Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

Fuzzy traffic signal control with DNA evolutionary algorithm

In order to optimize the signal control system, this paper proposes a method to design an optimized fuzzy logic controller （FLC） with the DNA evolutionary algorithm. Inspired by the DNA molecular operation characteristics, the DNA evolutionary algorithm modifies the corresponding genetic operators. Compared with the traditional genetic algorithm （GA）, the DNA evolutionary algorithm can overcome weak local search capability and premature convergence. The parameters of membership functions are optimized by adopting the quaternary encoding method and performing corresponding DNA genetic operators. The relevant optimized parameters are combined with the FLC for single intersection traffic signal control. Simulation experiments shows the better performance of the FLC with the DNA evolutionary algorithm optimization. The experimental results demonstrate the efficiency of the nrotmsed method.

Adaptive Robust Control for a Class of Uncertain MIMO Non-affine Nonlinear Systems

In this paper, the adaptive robust tracking control scheme is proposed for a class of multi-input and multi-output (MIMO) non-affine systems with uncertain structure and parameters, unknown control direction and unknown external disturbance based on backstepping technique. The MIMO non-affine system is first transformed into a time-varying system with strict feedback structure using the mean value theorem, and then the bounded time-varying parameters are estimated by adaptive algorithms with projection. To handle the possible 'controller singularity' problem caused by unknown control direction, a Nussbaum function is employed, and the dynamic surface control (DSC) method is applied to solve the problem of 'explosion of complexity' in backstepping control. It is proved that the proposed control scheme can guarantee that all signals of the closed-loop system are bounded through Lyapunov stability theorem and decoupled backstepping method. Simulation results are presented to illustrate the effectiveness of the proposed control scheme. © 2014 Chinese Association of Automation.

Optimal setting and placement of FACTS devices using strength Pareto multi-objective evolutionary algorithm

This work proposes a novel approach for multi-type optimal placement of flexible AC transmission system(FACTS) devices so as to optimize multi-objective voltage stability problem. The current study discusses a way for locating and setting of thyristor controlled series capacitor(TCSC) and static var compensator(SVC) using the multi-objective optimization approach named strength pareto multi-objective evolutionary algorithm(SPMOEA). Maximization of the static voltage stability margin(SVSM) and minimizations of real power losses(RPL) and load voltage deviation(LVD) are taken as the goals or three objective functions, when optimally locating multi-type FACTS devices. The performance and effectiveness of the proposed approach has been validated by the simulation results of the IEEE 30-bus and IEEE 118-bus test systems. The proposed approach is compared with non-dominated sorting particle swarm optimization(NSPSO) algorithm. This comparison confirms the usefulness of the multi-objective proposed technique that makes it promising for determination of combinatorial problems of FACTS devices location and setting in large scale power systems.

Adaptive Robust Control for A Class of Nonlinear Systems with Unknown Uncertainties

The problem of robust stabilization for nonlinear systems with partially known uncertainties is considered in this paper. The required information about uncertainties in the system is merely that the uncertainties are bounded, but the upper bounds are incompletely known. This paper can be viewed as an extension of the work in reference [1]. To compensate the uncertainties, an adaptive robust controller based on Lyapunov method is proposed and the design algorithm is also suggested. Compared with some previous controllers which can only ensure ultimate uniform boundedness of the systems, the controller given in the paper can make sure that the obtained closed-loop system is asymptotically stable in the large. Simulations show that the method presented is available and effective.

Randomized Algorithms for Probabilistic Optimal Robust Performance Controller Design

Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.

Modeling and Robust Backstepping Sliding Mode Control with Adaptive RBFNN for a Novel Coaxial Eight-rotor UAV

This paper focuses on the robust attitude control of a novel coaxial eight-rotor unmanned aerial vehicles (UAV) which has higher drive capability as well as greater robustness against disturbances than quad-rotor UAV. The dynamical and kinematical model for the coaxial eight-rotor UAV is developed, which has never been proposed before. A robust backstepping sliding mode controller (BSMC) with adaptive radial basis function neural network (RBFNN) is proposed to control the attitude of the eightrotor UAV in the presence of model uncertainties and external disturbances. The combinative method of backstepping control and sliding mode control has improved robustness and simplified design procedure benefiting from the advantages of both controllers. The adaptive RBFNN as the uncertainty observer can effectively estimate the lumped uncertainties without the knowledge of their bounds for the eight-rotor UAV. Additionally, the adaptive learning algorithm, which can learn the parameters of RBFNN online and compensate the approximation error, is derived using Lyapunov stability theorem. And then the uniformly ultimate stability of the eight-rotor system is proved. Finally, simulation results demonstrate the validity of the proposed robust control method adopted in the novel coaxial eight-rotor UAV in the case of model uncertainties and external disturbances. © 2014 Chinese Association of Automation.

A Review of Control Algorithms for Autonomous Quadrotors

The quadrotor unmanned aerial vehicle is a great platform for control systems research as its nonlinear nature and under-actuated configuration make it ideal to synthesize and analyze control algorithms. After a brief explanation of the system, several algorithms have been analyzed including their advantages and disadvantages: PID, Linear Quadratic Regulator (LQR), Sliding mode, Backstepping, Feedback linearization, Adaptive, Robust, Optimal, L1, H∞, Fuzzy logic and Artificial neutral networks. The conclusion of this work is a proposal of hybrid systems to be considered as they combine advantages from more than one control philosophy.

CAutoCSD-Evolutionary Search and Optimisation Enabled Computer Automated Control System Design

This paper attempts to set a unified scene for various linear time-invariant (LTI) control system design schemes, by transforming the existing concept of “computer-aided control system design” (CACSD) to novel “computer-automated control system design” (CAutoCSD). The first step towards this goal is to accommodate, under practical constraints, various design objectives that are desirable in both time and frequency domains. Such performance-prioritised unification is aimed at relieving practising engineers from having to select a particular control scheme and from sacrificing certain performance goals resulting from pre-commitment to such schemes. With recent progress in evolutionary computing based extra-numeric, multi-criterion search and optimisation techniques, such unification of LTI control schemes becomes feasible, analytical and practical, and the resultant designs can be creative. The techniques developed are applied to, and illustrated by, three design problems. The unified approach automatically provides an integrator for zero-steady state error in velocity control of a DC motor, and meets multiple objectives in the design of an LTI controller for a non-minimum phase plant and offers a high-performance LTI controller network for a non-linear chemical process.

Probabilistic Robust Linear Parameter-varying Control of a Small Helicopter Using Iterative Scenario Approach

In this paper, we present an iterative scenario approach (ISA) to design robust controllers for complex linear parameter-varying (LPV) systems with uncertainties. The robust controller synthesis problem is transformed to a scenario design problem, with the scenarios generated by identically extracting random samples on both uncertainty parameters and scheduling parameters. An iterative scheme based on the maximum volume ellipsoid cutting-plane method is used to solve the problem. Heuristic logic based on relevance ratio ranking is used to prune the redundant constraints, and thus, to improve the numerical stability of the algorithm. And further, a batching technique is presented to remarkably enhance the computational efficiency. The proposed method is applied to design an output-feedback controller for a small helicopter. Multiple uncertain physical parameters are considered, and simulation studies show that the closed-loop performance is quite good in both aspects of model tracking and dynamic decoupling. For robust LPV control problems, the proposed method is more computationally efficient than the popular stochastic ellipsoid methods. © 2014 Chinese Association of Automation.

Intelligent vehicle lateral controller design based on genetic algorithmand T-S fuzzy-neural network

Non-linearity and parameter time-variety are inherent properties of lateral motions of a vehicle. How to effectively control intelligent vehicle (IV) lateral motions is a challenging task. Controller design can be regarded as a process of searching optimal structure from controller structure space and searching optimal parameters from parameter space. Based on this view, an intelligent vehicle lateral motions controller was designed. The controller structure was constructed by T-S fuzzy-neural network (FNN). Its parameters were searched and selected with genetic algorithm (GA). The simulation results indicate that the controller designed has strong robustness, high precision and good ride quality, and it can effectively resolve IV lateral motion non-linearity and time-variant parameters problem.

Optimal Joint Space Control of a Cable-Driven Aerial Manipulator

This article proposes a novel method for maintaining the trajectory of an aerial manipulator by utilizing a fast nonsingular terminal sliding mode(FNTSM)manifold and a linear extended state observer(LESO).The developed controlmethod applies an FNTSMto ensure the tracking performance’s control accuracy,and an LESO to estimate the system’s unmodeled dynamics and external disturbances.Additionally,an improved salp swarm algorithm(ISSA)is employed to parameter tune the suggested controller by integrating the salp swarmtechnique with a cloud model.This approach also uses a model-free scheme to reduce the complexity of controller design without relying on complex and precise dynamics models.The simulation results show that the proposed controller outperforms linear active rejection disturbance control and PID controllers in terms of transient performance and resilience against lumped disturbances,and the ISSA can help the proposed controller find optimal control parameters.

Augmented Lagrangian Genetic Algorithm Based Decentralized Control Configuration Design for Fluid Catalytic Cracking Units

In this work, three decentralized control configuration designs—independent, sequential and simultaneous designs—were used in multivariable feedback configurations for PI control of the riser and regenerator temperatures of FCCU in order to compare their performances. Control design was formulated as optimization problem to minimize infinity norm of weighted sensitivity functions subject to μ-interaction measure bound on diagonal complementary functions of the closed loop system. The optimization problem was solved using augmented Lagrangian genetic algorithm. Simulation results show that simultaneous and independent designs give good response with less overshoot and with no oscillation. Bound on μ-interaction measure is satisfied for both designs meaning that their nominal stabilities are guaranteed;however, it is marginal for simultaneous design. Simultaneous design outperforms independent design in term of robust performance while independent design gives the best performance in terms of robust stability. Sequential design gives the worst performance out of the three designs.

INNOVATIVE PREDATORY SEARCH ALGORITHM FOR AIRCRAFT ARRIVAL SEQUENCING AND SCHEDULING PROBLEMS

To solve aircraft arrival sequencing and scheduling problems,and improve the typical predatory search algorithm（PSA）,an innovative PSA is developed.The new PSA uses variable constraints of local search and global search to avoid falling into local optimal solutions and the degeneration of solutions.To test the performance of new PSA,a case study with ten arriving flights and two runways is performed.Test results show that the new PSA performs much better than typical PSA and genetic algorithm（GA）in the aspects of the rate of gaining optimal solutions and the computational time.

平面并联机器人离线PID控制优化研究

为了使平面并联机器人在闭环控制系统中具有更稳定的控制性能。对此,这里构建了平面五杆并联机器人动力学模型及其控制系统模型,给出了控制系统的增益矢量p。对机器人系统模型进行离线PID控制优化,通过控制增益来设计控制系统中的增益矢量p,从而实现非线性单目标动态优化(NLMODOP)。在NLMODOP中加入动态约束,采用带约束处理机制的差分进化(DE)算法求解平面并联机器人的非线性规划问题,进而处理不稳定的动态优化。对机器人模型中的五个连杆进行仿真实验,并对有无DE算法控制的仿真结果进行了比较。结果表明:相比于无DE算法,采用DE算法下的机器人系统模型的连杆跟踪位移基本无跟踪误差。说明基于差分进化算法的平面并联机器人离线PID控制优化具有较好的控制精度和跟踪性能。

金属弧焊机器人加工轨迹智能规划研究

针对金属弧焊机器人加工轨迹运动规划精度低的问题,提出一种基于改进梯形速度规划算法的弧焊加工轨迹智能规划方法。该方法首先根据D-H法构建机器人的运动学模型,根据拉格朗日法构建机器人的动力学模型;然后应用加速度规划改进后的梯形速度规划算法进行运动规划;最后基于稳健控制的运动修正方法对初步运动规划结果进行修正。仿真实验结果表明:相比于梯形速度规划算法,改进的梯形速度规划算法具有更高的运动规划精度;基于稳健控制的运动修正方法的加入,使运动规划精度、抗干扰性都得到提升,且提高了机器人关节的使用寿命;该方法具有较好的弧焊加工轨迹智能规划性能,基本满足研究需求。

代理模型辅助的复杂网络能控性鲁棒性优化方法

近年来,复杂网络的鲁棒性优化问题引起人们广泛关注.复杂网络暴露在外会受到各种各样的攻击,因此如何设计抗击能力较好的网络结构成为了研究热点.虽然现有的方法在小规模复杂网络的鲁棒性方面已经取得了显著成果,但大规模复杂网络的能控性鲁棒性优化的计算成本非常大.而代理模型可以以较低的计算成本来代替优化过程中对复杂网络能控性鲁棒性的评估,但一个代理模型不可能适用于评估所有类型的复杂网络能控性鲁棒性.文中将Dempster-Shafer理论应用于代理模型选择及其混合,并把选择出的代理模型用来辅助进化算法搜索能控性鲁棒性更优的网络结构.此方法在SF、ER、SW、RR、RT和QS 6种合成网络上的实验结果表明:在不同类型的复杂网络中选择合适的代理模型能更好的辅助进化算法找到能控性鲁棒性更优的网络结构.

Boost型PFC变换器的自适应超螺旋全局滑模控制

针对Boost型功率因数校正(PFC)变换器,提出一种自适应超螺旋全局滑模控制(ASTTSMC)方法。该方法可以保证PFC变换器在理想工作条件下具备良好的性能,并且在面对负载突变、电网电压波动和电感参数变化等系统不确定性情况下时仍然能够保持全局鲁棒性。在控制律的设计中,采用全局滑模控制作为基准模型,从而确保系统在整个响应过程都具有鲁棒性;同时,切换项采用自适应超螺旋算法,考虑系统不确定项,通过自适应超螺旋算法中的切换增益参数来改善系统潜在的“抖振”现象;此外,基于李雅普诺夫稳定定理证明了所设计控制系统的稳定性。实验结果表明,在理想工作条件下和系统存在不确定性的情况下,该方法都能够有效改善网测电流的质量和提高变换器的功率因数,验证了该方法的有效性和所设计控制器的鲁棒性。

Real-time Feed-forward Force Compensation for Active Magnetic Bearings System Based on H∞ Controller

There are two kinds of unbalance vibrations—force vibration and displacement vibration due to the existence of unbalance excitation in active magnetic bearings（AMB） system. And two unbalance compensation methods—closed-loop feedback and open loop feed-forward are presented to reduce the force vibration. The transfer function order of the control system directly influencing the system stability will be increased when the closed-loop method is adopted, which makes the real-time compensation not easily achieved. While the open loop method would not increase the primary transfer function order, it provides conditions for real-time compensation. But the real-time compensation signals are not easy to be obtained in the open loop method. To implement real-time force compensation, a new method is proposed to reduce the force vibration caused by the rotor unbalance on the basis of AMB active control. The method realizes real-time and on-line force auto-compensation based on H∞ controller and one novel feed-forward compensation controller, which makes the rotor rotate around its inertia axis. The time-variable feed-forward compensatory signal is provided by a modified adaptive variable step-size least mean square（VSLMS） algorithm. And the relevant least mean square（LMS） algorithm parameters are used to solve the H∞ controller weighting functions. The simulation of the new method to compensate some frequency-variable and sinusoidal signals is completed by MATLAB programming, and real-time compensation is implemented in the actual AMB experimental system. The simulation and experiment results show that the compensation scheme can improve the robust stability and the anti-interference ability of the whole AMB system by using H∞ controller to achieve close-loop control, and then real-time force unbalance compensation is implemented. The proposed research provides a new control strategy containing real-time algorithm and H∞ controller for the force compensation of AMB system. And the stability of the control system is finally improved.