Robust adaptive fuzzy control scheme for nonlinear system with uncertainty

In this paper, a robust adaptive fuzzy control scheme for a class of nonlinear system with uncertainty is proposed. First, using prior knowledge about the plant we obtain a fuzzy model, which is called the generalized fuzzy hyperbolic model （GFHM）. Secondly, for the case that the states of the system are not available an observer is designed and a robust adaptive fuzzy output feedback control scheme is developed. The overall control system guarantees that the tracking error converges to a small neighborhood of origin and that all signals involved are uniformly bounded. The main advantages of the proposed control scheme are that the human knowledge about the plant under control can be used to design the controller and only one parameter in the adaptive mechanism needs to be on-line adjusted.     

Output feedback adaptive DSC for nonlinear systems with guaranteed L-infinity tracking performance

In this paper, the problem of output tracking for a class of uncertain nonlinear systems is considered. First, neural networks are employed to cope with uncertain nonlinear functions, based on which state estimation is constructed. Then, an output feedback control system is designed by using dynamic surface control (DSC). To guarantee the L-infinity tracking performance, an initialization technique is presented. The main feature of the scheme is that explosion of complex- ity problem in backstepping control is avoided, and there is no need to update the unknown parameters including control gains as well as neural networks weights, the adaptive law with one update parameter is necessary only at the first design step. It is proved that all signals of the closed-loop system are semiglobally uniformly ultimately bounded and the L-infinity performance of system tracking error can be guaranteed. Simulation results demonstrate the effectiveness of the proposed scheme.     

Guaranteed transient performance based control with input saturation for near space vehicles

This paper describes the design of guaranteed transient performance based attitude control for the near space vehicle(NSV)with control input saturation using the backstepping method.To improve the robust controllability of the NSV,the parameter adaptive method is used to tackle the integrated effect of unknown time-varying disturbance and control input saturation.Based on the backstepping technique and parameter estimated outputs,a robust attitude control scheme is proposed for the NSV with input saturation.A novel robust attitude control scheme is then proposed based on a prescribed performance bound(PPB)which characterizes the convergence rate and maximum overshoot of the attitude tracking error.The closed-loop system stability under both the developed robust attitude control schemes is proved using Lyapunov’s method and uniformly asymptotical convergence of all closed-loop signals is guaranteed.Finally,simulation results are given to show the effectiveness of both the proposed robust constrained attitude control schemes.     

Robust dynamic surface control of flexible joint robots using recurrent neural networks

A robust neuro-adaptive controller for uncertain flexible joint robots is presented. This control scheme integrates H-infinity disturbance attenuation design and recurrent neural network adaptive control technique into the dynamic surface control framework. Two recurrent neural networks are used to adaptively learn the uncertain functions in a flexible joint robot. Then, the effects of approximation error and filter error on the tracking performance are attenuated to a prescribed level by the embedded H-infinity controller, so that the desired H-infinity tracking performance can be achieved. Finally, simulation results verify the effectiveness of the proposed control scheme.     

A simple and robust speed control scheme of permanent magnet synchronous motor

This paper presents a simple and robust speed control scheme of Permanent Magnet Synchronous Motor (PMSM). It is to achieve accurate control performance in the presence of load torque and plant parameter variation. A robust disturbance cancellation feed forward controller is used to estimate the torque disturbance. The simple and practical control scheme is easily implemented on a PMSM driver using a TMS320LF2407 DSP. The effectiveness of the proposed robust speed control approach is demonstrated by simulation and experhnental results.     

Dynamics and control of a novel 3-DOF parallel manipulator with actuation redundancy

This paper deals with the dynamics and control of a novel 3-degrees-of-freedom （DOF） parallel manipulator with actuation redundancy. According to the kinematics of the redundant manipulator, the inverse dynamic equation is formulated in the task space by using the Lagrangian formalism, and the driving force is optimized by utilizing the minimal 2-norm method. Based on the dynamic model, a synchronized sliding mode control scheme based on contour error is proposed to implement accurate motion tracking control. Additionally, an adaptive method is introduced to approximate the lumped uncertainty of the system and provide a chattering-free control. The simulation results indicate the effectiveness of the proposed approaches and demonstrate the satisfactory tracking performance compared to the conventional controller in the presence of the parameter uncertainties and un-modelled dynamics for the motion control of manipulators.     

Revisiting the benchmark problem of starting control of combustion engines

Starting of combustion engines is a typical transient operating mode that has significant influence to the engine performance. Due to the distinct variations in the pathes of air intake and fuel injection, the model of the engine system contains considerable uncertain parameters. To search effective control schemes that guarantee desired performance, engine starting control is proposed as a benchmark challenge problem. As a challenging result, a model-based control scheme is developed perviously. In this work, the benchmark problem is revisited and a modification for the fuel injection path control of the previous work is proposed by integrating a time sequence regressive based parameter tuning strategy. Validation by the benchmark problem simulator shows that although the new strategy has simple structure, similar control performance is obtained. Especially, the new strategy has potential extensibility with learning based methods to further improve the performance of the benchmark problem on engine starting control.     

Output feedback adaptive super twisting sliding mode control for quadrotor UAVs

In this paper, a sliding mode control with adaptive gain combined with a high-order sliding mode observer to solve the tracking problem for a quadrotor UAV is addressed, in presence of bounded external disturbances and parametric uncertainties. The high order sliding mode observer is designed for estimating the linear and angular speed in order to implement the proposed scheme. Furthermore, a Lyapunov function is introduced to design the controller with the adaptation law, whereas an analysis of finite time convergence towards to zero is provided, where sufficient conditions are obtained. Regarding previous works from literature, one important advantage of proposed strategy is that the gains of control are parameterized in terms of only one adaptive parameter, which reduces the control effort by avoiding gain overestimation. Numerical simulations for tracking control of the quadrotor are given to show the performance of proposed adaptive control–observer scheme.     

An adaptive control with optimal disturbances rejection

This paper provides a way to optimize the overall disturbances rejection performance of the adaptive control system in the presence of unknown external disturbances.Especially,the updatable non-empty admissible model set,which is consistent to the a priori knowledge of the plant parameter and the online measurements,is computed.With the overall system performance as the criteria,the nominal model is optimally chosen within the admissible model set.The optimal nominal model is subsequently used to synthesize the optimal closed-loop controller based on the 1 design methodology.Combining the above two aspects,an optimal adaptive control scheme is proposed.Because of the consistency of the identification criteria and control object,the adaptive control scheme proposed in this paper can achieve the overall optimal disturbances rejection performance,and the effect of the interplay between the identification and control of the adaptive system can be handled effectively.In addition,the computable optimal performance is also provided.     

Semitensor product based adaptive control for attitude tracking of spacecraft with unknown external disturbances

This paper investigates the attitude tracking and disturbance rejection problem of rigid spacecraft.Using a new matrix product,i.e.,the semitensor product of matrices,the parameter uncertainties in the inertia matrix is isolated. Both the parameter uncertainties and the external disturbance are handled by robust adaptive control method.By combining the semitensor product of matrices,backstepping control methodology,and adaptive control technique,a new adaptive control law is designed.Simulation result is also presented to demonstrate the proposed design method.     

A simple self-adaptive Differential Evolution algorithm with application on the ALSTOM gasifier

Differential Evolution (DE) has gathered a reputation for being a powerful yet simple global optimiser with continually outperforming many of the already existing stochastic and direct search global optimisation techniques. It is however well established that DE is particularly sensitive to its control parameters, most notably the mutation weighting factor F. This sensitivity is further studied here and a simple randomised self-adaptive scheme is proposed for the DE mutation weighting factor F. The performance of this algorithm is studied with the use of several benchmark problems and applied to a difficult control systems design case study.     

Hybrid whale optimization algorithm enhanced with Lévy flight and differential evolution for job shop scheduling problems

The job shop scheduling problem (JSSP) has been a hot issue in manufacturing. For the past few decades, scholars have been attracted to research JSSP and proposed many novel meta-heuristic algorithms to solve it. Whale optimization algorithm (WOA) is such a novel meta-heuristic algorithm and has been proven to be efficient in solving real-world optimization problems in the literature. This paper proposes a hybrid WOA enhanced with Lévy flight and differential evolution (WOA-LFDE) to solve JSSP. By changing the expression of Lévy flight and DE search strategy, Lévy flight enhances the abilities of global search and convergence of WOA in iteration, while DE algorithm improves the exploitation and local search capabilities of WOA and keeps the diversity of solutions to escape local optima. It is then applied to solve 88 JSSP benchmark instances and compared with other state-of-art algorithms. The experimental results and statistical analysis show that the proposed algorithm has superior performance over contesting algorithms.     

Modified firefly algorithm for multidimensional optimization in structural design problems

An enhanced nature-inspired metaheuristic optimization algorithm, called the modified firefly algorithm (MFA) is proposed for multidimensional structural design optimization. The MFA incorporates metaheuristic components, namely logistic and Gauss/mouse chaotic maps, adaptive inertia weight, and Lévy flight with a conventional firefly algorithm (FA) to improve its optimization capability. The proposed MFA has several advantages over its traditional FA counterpart. Logistic chaotic maps provide a diverse initial population. Gauss/mouse maps allow the tuning of the FA attractiveness parameter. The adaptive inertia weight controls the local exploitation and the global exploration of the search process. Lévy flight is used in the exploitation of the MFA. The proposed MFA was evaluated by comparing its performance in solving a series of benchmark functions with those of the FA and other well-known optimization algorithms. The efficacy of the MFA was then proven by its solutions to three multidimensional structural design optimization problems; MFA yielded the best solutions among the observed algorithms. Experimental results revealed that the proposed MFA is more efficient and effective than the compared algorithms. Therefore, the MFA serves as an alternative algorithm for solving multidimensional structural design optimization problems.     

An improved Differential Evolution algorithm using learning automata and population topologies

A new variant of Differential Evolution (DE), called ADE-Grid, is presented in this paper which adapts the mutation strategy, crossover rate (CR) and scale factor (F) during the run. In ADE-Grid, learning automata (LA), which are powerful decision making machines, are used to determine the proper value of the parameters CR and F, and the suitable strategy for the construction of a mutant vector for each individual, adaptively. The proposed automata based DE is able to maintain the diversity among the individuals and encourage them to move toward several promising areas of the search space as well as the best found position. Numerical experiments are conducted on a set of twenty four well-known benchmark functions and one real-world engineering problem. The performance comparison between ADE-Grid and other state-of-the-art DE variants indicates that ADE-Grid is a viable approach for optimization. The results also show that the proposed ADE-Grid improves the performance of DE in terms of both convergence speed and quality of final solution.     

Scalability of generalized adaptive differential evolution for large-scale continuous optimization

Differential evolution (DE) has become a very powerful tool for global continuous optimization problems. Parameter adaptations are the most commonly used techniques to improve its performance. The adoption of these techniques has assisted the success of many adaptive DE variants. However, most studies on these adaptive DEs are limited to some small-scale problems, e.g. with less than 100 decision variables, which may be quite small comparing to the requirements of real-world applications. The scalability performance of adaptive DE is still unclear. In this paper, based on the analyses of similarities and drawbacks of existing parameter adaptation schemes in DE, we propose a generalized parameter adaptation scheme. Applying the scheme to DE results in a new generalized adaptive DE (GaDE) algorithm. The scalability performance of GaDE is evaluated on 19 benchmark functions with problem scale from 50 to 1,000 decision variables. Based on the comparison with three other algorithms, GaDE is very competitive in both the performance and scalability aspects.     

基于Lévy分布的柔软自适应演化采样算法

已经有相关工作将演化思想引入采样算法中,并结合 Lévy 分布提出了自适应的采样算法。针对 Lévy 分布的参数设 置和“厚尾”特性的关系进行了研究,改进了基于 Lévy 分布的演化采样算法,通过设置该分布的参数 ? 值为 1.0,1.3,1.7, 2.0,分别对应四种转移概率分布,从而增加了生成的候选样本的多样性。理论分析和实验表明,改进算法在收敛速率和精度 上优于基于高斯分布,柯西分布,对称指数分布的演化采样算法和其他自适应的演化采样算法。     

Stabilisation of stochastic delayed systems with Lévy noise on networks via periodically intermittent control

ABSTRACT

This paper investigates the stabilisation of stochastic coupled systems with time-varying delays and Lévy noise on networks (SCSTLN) via periodically intermittent control. And here, internal delays, white noise and Lévy noise are considered in the networks. To ensure stability of SCSTLN with a periodically intermittent controller, several simple and useful criteria are obtained by establishing a new differential inequality and using a graph-theoretic approach. The intensity of control is closely related to the coupling strength and the perturbed intensity of white noise and Lévy noise. In particular, the stabilisation of stochastic coupled oscillators with time-varying delays and Lévy noise on a network as a practical application of our theoretical results is studied. Finally, a numerical example about oscillators network is carried out to show the validity and feasibility of our analytical results.     

Accelerating Differential Evolution Using an Adaptive Local Search

We propose a crossover-based adaptive local search (LS) operation for enhancing the performance of standard differential evolution (DE) algorithm. Incorporating LS heuristics is often very useful in designing an effective evolutionary algorithm for global optimization. However, determining a single LS length that can serve for a wide range of problems is a critical issue. We present a LS technique to solve this problem by adaptively adjusting the length of the search, using a hill-climbing heuristic. The emphasis of this paper is to demonstrate how this LS scheme can improve the performance of DE. Experimenting with a wide range of benchmark functions, we show that the proposed new version of DE, with the adaptive LS, performs better, or at least comparably, to classic DE algorithm. Performance comparisons with other LS heuristics and with some other well-known evolutionary algorithms from literature are also presented.     

基于均衡池和莱维飞行的饥饿游戏搜索算法

针对饥饿游戏搜索算法（hunger games search,HGS）存在收敛速度慢和易陷入局部最优等缺点,提出了一种基于均衡池和莱维飞行的饥饿游戏搜索算法（equilibrium Lévy hunger games search,ELHGS）。该算法首先利用tent映射产生更具多样性的初始种群;受到平衡优化器算法（EO）的启发,提出一种基于动态均衡池收敛的更新公式,其动态调整的更新策略使算法的全局搜索能力增强;为了进一步增强算法跳出局部最优的能力,在一定条件下对种群实施基于莱维飞行的变异操作。对23个基准函数进行仿真实验,结果显示与原始HGS算法相比,ELHGS求解精度更高、收敛更为迅速,在高维度多峰函数问题上效果最为显著。     

基于Lévy变异的微粒群算法

微粒群算法因其实现简单及优化效果较好而得到广泛应用,但也存在易早熟和局部收敛的缺点;结合Lévy飞行的特性,提出了一种新的带Lévy变异的微粒群算法,并对其收敛性进行分析,指出该算法依概率收敛于全局最优解.通过对8个标准测试函数的仿真实验,结果表明改进算法中的Lévy变异能够利用粒子的当前知识并增加群体的多样性,从而能够更有效地平衡局部搜索和全局搜索,使其具有更好的性能,最后对改进算法的各参数设置进行了探讨分析.