一种新的变步长盲均衡算法研究

常数模算法（constant modulus algorithm,CMA）能够很好地克服无线信道引入的符号间干扰（ISI）,在信道均衡中广泛应用,但存在稳态误差大,相位旋转的问题;MMA算法解决了CMA算法的相位旋转问题,但仍然有较大的稳态误差。为了克服以上缺点,在研究各种算法的基础上,引入非线性函数来构造步长调整参数,计算机仿真结果表明,相比传统算法,变步长盲均衡算法有较快的收敛速度和更好的均衡效果。     

一种自适应变步长迭代动态规划方法研究

针对传统迭代动态规划方法计算效率低的缺点,提出了一种改进的自适应变步长迭代动态规划方法,在求解中引人Runge—Kutta—Fehlberg自适应变步长方法来提高寻优精度和求解效率。以经典的间歇反应过程动态优化问题作为研究实例．研究结果表明：所提出的自适应变步长迭代动态规划方法,在保留传统的迭代动态规划方法有效寻找全局最优优点的同时,能够进一步提高寻优精度,而且优化效率也较高。     

An adaptive penalty-like continuous-time algorithm to constrained distributed convex optimization

This paper considers a nonsmooth constrained distributed convex optimization over multi-agent systems. Each agent in the multi-agent system only has access to the information of its objective function and constraint, and cooperatively minimizes the global objective function, which is composed of the sum of local objective functions. A novel continuous-time algorithm is proposed to solve the distributed optimization problem and effectively characterize the appropriate gain of the penalty function. It should be noted that the proposed algorithm is based on an adaptive strategy to avoid introducing the primal-dual variables and estimating the related exact penalty parameters. Additional, it is demonstrated that the state solution of the proposed algorithm achieves consensus and converges to an optimal solution of the optimization problem. Finally, numerical simulations are given and the proposed algorithm is applied to solve the optimal placement problem and energy consumption problem.     

Experimental validation of a continuous-time MCSI algorithm with bounded adaptive gains

Model reference adaptive control algorithms with minimal controller synthesis have proven to be an effective solution to tame the behaviour of linear systems subject to unknown or time-varying parameters, unmodelled dynamics and disturbances. However, a major drawback of the technique is that the adaptive control gains might exhibit an unbounded behaviour when facing bounded disturbances. Recently, a minimal controller synthesis algorithm with an integral part and either parameter projection or σ-modification strategies was proposed to guarantee boundedness of the adaptive gains. In this article, these controllers are experimentally validated for the first time by using an electro-mechanical system subject to significant rapidly varying disturbances and parametric uncertainty. Experimental results confirm the effectiveness of the modified minimal controller synthesis methods to keep the adaptive control gains bounded while providing, at the same time, tracking performances similar to that of the original algorithm.     

Online adaptive policy iteration based fault-tolerant control algorithm for continuous-time nonlinear tracking systems with actuator failures

In this paper, a novel tracking control scheme for continuous-time nonlinear affine systems with actuator faults is proposed by using a policy iteration (PI) based adaptive control algorithm. According to the controlled system and desired reference trajectory, a novel augmented tracking system is constructed and the tracking control problem is converted to the stabilizing issue of the corresponding error dynamic system. PI algorithm, generally used in optimal control and intelligence technique fields, is an important reinforcement learning method to solve the performance function by critic neural network (NN) approximation, which satisfies the Lyapunov equation. For the augmented tracking error system with actuator faults, an online PI based fault-tolerant control law is proposed, where a new tuning law of the adaptive parameter is designed to tolerate four common kinds of actuator faults. The stability of the tracking error dynamic with actuator faults is guaranteed by using Lyapunov theory, and the tracking errors satisfy uniformly bounded as the adaptive parameters get converged. Finally, the designed fault-tolerant feedback control algorithm for nonlinear tracking system with actuator faults is applied in two cases to track the desired reference trajectory, and the simulation results demonstrate the effectiveness and applicability of the proposed method.     

An efficient adaptive degree-based heuristic algorithm for influence maximization in hypergraphs

Influence maximization (IM) has shown wide applicability in immense fields over the past decades. Previous researches on IM mainly focused on the dyadic relationship but lacked the consideration of higher-order relationship between entities, which has been constantly revealed in many real systems. An adaptive degree-based heuristic algorithm, i.e., Hyper Adaptive Degree Pruning (HADP) which aims to iteratively select nodes with low influence overlap as seeds, is proposed in this work to tackle the IM problem in hypergraphs. Furthermore, we extend algorithms from ordinary networks as baselines. Results on 8 empirical hypergraphs show that HADP surpasses the baselines in terms of both effectiveness and efficiency with a maximally 46.02% improvement. Moreover, we test the effectiveness of our algorithm on synthetic hypergraphs generated by different degree heterogeneity. It shows that the improvement of our algorithm effectiveness increases from 2.66% to 14.67% with the increase of degree heterogeneity, which indicates that HADP shows high performance especially in hypergraphs with high heterogeneity, which is ubiquitous in real-world systems.     

An adaptive high-voltage direct current detection algorithm using cognitive wavelet transform

This paper proposes an algorithm that uses wavelet level adaptive decision-making for detecting high-voltage direct current (HVDC) discharge in wavelet transform cognitively. The identification and detection of HVDC discharge is an essential area of investigation, which contributes to ensuring pipeline safety and the optimal operation of an electrical power system. The proposed algorithm overcomes the wavelet packet transform’s disadvantage of needing to determine the level in advance. The decomposition level of wavelet packet transform is controlled by calculating relative wavelet energy change to decide its wavelet level. Our proposal extracts richer features of HVDC discharge by comparing other feature extraction algorithms. To select the best-suited mother wavelet function, we also design a selection method based on quantitative and qualitative approaches. An additional objective of this study is to detect the phenomenon of HVDC discharge using CP time-series data to assess the corrosion of energy pipelines. Moreover, a third primary discovery is that a wavelet-based application framework is designed to detect the HVDC discharge and further protect the energy pipeline. These discoveries can be valuably applied to the protection of power systems. They also provide brighter perspectives on future opportunities to expand on studies-to-date on the detection and classification of time-series data.     

Flatness-based adaptive sliding mode tracking control for a quadrotor with disturbances

In this paper, a flatness-based adaptive sliding mode control strategy is presented to solve the trajectory tracking problem of a quadrotor. According to the differential flatness theory, the typical under-actuated quadrotor dynamics is transformed into a fully-actuated one. Based on this model, backstepping sliding mode controllers are designed to solve the trajectory tracking problem. To improve the robustness to disturbances, extended state observers are applied as a feedforward compensation of disturbances. Moreover, considering the high-order dynamics and possible instability caused by large observer gains, the adaptive method is applied to compensate for the estimation error. The effectiveness of the proposed control scheme is verified in simulations.     

新型温湿度测控系统的设计与控制算法

本文着重介绍了新型温湿度测控系统的控制算法与硬、软件设计方法。该系统的硬件核心设计采用新型单片机AT89C55，并通过使用单片机接口电路的复用技术，使整个硬件系统更简单、可靠。系统中采用X25045作辅助芯片，从而实现了系统的自动化标定与校准。调节设备中的空气压缩机采用调频技术，实现了制冷量的PID调节。     

An adaptive online learning algorithm for distributed convex optimization with coupled constraints over unbalanced directed graphs

This paper investigates a distributed optimization problem over multi-agent networks subject to both local and coupled constraints in a non-stationary environment, where a set of agents aim to cooperatively minimize the sum of locally time-varying cost functions when the communication graphs are time-changing connected and unbalanced. Based on dual decomposition, we propose a distributed online dual push-sum learning algorithm by incorporating the push-sum protocol into dual gradient method. We then show that the regret bound has a sublinear growth of O(T^p) and the constraint violation is also sublinear with order of $O\left(T^{1?p/2}\right),$ where T is the time horizon and 0 < p ≤ 1/2. Finally, simulation experiments on a plug-in electric vehicle charging problem are utilized to verify the performance of the proposed algorithm. The proposed algorithm is adaptive without knowing the total number of iterations T in advance. The convergence results are established on more general unbalanced graphs without the boundedness assumption on dual variables. In addition, more privacy concerns are guaranteed since only dual variables related with coupled constraints are exchanged among agents.     

An intelligent self-repairing control for nonlinear MIMO systems via adaptive sliding mode control technology

In this paper, an intelligent self-repairing control scheme is proposed for a class of nonlinear MIMO system. A direct self-repairing controller of a nonlinear SISO system is firstly designed, and then the control scheme is promoted to a nonlinear MIMO system. The error signals are replaced by the state variables to deal with the high derivate problems of the desired signals and a nonlinear regulating function is brought in to improve the performances of the sliding mode. The self-repairing controller is made up of four parts: the nonlinear regulator, the equal controller, the compensator I and the compensator II. The control method is applied to a helicopter flight control system with loss-in-effectiveness faults. Some simulation results illustrate the effectiveness and feasibility of the proposed control scheme in the paper.     

An adaptive fast super-twisting disturbance observer-based dual closed-loop attitude control with fixed-time convergence for UAV

In this paper, a fixed-time dual closed-loop attitude control method is investigated for a quadrotor unmanned aerial vehicle. Firstly, a fixed-time adaptive fast super-twisting disturbance observer is presented for estimating the unknown external disturbance. A modified adaptive law is employed based on an equivalent control method to obtain proper observer gains. Secondly, a fixed-time controller is designed by using a universal barrier Lyapunov function to satisfy asymmetric tracking error constraints. Then, a tracking differentiator is utilised to arrange the transition process. Finally, the implementation of the developed method in a quadrotor unmanned aerial vehicle is performed. Through stability analysis and simulation results, the effectiveness and superiority of the proposed fixed-time control method are validated.     

Direct adaptive control for nonlinear systems using a TSK fuzzy echo state network based on fractional-order learning algorithm

This paper presents a new Takagi-Sugeno-Kang fuzzy Echo State Neural Network (TSKFESN) structure to design a direct adaptive control for uncertain SISO nonlinear systems. The proposed TSKFESN structure is based on the echo state neural network framework containing multiple sub-reservoirs. Each sub-reservoir is weighted with a TSK fuzzy rule. The adaptive law of the TSKFESN-based direct adaptive controller is derived by using a fractional-order sliding mode learning algorithm. Moreover, the Lyapunov stability criterion is employed to verify the convergence of the fractional-order adaptive law of the controller parameters. The evaluation of the proposed direct adaptive control scheme is verified using two case studies, the regulation problem of a torsional pendulum and the speed control of a direct current (DC) machine as a real-time application. The simulation and the experimental results show the effectiveness of the proposed control scheme.     

Leader-following consensus control of multiple nonholomomic mobile robots: An iterative learning adaptive control scheme

In this paper, the distributed iterative learning control for nonholonomic mobile robots with a time-varying reference is investigated, in which the mobile robots are with parametric uncertainties and are not fully actuated. Besides, the control gains of mobile robots are unknown. The leader is with a time-varying reference trajectory, and there is no need to assume that the time-varying reference is linearly parameterized by a set of known functions. A distributed control scheme is designed for each mobile robot based on a set of local compensatory filters designed by its neighborhood information. Stability analysis is established through a set of composite energy function. The uniform convergence of the consensus errors can be guaranteed. An example is given to show that our designed control law is effective.     

An explicit-time and explicit-accuracy control for a state-constrained nonstrict-feedback uncertain system based on adaptive fuzzy dynamic-approximation

This article proposes a novel explicit-time and explicit-accuracy adaptive fuzzy control for a state-constrained nonlinear nonstrict-feedback uncertain system. This method can explicitly parameterize the upper bound of settling-time with low initial control input under a bounded initial condition. Meanwhile, this method can also explicitly parameterize the upper bound of accuracy while achieving low control input based on the adaptive fuzzy dynamic-approximation theorem. Firstly, a novel generalized explicit-time stability system is proposed by introducing the boundary gain term to render the time-parameter explicit, this method can solve the input conservatism problem caused by the unbounded-state gain term of traditional fixed/prdefined-time function. Then, according to the universal fuzzy approximation theorem, the novel dynamic relationship of adaptive fuzzy logic system between approximation error and adaptive parameters is presented. This relationship can lead to the adaptive fuzzy dynamic-approximation theorem, and an adaptive law designed by this theorem can realize the Lyapunov stability of adaptive control system under a Lasalle invariant set. In the end, a novel adaptive fuzzy control scheme is proposed by the generalized explicit-time function and adaptive fuzzy dynamic-approximation theorem. This scheme can achieve the explicit-time stability by the human-like activation function, and the accuracy can be parameterized by Lyapunov synthesis. Compared with other existing fixed/prdefined-time adaptive fuzzy control methods, the proposed explicit-time and explicit-accuracy controller achieves a significant reduction in the initial control input. Theoretical analysis and simulation results validate the effectiveness of the proposed method.     

An equation of state for a system with a single type of transformation

Based on theory of a previous paper, the writer has developed an equation of state for a system with a single type of transformation. This equation is of the form

$\text{h=A+Bv+Cp+Dpv?T(E+Fv+Gp+Hpv)}$

where h = ε + pv is the total heat, p the pressure, v the specific volume, T the temperature, and p, v, T are considered independent variables. A, B, C, etc., are constants for the system. The latent eat at constant (p, T) is given by

$\text{λp,T=(v}{}_2\text{?v}{}_1\text{)(}\text{?h}\text{?v}\text{)P,T= (v}{}_2\text{?v}{}_1\text{)[(B?TF)+p(D?TH)]}$

. These equations are checked with data on saturated and superheated ammonia, and the agreement is good to within a few tenths of a per cent. Also, checks with data on saturated and superheated steam show agreement within several per cent.     

On the control of solutions of a viscoelastic equation

In this paper we consider the semilinear viscoelastic equation

     

An adaptive mesh method with variable relaxation time

Moving mesh partial differential equations have been widely used in the last decade for solving differential equations exhibiting large solution variations such as shock waves and boundary layers.In this paper, we have applied a dynamic adaptive method for solving time-dependent differential equations. The mesh velocities are governed by an equation in which a relaxation time is employed to move nodes in such a way that they remain concentrated in regions of rapid variation of the solution. A numerical example involving a blow-up problem shows the advantage of using a variable relaxation time over a fixed one.     

Design of a model-free adaptive sliding mode control to synchronize chaotic fractional-order systems with input saturation: An application in secure communications

In this work, a model-free adaptive sliding mode control (ASMC) methodology is proposed for synchronization of chaotic fractional-order systems (FOSs) with input saturation. Based on the frequency distributed model and the non-integer version of the Lyapunov stability theorem, a model-free ASMC method is designed to overcome the chaotic behavior of the FOSs. The control inputs are free from the nonlinear-linear dynamical terms of the system because of utilizing the boundedness feature of the states of chaotic FOSs. Moreover, a new medical image encryption scheme is tentatively proposed according to our synchronization method, and its effectiveness is verified by numerical simulations. Furthermore, the performance and security analyses are given to confirm the superiority of the proposed encryption scheme, including statistical analysis, key space analysis, differential attack analysis, and time performance analysis.     

Leader-following consensus control for semi-Markov jump multi-agent systems: An adaptive event-triggered scheme

The problem of event-triggered leader-following consensus control for semi-Markov multi-agent systems is investigated in this paper. A semi-Markov process is used to describe the sudden parameter changes between every agent. An adaptive event-triggered control strategy is proposed to make a balance between reducing unnecessary communication and meeting the required performance. A control protocol which can resist actuator faults is used to ensure the reliable leader-following consensus. By employing the Lyapunov–Krasovskii functional method, some sufficient conditions are provided to guarantee that the leader-following consensus can be achieved in mean-square sense. The consensus controller and the event-triggered parameter can be co-designed. Finally, the effectiveness of the proposed method is verified by a F-404 aircraft engine system.