simulation study is carried out to verify the feasibility and efficiency of the application of robust receding horizon dual-mode control for dynamic positioning ship.

In this paper, we described a class of nonlinear multi-agent dynamic systems in consensus problem which every agent is multi-dimensional dynamic system. The control objective is expect to find suitable consensus controller for every agent such that 1) guarantee all the signals in the dynamic systems remain bounded. 2) design the consensus controllers for every agent then the average-consensus behavior of the multi-agent systems can be obtained.

H ∞ full information control method for structural vibration suppression with control delay is proposed within a discrete-time framework. Firstly, the continuous-time system equation with control delay is discretized by precise computation of matrix exponentials. By introducing an appropriate extended state vector, the time-delay system is transformed into standard discrete forms without time delays. Then a controller is designed based on the discrete time H ∞ full information control theory. The controller consists of a regular feedback closed-loop and a feed-forward compensator which utilizes external excitation signals measured in real time. The feed-forward plus feedback controller has better performance than feedback only controllers. The feedback loop of the deduced controller contains linear combinations of former control inputs besides the current state feedback terms. Thus the proposed method is applicable to large time-delay cases. Considering the complexity of the transformed system, a 2 N algorithm is introduced to search the optimal attenuation level and evaluate the control law. The present algorithm can be executed parallel, which makes the design of H ∞ controllers very convenient. Finally, numerical simulations of a three-story structure control are implemented. Time-delay effects on the optimal attenuation level are given to show that it is an important factor for system stability. Simulation results demonstrate the feasibility and effectiveness of the proposed control method.

Consensus algorithms are promising control schemes for secondary control tasks in microgrids. Since consensus algorithms are distributed protocols, communication efforts and time delays are significant factors for the control design and performance. Moreover, both the electrical and the communication layer in a microgrid are continuously exposed to exogenous disturbances. Motivated by this, we derive a synthesis for a consensus-based secondary frequency controller that guarantees robustness with respect to time-varying delays and in addition provides the option to trade off $L_{2}$ disturbance attenuation against the number of required communication links. The efficacy of the proposed approach is illustrated via simulations based on the CIGRE benchmark medium voltage distribution network.

In this work, considering the influence of input saturation and time-varying output constraints, an adaptive neural networks force/position control method is proposed for a class of uncertain robotic manipulators in contact with the external environment. Radial basis function neural networks are introduced to approximate the system uncertainty and only one parameter needs to be updated online. The output constraints are satisfied by converting the constrained system to an unconstrained one. Moreover, an auxiliary dynamic system is applied in the transformed unconstrained system to handle the input saturation nonlinearity and the Moore–Penrose pseudo-inverse term is introduced for controller design. All signals in the closed-loop system are proved to be bounded by using the Lyapunov method. To show the good performance of the given scheme, comparative simulation is conducted on a two-joint manipulator, which is controlled by the proposed adaptive neural networks force/position tracking controller and a conventional force/position controller without considering input saturation and output constraints. Simulation results show that the adaptive neural network force/position controller has better control performance than the traditional method.

In this paper, a novel robust distributed consensus control scheme based on event-triggered adaptive sliding mode control is proposed for multiagent systems with unknown disturbances in a leader-follower framework. First, an adaptive multivariate disturbance observer is utilized to compensate for the disturbance of each agent. Next, a distributed consensus control protocol is constructed via integral sliding mode control, in which a novel adaptive law is designed for the switching gain to overcome the unknown perturbations. An event-triggered strategy is designed to update the control input. Furthermore, the feasibility of the proposed scheme is rigorously analyzed by Lyapunov theory, and a lower bound expression for the inter-event time is derived to guarantee that Zeno behavior can be excluded. The proposed nonlinear consensus algorithm is remarkable in that it does not require any information about the bounds of the disturbances. Finally, compared with existing methods, the proposed algorithm is validated through detailed numerical simulations. In addition, the proposed algorithm is applied to a group of UAVs in this paper, and the results show that it has more application value.

Markovian jumping systems have arisen naturally in the mathematical modeling of phenomena spanning disciplines in the social sciences, natural sciences, and engineering. This kind of stochastic dynamical systems can be employed to model the dynamics when parameters are subject to random abrupt changes due to sudden environment changes, subsystem switching, system noises, executor faults, and so forth. Much attention has been given to modeling, optimization, and real applications of such stochastic dynamical systems in the literature in recent years. As the advanced control and optimization will provide a basis for the design and application of such stochastic systems, these advanced techniques would result in substantial and sustainable benefits. The accepted papers in this special issue include stochastic stability, stabilization, stochastic control optimization, system modeling and identification methods, predictive control, signal processing, robust filtering, multiagent systems, networked control systems, time-delayed systems, neural networks, the Takagi-Sugeno fuzzy systems, simulated annealing, and fault detection methods.