Adaptive Backstepping Terminal Sliding Mode Control Method Based on Recurrent Neural Networks for Autonomous Underwater Vehicle

The trajectory tracking control problem is addressed for autonomous underwater vehicle(AUV) in marine environ?ment, with presence of the influence of the uncertain factors including ocean current disturbance, dynamic modeling uncertainty, and thrust model errors. To improve the trajectory tracking accuracy of AUV, an adaptive backstepping terminal sliding mode control based on recurrent neural networks(RNN) is proposed. Firstly, considering the inaccu?rate of thrust model of thruster, a Taylor’s polynomial is used to obtain the thrust model errors. And then, the dynamic modeling uncertainty and thrust model errors are combined into the system model uncertainty(SMU) of AUV; through the RNN, the SMU and ocean current disturbance are classified, approximated online. Finally, the weights of RNN and other control parameters are adjusted online based on the backstepping terminal sliding mode controller. In addition, a chattering?reduction method is proposed based on sigmoid function. In chattering?reduction method, the sigmoid function is used to realize the continuity of the sliding mode switching function, and the sliding mode switching gain is adjusted online based on the exponential form of the sliding mode function. Based on the Lyapu?nov theory and Barbalat’s lemma, it is theoretically proved that the AUV trajectory tracking error can quickly converge to zero in the finite time. This research proposes a trajectory tracking control method of AUV, which can e ectively achieve high?precision trajectory tracking control of AUV under the influence of the uncertain factors. The feasibility and e ectiveness of the proposed method is demonstrated with trajectory tracking simulations and pool?experi?ments of AUV.

SYNCHRONIZATION OF MASTER-SLAVE MARKOVIAN SWITCHING COMPLEX DYNAMICAL NETWORKS WITH TIME-VARYING DELAYS IN NONLINEAR FUNCTION VIA SLIDING MODE CONTROL

In this article, a synchronization problem for master-slave Markovian switching complex dynamical networks with time-varying delays in nonlinear function via sliding mode control is investigated. On the basis of the appropriate Lyapunov-Krasovskii functional, introducing some free weighting matrices, new synchronization criteria are derived in terms of linear matrix inequalities （LMIs）. Then, an integral sliding surface is designed to guarantee synchronization of master-slave Markovian switching complex dynamical networks, and the suitable controller is synthesized to ensure that the trajectory of the closed-loop error system can be driven onto the prescribed sliding mode surface. By using Dynkin＇s formula, we established the stochastic stablity of master-slave system. Finally, numerical example is provided to demonstrate the effectiveness of the obtained theoretical results.

Adaptive Terminal Sliding Mode Control for Rigid Robotic Manipulators

In order to apply the terminal sliding mode control to robot manipulators,prior knowledge of the exact upper bound of parameter uncertainties,and external disturbances is necessary.However,this bound will not be easily determined because of the complexity and unpredictability of the structure of uncertainties in the dynamics of the robot.To resolve this problem in robot control,we propose a new robust adaptive terminal sliding mode control for tracking problems in robotic manipulators.By applying this adaptive controller,prior knowledge is not required because the controller is able to estimate the upper bound of uncertainties and disturbances.Also,the proposed controller can eliminate the chattering effect without losing the robustness property.The stability of the control algorithm can be easily verified by using Lyapunov theory.The proposed controller is tested in simulation on a two-degree-of-freedom robot to prove its effectiveness.

Some Remarks on the Boundedness and Convergence Properties of Smooth Sliding Mode Controllers

Conventional sliding mode controllers are based on the assumption of switching control, but a well-known drawback of such controllers is the chattering phenomenon. To overcome the undesirable chattering effects, the discontinuity in the control law can be smoothed out in a thin boundary layer neighboring the switching surface. In this paper, rigorous proofs of the boundedness and convergence properties of smooth sliding mode controllers are presented. This result corrects flawed conclusions previously reached in the literature. An illustrative example is also presented in order to confirm the convergence of the tracking error vector to the defined bounded region.

Robust Sliding Mode Control for Nonlinear Discrete-Time Delayed Systems Based on Neural Network

This paper presents a robust sliding mode controller for a class of unknown nonlinear discrete-time systems in the presence of fixed time delay. A neural-network approximation and the Lyapunov-Krasovskii functional theory into the sliding-mode technique is used and a neural-network based sliding mode control scheme is proposed. Because of the novality of Chebyshev Neural Networks (CNNs), that it requires much less computation time as compare to multi layer neural network (MLNN), is preferred to approximate the unknown system functions. By means of linear matrix inequalities, a sufficient condition is derived to ensure the asymptotic stability such that the sliding mode dynamics is restricted to the defined sliding surface. The proposed sliding mode control technique guarantees the system state trajectory to the designed sliding surface. Finally, simulation results illustrate the main characteristics and performance of the proposed approach.

Sliding Mode Control for Singularly Perturbed Systems Using Accurate Reduced Model

In order to deal with unmodeled dynamics in large vehicle systems, which have an ill condition of the state matrix, the use of model order reduction methods is a good approach. This article presents a new construction of the sliding mode controller for singularly perturbed systems. The controller design is based on a linear diagonal transformation of the singularly perturbed model. Furthermore, the use of a single sliding mode controller designed for the slow component of the diagonalized system is investigated. Simulation results indicate the performance improvement of the proposed controllers.

Higher Order Sliding Mode Control of Uncertain Robot Manipulators

This paper proposes a higher order sliding mode controller for uncertain robot manipulators. The motivation for using high order sliding mode mainly relies on its appreciable features, such as high precision and elimination of chattering in addition to assure the same performance of conventional sliding mode like robustness. Instead of a regular control input, the derivative of the control input is used in the proposed control law. The discontinuity in the controller is made to act on the time derivative of the control input. The actual control signal obtained by integrating the derivative control signal is smooth and chattering free. The stability and the robustness of the proposed controller can be easily verified by using the classical Lyapunov criterion. The proposed controller is tested to a three-degree-of-freedom robot to prove its effectiveness.

基于剩余碰撞时间的线控制动分层控制策略

针对不同制动工况需求的制动策略存在差异的情况,提出一种线控制动分层控制策略.在该分层策略的上层,利用二阶TTC安全碰撞时间模型计算出车辆与前车的剩余碰撞时间,以此作为依据进行制动策略的选取,并建立了汽车二自由度模型、车身法向受力模型和Burckhardt轮胎模型;在该分层策略的下层,进行了制动力在轮间的分配,使用序列二次规划(SQP)方法,分别在一般制动、紧急制动、失稳制动3种工况下,以轮胎滑移率为对象建立优化函数,对车辆制动力进行了优化分配.使用MATLAB/Simulink和Carsim进行了联合仿真,对所提出3种工况下的制动分配策略进行了有效性验证.结果表明:在一般制动工况下,采用该策略时相比对照工况制动距离减少18.08%,制动时间减少25.12%;在紧急制动工况下,采用该策略时相比对照工况制动距离减少19.17%,制动时间减少12.79%;在失稳制动工况下,该策略可通过轮胎差扭来提升车辆的横向稳定性.采用文中制动策略显著提升了车辆的制动效能.

Four Wheel Independent Drive Electric Vehicle Lateral Stability Control Strategy

In this paper,a kind of lateral stability control strategy is put forward about the four wheel independent drive electric vehicle.The design of control system adopts hierarchical structure.Unlike the previous control strategy,this paper introduces a method which is the combination of sliding mode control and optimal allocation algorithm.According to the driver’s operation commands(steering angle and speed),the steady state responses of the sideslip angle and yaw rate are obtained.Based on this,the reference model is built.Upper controller adopts the sliding mode control principle to obtain the desired yawing moment demand.Lower controller is designed to satisfy the desired yawing moment demand by optimal allocation of the tire longitudinal forces.Firstly,the optimization goal is built to minimize the actuator cost.Secondly,the weighted least-square method is used to design the tire longitudinal forces optimization distribution strategy under the constraint conditions of actuator and the friction oval.Beyond that,when the optimal allocation algorithm is not applied,a method of axial load ratio distribution is adopted.Finally,Car Sim associated with Simulink simulation experiments are designed under the conditions of different velocities and different pavements.The simulation results show that the control strategy designed in this paper has a good following effect comparing with the reference model and the sideslip angle is controlled within a small rang at the same time.Beyond that,based on the optimal distribution mode,the electromagnetic torque phase of each wheel can follow the trend of the vertical force of the tire,which shows the effectiveness of the optimal distribution algorithm.

Robust Attitude Control for Reusable Launch Vehicles Based on Fractional Calculus and Pigeon-inspired Optimization

In this paper, a robust attitude control system based on fractional order sliding mode control and dynamic inversion approach is presented for the reusable launch vehicle RLV during the reentry phase. By introducing the fractional order sliding surface to replace the integer order one, we design robust outer loop controller to compensate the error introduced by inner loop controller designed by dynamic inversion approach. To take the uncertainties of aerodynamic parameters into account, stochastic robustness design approach based on the Monte Carlo simulation and Pigeon-inspired optimization is established to increase the robustness of the controller. Some simulation results are given out which indicate the reliability and effectiveness of the attitude control system. © 2014 Chinese Association of Automation.

PMSM正切趋近律无位置传感器角度补偿方法研究

在负载转矩突变的动态过程中,基于PI调节器的角度补偿方法作用时PMSM超螺旋滑模观测器(STSMO)转子电角度估算误差波动剧烈,因此依据角度估算误差的定义,建立了转子电角度补偿算法的数学模型。根据滑模控制和趋近律理论,提出了基于正切趋近律的变步长闭环角度补偿方法,选择角度估算误差的半角正切值作为角度调节步长,并通过前馈解耦得到的角度估算误差正余弦信号计算该步长,实现了对无位置传感器控制系统动态性能和抗扰动能力的改善。根据归一化灵敏度的定义,分析调节步长随角度估算误差变化的灵敏度,提出了基于归一化补偿灵敏度的系统动态性能分析方法,衡量两种补偿算法作用下系统的动态性能。计算和仿真结果表明,正切趋近律补偿方法具有更高的归一化补偿灵敏度,在负载转矩突变等角度估算误差变化剧烈的工况下能够实现更好的补偿效果,抑制估算角度误差的波动。实验结果表明,相比传统的PI补偿方法,正切趋近律补偿方法能够将突加额定转矩动态过程中角度估算误差的波动幅度降低61.9%,动态过程持续时间缩短23%,有效提升了系统的动态性能和抗扰动能力。

基于回流功率优化的直流微网DC-DC变流器超螺旋滑模控制

针对直流微网系统中双有源桥直流变换器在系统发生扰动时动态特性差及在单移相调制下变换器效率不高等问题,该文在扩展移相调制下提出一种结合回流功率优化的超螺旋滑模控制策略。首先,分析双有源全桥(dual-active-bridge,DAB)变换器在扩展移相调制下两种模式的传输功率、回流功率数学模型及软开关特性,并以传输功率数学模型为基础建立DAB变换器的降阶模型。其次,以回流功率作为目标函数,以传输功率和软开关特性作为约束条件,通过Karush-Kuhn-Tucker(KKT)条件法寻找最优移相比组合;利用DAB变换器降阶模型设计超螺旋滑模控制中的等效部分,采用超螺旋算法作为切换部分,这两部分与内移相比D1共同作用生成外移相比D2,用于调节变换器的输出电压。最后进行了仿真及实验验证,实验结果表明:在负载投切、输入电压突变、参考电压改变时变换器具有良好的动态性能,同时能有效地减小回流功率。

基于改进反步滑模控制算法的拖轮自主靠泊控制

[目的]针对拖轮的自主靠泊控制问题,研究基于虚拟船领导的目标跟踪控制策略在拖轮自主靠泊中的应用。[方法]首先,将拖轮的自主靠泊过程转变为虚拟拖轮和实际拖轮的目标跟踪控制过程;然后,设计靠泊系统的运动学模型,考虑靠泊场景中的特殊环境干扰,使用反步法和滑模控制法(SMC)设计全回转尾推进拖轮的自主靠泊控制器,提供了3种不同的滑模面设计并使用李雅普诺夫函数进行稳定性验证;最后,采用仿真实验的方式,通过靠泊轨迹、速度误差和距离误差来对控制的效果进行验证。[结果]仿真实验结果表明,设计的拖轮自主靠泊控制策略以及控制器在拖轮自主靠泊场景中具有较好的效果,面对系统的不确定干扰有较好的表现。[结论]该控制策略以及靠泊控制器的适用性以及鲁棒性较好,以全新的角度实现了拖轮自主靠泊控制,为后续拖轮的靠泊控制研究提供了新的方向。

融合反步法的无人驾驶汽车轨迹跟踪控制方法

为了解决无人驾驶汽车轨迹跟踪控制的准确性、稳定性和快速性问题,将反步法分别融合滑模变结构控制和模糊自适应控制.建立车辆三自由度运动学位姿误差微分方程,推导了基于反步法的车速和横摆角速度控制律,结合Lyapunov稳定性判据验证了系统稳定性.分别建立融合反步法的滑模变结构和模糊自适应轨迹跟踪控制方法,结合轨迹跟踪稳态误差、超调量和调整时间,验证、比较了控制方法准确性、稳定性和快速性3种性能的优劣.结果表明:融合反步法的滑模变结构轨迹跟踪控制的稳定性最好,轨迹跟踪超调量接近于0;融合反步法的模糊自适应轨迹跟踪控制快速性最好,轨迹跟踪调整时间相对于反步法缩短了18.2%.

基于反步滑模方法的车辆纵向自适应控制

车辆纵向控制是自动驾驶研究的关键技术之一,建立了复杂的车辆纵向动力学模型,设计了合适的控制算法,并构造了控制器,提升了控制性能。首先,在考虑道路倾角、轮胎形变和车轮滑动等影响因素的基础上,建立了复杂的车辆纵向动力学模型;然后,综合运用自适应方法和反步滑模控制方法对所建模型设计控制器,并对控制系统的稳定性作了证明;最后,采用MATLAB/Simulink软件对控制系统进行仿真分析。研究结果表明:通过自适应反步滑模方法设计的控制器具有较好的稳定性,能够控制车辆以极小的误差以期望速度行驶。研究成果可为车辆自动驾驶及交通拥堵的缓解提供技术支撑。

一种新型3-RRPU并联机构的运动学及鲁棒控制研究

针对现有的三平移并联机构大多正解不唯一、较难实现精确稳定运动控制的问题,提出了一种新的3-RRPU型三平移并联机构,同时研究了机构的运动学、动力学及轨迹跟踪控制方法。首先,运用螺旋理论分析了该机构三平移的原理,进行了驱动输入的选取与论证,对机构平台奇异性、驱动奇异性和支链奇异性进行了分析;其次,运用运动学理论建立了运动学模型,推导了位置正、逆解析解;再次,运用拉格朗日动力学理论建立了机构操作空间的刚体动力学模型;最后,提出了一种基于非线性反馈解耦的自适应滑模轨迹跟踪控制方法,以证明其控制稳定性,并基于动力学模型对空间轨迹跟踪精度和控制力矩进行了仿真分析。研究结果表明:3-RRPU并联机构的位置正解在一般位形下具有唯一性,机构的平台奇异位形和驱动奇异位形少且无支链奇异。该轨迹控制方法可使机构在载荷20 kg、速度0.18 m/s、加速度0.12 m/s 2下,实现0.002 m以内的高精度复杂轨迹跟踪;基于Sigmoid函数的轨迹控制方法还能有效削弱控制力矩的抖振,为实现工业领域高速、高精度、高性能的三平移运动提供了新方法。

Stewart平台神经网络非奇异终端滑模控制

针对Stewart平台的六自由度(six degrees of freedom,6-DOF)轨迹跟踪问题,提出一种基于神经网络的非奇异终端滑模控制方法并应用于Stewart平台的位置姿态控制中。通过分析Stewart平台的位置反解和速度反解,建立运动学方程,利用牛顿-欧拉方程建立动力学方程,并结合加速度反解得到了平台的状态空间表达式;基于非奇异滑模面函数,设计非奇异终端滑模控制律。考虑到径向基函数(radial Basis function,RBF)神经网络的逼近特性,采用RBF神经网络对模型未知部分进行自适应逼近,并利用Lyapunov第二法设计了自适应律;通过仿真证明控制器设计的有效性。仿真结果表明,相比于比例积分微分(proportional integral derivative,PID)控制器,提出的RBF神经网络非奇异终端滑模控制器具有更好的轨迹跟踪精度和动态特性。

基于积分滑模观测器的直线振荡电机谐振频率跟踪控制

现有直线振荡电机(LOM)无位置传感器谐振频率跟踪控制方法存在行程观测精度低、抗干扰能力差等问题,容易导致谐振频率跟踪精度低、频率振荡严重。为提高谐振频率跟踪控制性能,该文研究了一种基于积分滑模观测器(ISMO)的新型谐振频率跟踪控制方法。首先,基于LOM工作在系统谐振频率点时行程-电流相位差为90°的特点,提出一种双互相关函数比值方法,消除了行程变化对谐振频率跟踪控制的影响。其次,采用积分滑模面及新型趋近律设计ISMO,并利用二阶广义积分器获取行程估算信号,大大提高了行程估算精度。最后,对比实验结果表明,所提方法能够有效提高谐振频率跟踪精度、稳定时间及抗干扰能力。

三维运动模式下的桥式吊车神经网络滑模控制

三维运动模式下的桥式吊车具有更高的生产效率,但其定位与防摆控制也更具挑战性.针对该问题,本文提出一种基于最小参数学习的神经网络滑模控制方法.首先,建立了包含机械摩擦力和空气阻力的全驱动动力学模型,解决了系统由于欠驱动特性导致控制器难以设计的问题;随后,设计了基于指数趋近律的滑模控制器,引入径向基函数(radial basis functions,RBF)神经网络的最小参数学习法对系统的不确定性模型进行逼近;并对控制器的稳定性进行了严格的数学证明.仿真与实验结果表明,本文所提控制方法在有/无外界干扰的情况下,都能实现吊车的精确定位与负载摆动的有效抑制.

基于载机视觉信息的改进视线制导律设计

针对无人机机载武器低成本精确打击的需求,提出利用机载光电吊舱获取制导信息、采用视线制导法进行制导律设计的方案。通过分析载机、导弹和目标的运动关系,建立了系统状态空间模型,基于反步法和滑模控制设计了改进视线制导律。对制导律中无法由载机获取的参数进行了讨论,采用跟踪微分器提取视线角并估计视线角速度信息。数值仿真结果表明,在视线角存在噪声且导弹矢径拉偏的情况下,改进视线制导律仍可保证对地面目标的精准打击。与传统视线制导律相比,载机的运动方向不受限制,可在发射导弹后远离目标以免受到威胁,符合现实需求。