Decoupled adaptive neuro-fuzzy (DANF) sliding mode control system for a Lorenz chaotic problem

This paper introduces a decoupled adaptive neuro-fuzzy (DANF) sliding mode control system for the chaos control problem in a system without precise system model information. It has on-line learning ability to deal with the parametric uncertainty and disturbance by adjusting the control parameters and no constrained conditions and prior knowledge of the controlled plant is required in the design process. Also, a decoupled adaptive sliding mode controller is developed to control the chaotic Lorenz system for comparison. Finally, the effectiveness of the proposed decoupled adaptive sliding mode and DANF sliding mode controllers are demonstrated by some simulated results.     

Derivation of a parameter stabilizing training criterion for adaptive neuro-fuzzy inference systems in motion control

This paper presents a novel training algorithm for adaptive neuro-fuzzy inference systems. The algorithm combines the error back-propagation algorithm with the variable structure systems approach. Expressing the parameter update rule as a dynamic system in continuous time and applying sliding mode control (SMC) methodology to the dynamic model of the gradient based training procedure results in the parameter stabilizing part of training algorithm. The proposed combination therefore exhibits a degree of robustness to the unmodelled multivariable internal dynamics of the gradient-based training algorithm. With conventional training procedures, the excitation of this dynamics during a training cycle can lead to instability, which may be difficult to alleviate owing to the multidimensionality of the solution space and the ambiguities concerning the environmental conditions. This paper shows that a neuro-fuzzy model can be trained such that the adjustable parameter values are forced to settle down (parameter stabilization) while minimizing an appropriate cost function (cost optimization), which is based on state tracking performance. In the application example, trajectory control of a two degrees of freedom direct drive robotic manipulator is considered. As the controller, an adaptive neuro-fuzzy inference mechanism is used and, in the parameter tuning, the proposed algorithm is utilized.     

Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm

A neuro-fuzzy adaptive control approach for nonlinear dynamical systems, coupled with unknown dynamics, modeling errors, and various sorts of disturbances, is proposed and used to design a wheel slip regulating controller. The implemented control structure consists of a conventional controller and a neuro-fuzzy network-based feedback controller. The former is provided both to guarantee global asymptotic stability in compact space and as an inverse reference model of the response of the controlled system. Its output is used as an error signal by an incremental learning algorithm to update the parameters of the neuro-fuzzy controller. In this way the latter is able to gradually replace the conventional controller from the control of the system. The proposed new learning algorithm makes direct use of the variable structure systems theory and establishes a sliding motion in terms of the neuro-fuzzy controller parameters, leading the learning error toward zero. In the simulations and in the experimental studies, it has been tested on the control of antilock breaking system model and the analytical claims have been justified under the existence of uncertainty and large nonzero initial errors.     

基于AFSMC算法的飞机飞行姿态自适应滑模控制系统

传统飞机飞行姿态滑膜控制系统,存在飞机飞行姿态自适应系数稳定性差的问题,在控制过程中会受到多重因素影响,导致飞行姿态可控误差系数增大,需要辅助控制系统修正才能完成飞行姿态的控制操作;针对上述问题,提出基于AFSMC算法的飞机飞行姿态自适应滑模控制系统;系统硬件基于PID自适应滑模控制器,对飞机飞行姿态控制器进行结构设计;软件部分通过引入自适应滑模控制策略,对PID控制器姿态控制变量进行适配;引入AFSMC算法计算姿态控制器当前时间点下的运动控制方程,得到飞行姿态自适应滑模控制的最优量,完成基于AFSMC算法的飞机飞行姿态自适应滑模控制系统设计;实验结果表明,所设计系统能够在不同飞行工况下,对飞机飞行姿态作出准确控制,系统的整体控制精度范围为90%~97.4%,飞机飞行控制稳定性较好,有效提升了系统对飞机飞行姿态的控制准确度。     

GA-based modified adaptive fuzzy sliding mode controller for nonlinear systems

In this paper, the stability analysis of the GA-based adaptive fuzzy sliding model controller for a nonlinear system is presented. First, an uncertain and nonlinear plant for the tracking of a reference trajectory is well approximated and described via the reference model and the fuzzy model involving fuzzy logic control rules. Next, the difficulty in designing a fuzzy sliding mode controller (FSMC) capable of rapidly and efficiently controlling complex and nonlinear systems is how to select the most appropriate initial values for the parameter vector. The initial values of the consequent parameter vector are decided via the genetic algorithm. After this, a modified adaptive law can be adopted to find the best high-performance parameters for the fuzzy sliding model controller. The adaptive fuzzy sliding model controller is derived to simultaneously stabilize and control the system. The stability of the nonlinear system is ensured by the derivation of the stability criterion based upon Lyapunov’s direct method. Finally, a numerical simulation is provided as an example to demonstrate the control methodology.     

On-line genetic algorithm-based fuzzy-neural sliding mode controller using improved adaptive bound reduced-form genetic algorithm

In this article, a novel on-line genetic algorithm-based fuzzy-neural sliding mode controller trained by an improved adaptive bound reduced-form genetic algorithm is developed to guarantee robust stability and good tracking performance for a robot manipulator with uncertainties and external disturbances. A general sliding manifold, which can be non-linear or time varying, is used to construct a sliding surface and reduce control law chattering. In this article, the sliding surface is used to derive a genetic algorithm-based fuzzy-neural sliding mode controller. To identify structured system dynamics, a B-spline membership function fuzzy-neural network, which is trained by the improved genetic algorithm, is used to approximate the regressor of the robot manipulator. The sliding mode control with a general sliding surface plays the role of a compensator when the fuzzy-neural network does not approximate the dynamics regressor of the robot manipulator well in the transient period. The adjustable parameters of the fuzzy-neural network are tuned by the improved genetic algorithm, which, with the use of the sequential-search-based crossover point method and the single gene crossover, converges quickly to near-optimal parameter values. Simulation results show that the proposed genetic algorithm-based fuzzy-neural sliding mode controller is effective and yields superior tracking performance for robot manipulators.     

Robust adaptive motion control for underwater remotely operated vehicles with velocity constraints

In this paper, robust adaptive control strategies are designed for Underwater Remotely Operated Vehicles (ROVs) with velocity constraints. First, robust control strategies are investigated for under-water ROVs, and then adaptive robust control strategies are further developed with online parameter estimation. To prevent the velocity constraint violation, the Barrier Lyapunov Function (BLF) is employed in Lyapunov synthesis. By ensuring the boundedness of the BLF, we also guarantee that the velocity constraints are not transgressed. The stability analysis of the closed-loop system is provided and all closed-loop signals are ensured to be bounded. Simulation results for 5 degree-of-freedom (DOF) underwater ROV demonstrate the effectiveness of the proposed approach.     

无刷直流电机的自适应模糊滑模控制策略研究

为了提高无刷直流电机（BLDCM,brushless dC motor）控制系统的动态响应速度和干扰抑制能力,提出了一种新的自适应模糊滑模控制（AFSMC,adaptive fuzzy sliding mode control）策略。控制系统根据滑模开关函数的取值范围,可以切换滑模控制器的输出,能够改进滑模观测器的抖振现象和系统稳定性。控制器的控制律由自适应模糊控制算法调节,滑模控制器的输出减少了系统不确定时延的影响。根据所提出控制策略建立了仿真模型,并进行了仿真。仿真结果表明,所提出的控制策略能提高系统的动态性能和鲁棒性。该方法用于无刷直流电机的控制是可行的、有效的。     

采用遗传优化自适应模糊PID控制球杆系统

为解决球杆系统动态、静态性能不高的问题,提出了遗传算法优化自适应模糊PID控制器的控制方法.该模型在拉格朗日方程建立球杆系统数学模型的基础上,采用遗传算法优化模糊控制规则、隶属函数和自适应PID参数.在GBB1004系统中建立了遗传算法优化后的自适应模糊PID控制器以及控制模型,并对该控制器进行实验验证.实验结果证明了遗传算法优化后的模糊控制器有效地减小了系统的超调量,缩短了系统的调节时间,能够较好地控制球杆系统.     

集成神经网络与自适应算法的分数阶滑模控制

针对被控对象的参数时变和外部扰动问题,本文融合神经网络的万能逼近能力和自适应控制技术,并结合分数阶微积分理论,提出了基于神经网络和自适应控制算法的分数阶滑模控制策略.本文采用等效控制的方法设计滑模控制律,并利用神经网络的万能逼近能力估测控制律的变化,结合自适应控制算法和分数阶微积分理论抑制传统滑模控制系统的抖震,同时根据Lyapunov稳定性理论分析了系统的稳定性,最后给出了实验结果.实验结果表明,本文提出的基于神经网络和自适应控制算法的分数阶滑模控制系统,能保持滑模控制器对系统外部扰动和参数变化鲁棒性的同时,也能有效地抑制抖震,使得系统获得较高的控制性能.     

Analysis and design of single-phase power factor corrector with genetic algorithm and adaptive neuro-fuzzy-based sliding mode controller using DC–DC SEPIC

This paper proposes a methodology for single-phase power factor correction with DC–DC single-ended primary inductance converter (SEPIC) using cascade control strategy which comprises of genetic algorithm-based outer PI controller and an inner current controller which uses an adaptive neuro-fuzzy inference system-based sliding mode controller. DC–DC SEPIC is a fourth-order converter, and in order to reduce the complexity in controller design, reduced-order model of the original higher-order system is obtained by using Type-I Hankel matrix method. The performance of the proposed system is analysed using MATLAB/Simulink-based simulation studies. In order to ensure the robustness of the proposed controller, the performance parameters such as percentage total harmonic distortion, power factor, % voltage regulation, and % efficiency are analysed. From the simulation results, it is inferred that the proposed method provides efficient tracking of output voltage and effective source current shaping for load, line, and set point variations.

     

Nonsingular Terminal Sliding Mode Control of Robot Manipulators Using Fuzzy Wavelet Networks

This paper presents an adaptive nonsingular terminal sliding mode (NTSM) tracking control design for robotic systems using fuzzy wavelet networks. Compared with linear hyperplane-based sliding control, terminal sliding mode controller can provide faster convergence and higher precision control. Therefore, a terminal sliding controller combined with the fuzzy wavelet network, which can accurately approximate unknown dynamics of robotic systems by using an adaptive learning algorithm, is an attractive control approach for robots. In addition, the proposed learning algorithm can on-line tune parameters of dilation and translation of fuzzy wavelet basis functions and hidden-to-output weights. Therefore, a robust control law is used to eliminate uncertainties including the inevitable approximation errors resulted from the finite number of fuzzy wavelet basis functions. The proposed controller requires no prior knowledge about the dynamics of the robot and no off-line learning phase. Moreover, both tracking performance and stability of the closed-loop robotic system can be guaranteed by Lyapunov theory. Finally, the effectiveness of the fuzzy wavelet network-based control approach is illustrated through comparative simulations on a six-link robot manipulator     

Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties

This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input multi-output nonlinear system. The control strategy is an inverse nonlinear controller combined with an adaptive neural network with sliding mode control using an on-line learning algorithm. The adaptive neural network with sliding mode control acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations in its entire structure (kinematics and dynamics). The controllers are obtained by using Lyapunov's stability theory. Experimental results of a case study show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.     

An optimal adaptive robust PID controller subject to fuzzy rules and sliding modes for MIMO uncertain chaotic systems

In this paper, an optimal adaptive robust PID controller based on fuzzy rules and sliding modes is introduced to present a general scheme to control MIMO uncertain chaotic nonlinear systems. In this control scheme, the gains of the PID controller are updated by using an adaptive mechanism, fuzzy rules, the gradient search method, and the chain rule of differentiation in order to minimize the sliding surfaces of sliding mode control. More precisely, sliding mode control is used as a supervisory controller to provide sufficient control inputs and guarantee the stability of the control approach. To ascertain the parameters of the proposed controller and avoid trial and error, the multi-objective genetic algorithm is employed to augment the performance of proposed controller. The chaotic system of a Duffing-Holmes oscillator and an industrial robotic manipulator are the case studies to evaluate the performance of the proposed control approach. The obtained results of this study regarding both systems are compared with the outcomes of two notable studies in the literature. The results and analysis prove the efficiency of the proposed controller with regard to MIMO uncertain systems having challenging external disturbances in terms of stability, minimum tracking error and optimal control inputs.     

Aero‐Engine DCS Fault‐Tolerant Control with Markov Time Delay Based On Augmented Adaptive Sliding Mode Observer

With a focus on aero‐engine distributed control systems (DCSs) with Markov time delay, unknown input disturbance, and sensor and actuator simultaneous faults, a combined fault tolerant algorithm based on the adaptive sliding mode observer is studied. First, an uncertain augmented model of distributed control system is established under the condition of simultaneous sensor and actuator faults, which also considers the influence of the output disturbances. Second, an augmented adaptive sliding mode observer is designed and the linear matrix inequality (LMI) form stability condition of the combined closed‐loop system is deduced. Third, a robust sliding mode fault tolerant controller is designed based on fault estimation of the sliding mode observer, where the theory of predictive control is adopted to suppress the influence of random time delay on system stability. Simulation results indicate that the proposed sliding mode fault tolerant controller can be very effective despite the existence of faults and output disturbances, and is suitable for the simultaneous sensor and actuator faults condition.     

基于滑模变结构控制的RBF神经元网络

针对高精度飞行仿真转台，设计一种基于滑模变结构控制的RBF神经元网络控制器。该控制器根据滑模变结构控制器的特点，将控制律分为等效控制律和到达控制律。等效控制律使系统运动于滑模面附近，由RBFN拟合而成，权值用自适应算法在线修正，确保了实时控制的可能性；到达控制律可使处于状态空间内任意初始位置的系统趋近于滑模面，由滑模控制器的可达性条件推出，其中用到了系统的不确定性参数的上下界。计算机仿真结果表明了该方法的鲁棒性和实际应用的可能性。     

Tuning of a neuro-fuzzy controller by genetic algorithm

Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with Gaussian membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance.     

Direct adaptive neural control of nonlinear systems with extreme learning machine

A direct adaptive neural control scheme for a class of nonlinear systems is presented in the paper. The proposed control scheme incorporates a neural controller and a sliding mode controller. The neural controller is constructed based on the approximation capability of the single-hidden layer feedforward network (SLFN). The sliding mode controller is built to compensate for the modeling error of SLFN and system uncertainties. In the designed neural controller, its hidden node parameters are modified using the recently proposed neural algorithm named extreme learning machine (ELM), where they are assigned random values. However, different from the original ELM algorithm, the output weight is updated based on the Lyapunov synthesis approach to guarantee the stability of the overall control system. The proposed adaptive neural controller is finally applied to control the inverted pendulum system with two different reference trajectories. The simulation results demonstrate good tracking performance of the proposed control scheme.     

基于GSO-BFA的采煤机双滚筒同步调高控制

为研究采煤机滚筒在煤岩混合复杂工况下的调高连续性,依据液压机构的工作原理建立了滚筒调高机构的动力学模型.基于自适应模糊微分积分滑模(AFDI-SMC)鲁棒性强的优点,采用了萤火虫-细菌觅食(GSO-BFA)算法优化滑模控制器参数条件下的滚筒调高控制方案.为保证双滚筒工作时举升高度的一致性,引入偏差-环形耦合同步控制策略补偿位置偏差,同时采用融合算法(GSO-BFA)对外闭环(滚筒高度-电压)控制器参数进行优化,并分析了双滚筒同步调高性能,且与采用遗传算法(GA)优化的系统同步调高精度相比较.仿真结果表明,采用融合算法优化且结合自适应模糊微分积分滑模的滚筒同步调高系统具有良好的鲁棒性及较高的同步精度.     

Time-varying sliding mode control for a class of uncertain MIMO nonlinear system subject to control input constraint

To solve the regulator problem of a class of uncertain MIMO nonlinear systems subject to control input constraint, three types of time-varying sliding mode control laws are proposed. The sliding surfaces pass the initial value of the system at the initial time, and are shifted/rotated towards the predetermined ones. The controller parameters are optimized by genetic algorithm (GA). Lyapunov method is adopted to prove the stability and robustness to the parameter uncertainties and external disturbance. By me...