Novel ARC-Fuzzy Coordinated Automatic Tracking Control of Four-Wheeled Mobile Robot

Four-wheeled,individual-driven,nonholonomic structured mobile robots are widely used in industries for automated work,inspection and explora-tion purposes.The trajectory tracking control of the four-wheel individual-driven mobile robot is one of the most blooming research topics due to its nonholonomic structure.The wheel velocities are separately adjusted to follow the trajectory in the old-fashioned kinematic control of skid-steered mobile robots.However,there is no consideration for robot dynamics when using a kinematic controller that solely addresses the robot chassis’s motion.As a result,the mobile robot has lim-ited performance,such as chattering during curved movement.In this research work,a three-tiered adaptive robust control with fuzzy parameter estimation,including dynamic modeling,direct torque control and wheel slip control is pro-posed.Fuzzy logic-based parameter estimation is a valuable tool for adjusting adaptive robust controller(ARC)parameters and tracking the trajectories with less tracking error as well as high tracking accuracy.This research considers the O type and 8 type trajectories for performance analysis of the proposed novel control technique.Our suggested approach outperforms the existing control methods such as Fuzzy,proportional–integral–derivative(PID)and adaptive robust controller with discrete projection(ARC–DP).The experimental results show that the scheduled performance index decreases by 2.77%and 4.76%.All the experimen-tal simulations obviously proved that the proposed ARC-Fuzzy performed well in smooth groud surfaces compared to other approaches.

Fuzzy sliding mode control guidance law with terminal impact angle and acceleration constraints

In this paper, a novel fuzzy sliding mode control（FSMC） guidance law with terminal constraints of miss distance, impact angle and acceleration is presented for a constant speed missile against the stationary or slowly moving target. The proposed guidance law combines the sliding mode control algorithm with a fuzzy logic control scheme for the lag-free system and the first-order lag system. Through using Lyapunov stability theory, we prove the sliding surface converges to zero in finite time. Furthermore, considering the uncertain information and system disturbances, the guidance gains are on-line optimized by fuzzy logic technique. Numerical simulations are performed to demonstrate the performance of the FSMC guidance law and the results illustrate the validity and effectiveness of the proposed guidance law.

Adaptive fuzzy sliding mode control for robotic airship with model uncertainty and external disturbance

An adaptive fuzzy sliding mode control （AFSMC） ap- proach is proposed for a robotic airship. First, the mathematical model of an airship is derived in the form of a nonlinear control system. Second, an AFSMC approach is proposed to design the attitude control system of airship, and the global stability of the closed-loop system is proved by using the Lyapunov stability theorem. Finally, simulation results verify the effectiveness and robustness of the proposed control approach in the presence of model uncertainties and external disturbances.

An Improved Fuzzy Predictive Control Algorithm and Its Application to an Industrial CSTR Process

A finite horizon predictive control algorithm, which applies a saturated feedback control law as its local control law, is presented for nonlinear systems with time-delay subject to input constraints. In the algorithm, N free control moves, a saturated local control law and the terminal weighting matrices are solved by a minimization problem based on linear matrix inequality （LMI） constraints online. Compared with the algorithm with a nonsaturated local law, the presented algorithm improves the performances of the closed-loop systems such as feasibility and optimality. This model predictive control （MPC） algorithm is applied to an industrial continuous stirred tank reactor （CSTR） with explicit input constraint. The simulation results demonstrate that the presented algorithm is effective.

Fuzzy Shape Control Based on El man Dynamic Recursion Network Prediction Model

In the strip rolling process, shape control system possesses the characteristics of nonlinearity, strong coupling, time delay and time variation. Based on self adapting Elman dynamic recursion network prediction model, the fuzzy control method was used to control the shape on four-high cold mill. The simulation results showed that the system can be applied to real time on line control of the shape.

An Adaptive Fuzzy Control Model for Multi-Joint Manipulators

Multi-joint manipulator systems are subject to nonlinear influences such as frictional characteristics,random disturbances and load variations.To account for uncertain disturbances in the operation of manipulators,we propose an adaptive manipulator control method based on a multi-joint fuzzy system,in which the upper bound information of the fuzzy system is constant and the state variables of the manipulator control system are measurable.The control algorithm of the system is a MIMO(multi-input-multi-output)fuzzy system that can approximate system error by using a robust adaptive control law to eliminate the shadow caused by approximation error.It can ensure the stability of complex manipulator control systems and reduce the number of fuzzy rules required.Comparison of experimental and simulation data shows that the controller designed using this algorithm has highly-precise trajectory-tracking control and can control robotic systems with complex characteristics of non-linearity,coupling and uncertainty.Therefore,the proposed algorithm has good practical application prospects and promotes the development of complex control systems.

Improved control strategy for PMSM based on fuzzy sliding mode control and sliding-mode observer

Aimed at the problems of large torque ripple,obvious chattering and poor estimation accuracy of back-EMFs in traditional permanent magnet synchronous motor(PMSM)control system with sliding mode observer(SMO),an improved control strategy for PMSM based on a fuzzy sliding mode control(FSMC)and a two-stage filter sliding mode observer(TFSMO)is proposed.Firstly,a novel reaching law(NRL)used in the speed loop based on hyperbolic sine function is studied,and fuzzy control ideal is shown to achieve the self-turning of the parameter for the reaching law,thus a fuzzy integral sliding mode controller based on the novel reaching law is designed in speed loop.Then the suppression effect upon chattering caused by the novel reaching law is analyzed strictly by discrete equation.Secondly,in order to restrain the high frequency components and measurement noise in back-EMFs,a two-stage filter structure based on a variable cut-off frequency low-pass filter(VCF-LPF)and a modified back-EMF observer(MBO)is conceived,and the rotor position is compensated reasonably.As a result,a TFSMO is designed.The stability of the proposed control strategy is proved by Lyapunov Criterion.The simulation and experiment results show that,compared with traditional SMO,the controller suggested above can obtain very nice system respond when the motor starts and is subjected to external disturbances,and effectively improve the problems about torque ripple,chattering and the estimation accuracy of back-EMF.

An Adaptive Fuzzy Sliding Mode Control Scheme for Robotic Systems

In this article, an adaptive fuzzy sliding mode control (AFSMC) scheme is derived for robotic systems. In the AFSMC design, the sliding mode control (SMC) concept is combined with fuzzy control strategy to obtain a model-free fuzzy sliding mode control. The equivalent controller has been substituted for by a fuzzy system and the uncertainties are estimated on-line. The approach of the AFSMC has the learning ability to generate the fuzzy control actions and adaptively compensates for the uncertainties. Despite the high nonlinearity and coupling effects, the control input of the proposed control algorithm has been decoupled leading to a simplified control mechanism for robotic systems. Simulations have been carried out on a two link planar robot. Results show the effectiveness of the proposed control system.

Stabilization of CSTR w ith Self-tuning Sliding Mode Controller Using T-S Fuzzy Linearization

A self-tuning reaching law based sliding mode control(SMC)theory is proposed to stabilize the nonlinear continuous stirred tank reactor(CSTR).T-S fuzzy logic is used to build a global fuzzy state-space linear model.Combing the traits of SMC and CSTR,three fuzzy rules can meet the requirements of controlled system.The self-tuning switch control law which can drive the state variables to the sliding surface as soon as possible is designed to ensure the robustness of uncertain fuzzy system.Lyapunov equation is applied to proving the stability of the sliding surface.The simulations show that the proposed approach can achieve desired performance with less chattering problem.

Robust control for a class of uncertain switched fuzzy systems

A model of uncertain switched fuzzy systems whose subsystems are uncertain fuzzy systems is presented. Robust controllers for a class of switched fuzzy systems are designed by using the Lyapunov function method. Stability conditions for global asymptotic stability are developed and a switching strategy is proposed. An example shows the effectiveness of the method.

Fuzzy-PID controlled lift feedback fin stabilizer

Conventional PID controllers are widely used in fin stabilizer control systems, but they have time-variations, nonlinearity, and uncertainty influencing their control effects. A lift feedback fuzzy-PID control method was developed to better deal with these problems, and this lift feedback fin stabilizer system was simulated under different sea condition. Test results showed the system has better anti-rolling performance than traditional fin-angle PID control systems.

Fuzzy Sliding Mode Control for a Discrete Servo System

A discrete fuzzy sliding mode variable structure controller was studied and designed for a class of electro-hydraulic servo system by means of the combination of sliding mode control theory and fuzzy control theory. Designed based on the exponential reaching law, paratmter e of conventional sliding mode controller is the key factor of system chattering, and is ~onal to it as well. In view of this, fuzzy control theory was introduced into the design to ensure the real- time adjusting of parameter e. Sinmlation results show that the sys tem chattering is eliminated perfectly, and the dynamic performance of the control system is improved effectively.

基于新型趋近律的PMSM模糊滑模控制

针对永磁同步电机的调速系统动态品质易受参数变化、外部扰动以及摩擦力等不确定因素影响而导致伺服性能降低的问题,提出一种改进的幂次指数趋近律,对该趋近律的参数k和ε引入模糊规则控制,有效抑制传统滑模变结构控制中的固有抖振问题,提升收敛速度。为了消除系统内、外部扰动引起的控制精度问题,设计一种新型扰动观测器进行观测。仿真结果表明:该方法能够实现精确的速度控制;与传统的滑模控制相比,在保留其优势的基础上,有效降低了滑模的抖振。

一类不确定切换模糊系统的记忆保成本控制

针对一类具有状态时滞和不确定性的切换模糊系统,提出了该类系统的记忆保成本控制问题。利用引入系统过去的状态信息,构造有记忆状态反馈控制器,以消除系统时滞对于系统稳定性产生的不良影响。在保证成本函数指标小于某个上界及允许的不确定性条件下,结合多Lyapunov函数方法和线性矩阵不等式(linear matrix inequality,LMI)处理方法得到最终的充分条件,保证闭环系统能够渐近稳定;且得到满足设计的切换律的记忆状态反馈控制器增益。最后通过Simulink仿真验证了所提方法的正确性。

基于模糊趋近律和扰动观测器的PMSM滑模控制

为了提高永磁同步电机调速系统的响应精度、速度、鲁棒性,解决传统滑模控制中趋于滑模面时间与系统抖振相矛盾的问题,提出了一种基于模糊趋近律和扰动观测器的滑模控制。模糊趋近律根据系统轨迹与滑模切换面的距离来有效地调整切换增益的大小,扰动观测器估计系统的内部参数变化和外部负载干扰,将估计值前馈补偿到速度控制器。仿真结果表明,系统快速跟踪阶跃信号,响应速度加快,系统出现扰动时,电机转速波动极小,迅速恢复稳定时间,结果表明采用基于模糊趋近律和扰动观测器的滑模控制方法能够有效地增强调速系统的鲁棒性和动态性能,并抑制系统的抖振。

基于改进模糊滑模控制的列车速度跟踪研究

列车运行过程具有强耦合、非线性等特征,且往往存在较大且不可观测的外界干扰,这些特性加大了列车运行控制的难度,针对列车运行速度跟踪控制问题,提出一种新的模糊滑模控制器设计方法。首先基于模糊滑模控制,引入全局快速终端滑模控制,使系统在有限时间内达到稳态;然后构造以李亚普洛夫函数导数的绝对值为补偿的自适应干扰估计项,对外界干扰进行准确估计,进而提出一种双层递阶指数趋近全局快速终端模糊滑模控制器,并且引入指数趋近率来调节滑模面的动态品质,该控制器能快速收敛到稳定状态,且有效地消除了控制器的抖振情况;最后通过仿真算例验证了所提方法的有效性,且提高了列车运行控制系统性能。

新型滑模观测器下的表贴式永磁同步电机控制

在传统的滑模观测器(SMO)下的永磁同步电机(PMSM)无位置传感器控制系统中存在着一些问题,如抖振较强,实际转速与预期转速差值较大并且有波动等情况。针对这些问题,采用滑模控制中的一般趋近律代替传统滑模观测器中的等速趋近律,并且用光滑的Sigmoid函数替代传统的符号函数,同时通过模糊控制系统对指数趋近律中的参数进行自适应调节,对表贴式PMSM的无位置传感器控制中产生的抖振进行抑制,并且提高SMO的观测精度。利用Lyapunov第二方法对该方法进行稳定性判断,验证该方法的稳定性。使用MATLAB/Simulink搭建基于滑模观测器的PMSM控制系统仿真模型,将该仿真结果与传统的滑模观测器的仿真结果进行比较分析。研究结果显示,新型的滑模观测器依旧能够快速地追踪转子的位置,同时具有更加良好的稳定性。

基于模糊控制在线整定新趋近律的速度调节器研究

针对传统指数趋近律滑模变结构控制抖振大和参数整定难的问题,设计了一种新的滑模趋近律并采用模糊控制以实现参数整定。在双闭环直流调速系统中,基于新趋近律设计了速度调节器;同时为了提高趋近律参数整定效率,利用模糊控制设计了趋近律参数在线整定方法,通过模糊控制在线整定新趋近律的滑模参数来提高系统的调速性能。在Matlab/Simulink中搭建直流调速系统的仿真模型,结果表明所设计的新趋近律在抑制抖振和快速收敛方面优于传统指数趋近律,相较于试凑法模糊控制在线整定趋近律参数的效率更高、性能更好。

Fuzzy控制理论在甜菜制糖过程的应用

基于Ｆｕｚｚｙ控制理论提出了归一Ｆｕｚｚｙ量化及带修正函数的Ｆｕｚｚｙ控制规则自调整方法，并介绍了该法在甜菜制糖过程的成功应用。通过某糖厂的运行结果，证明了微机Ｆｕｚｚｙ控制在甜菜制糖过程应用的可行性与有效性。

基于模糊终端滑模控制器的路径跟踪横向控制策略

为了解决智能车辆采用传统滑模控制器(SMC)时跟踪精度较差和稳定性欠佳的问题,提出了一种基于模糊终端滑模控制器(FTSMC)的路径跟踪横向控制策略.首先,建立了车辆二自由度动力学模型和路径跟踪误差模型,并采用改进的指数趋近律设计了终端滑模控制器(TSMC).其次,为解决SMC抖振问题,通过模糊逻辑在线优化指数趋近律中的边界层,并基于Lyapunov稳定性理论证明了控制系统的稳定性.最后,建立了Matlab/Simulink和Carsim联合仿真模型,验证了所提控制策略的有效性.结果表明,在低、高车速两种工况下,FTSMC的横向位移偏差最大值分别降低了14.4%和23.3%,横摆角偏差最大值分别降低了11.0%和18.9%,抖振现象得到了显著的抑制.