神经元PID控制器在两轮机器人控制中的应用

针对两轮机器人传统PID控制器参数整定困难的问题,设计了一种神经元PID控制器.该控制器利用神经元的自学习和自适应能力,在线实时调整控制器各项参数.建立了两轮机器人的非线性模型,讨论了神经元PID控制系统的结构及其控制算法和各项控制器参数的学习算法.将设计的控制器其应用于两轮机器人的平衡控制中,并且与传统PID控制器进...     

两轮自平衡机器人的LQR实时平衡控制

两轮平衡机器人已经成为能够为日常机器人提供未来运动方式的一个研究领域.两轮平衡机器人区别于传统形式的机器人,它需要必须具有一个独特的稳定控制系统来保持其直立.为了平衡系统该文利用平衡机器人的动态模型设计控制器,测试LQR在平衡系统的实用性并评估其性能.仿真结果表明LQR控制器可以稳定系统,并且在平衡基于倒立摆模型的两轮自平衡机器人时表现出满意的结果.     

两轮自平衡机器人的自适应模糊平衡控制

在两轮自平衡机器人系统的平衡控制中,为解决因所建立的数学模型不准确和存在未知干扰而影响控制性能的问题,设计了一种自适应模糊控制方法.首先,运用牛顿力学法建立了系统在斜坡上运动的数学模型.针对所建动态模型的非线性,提出采用单点模糊化、乘积推理机和中心平均解模糊化的方法构建了自适应模糊逻辑控制器,然后通过李雅普诺夫稳定性分析的方法,导出控制器的自适应律.在MA TLAB/Simulink中,对自适应模糊控制的两轮自平衡机器人的平衡情况进行了仿真,结果表明,提出的自适应模糊控制器可以实现系统平衡,并具有自适应能力和鲁棒性,为两轮机器人优化控制提供了依据.     

基于优化神经网络ADRC的机器人无标定视觉跟踪

研究机器人无标定视觉跟踪,采用基于神经网络的耦合自抗扰控制器,并用自适应遗传算法训练BP网络的初始权值,优化控制器参数,实现了六自由度机器人的无标定手眼协调控制,仿真结果验证了算法的可行性.     

基于Hopfield 网络的柔性两轮自平衡机器人控制

用弹簧模仿人的腰椎,采用LQR 成功实现了机器人实物控制．针对柔性两轮自平衡机器人的姿态控 制,提出了一种基于联想学习的离散Hopfield 网络实现方法,以生物学习控制方式实现柔性两轮自平衡机器人在姿 态控制上的自适应、自组织能力．针对非线性、强耦合的柔性机器人系统,首先定义了合理的能量变化函数,并运用 柔性机器人动力学方程设计了满足该动态过程的Hopfield 网络控制器,然后分析了该控制器的收敛性．仿真实验表 明了该方法的有效性和系统的稳定性．对实验结果进行详细分析,表明了系统姿态控制器设计的合理性和有效性．     

改进ACO优化的BP神经网络短时交通流量预测

针对BP神经网络算法收敛速度缓慢、易陷入局部最小值、在短时交通流量预测的问题中精度不高等问题,提出了一种改进ACO(蚁群算法)优化的BP神经网络短时交通流量预测算法.在确定BP神经网络权阈值的过程中,采用蚁群信息素挥发自适应参数、在蚁群信息素更新时采用精英选择策略和种群更新时加入变异因子的方法来得到最优权阈值.仿真结果表明,改进算法在预测流量趋势和准确度方面均有较大提升,在短时交通流量预测方面取得了良好的效果.     

模糊自适应在机器人行走平衡控制中的应用

针对多变量、非线性的两轮机器人系统的行走平衡控制问题,提出一种基于Backstepping（反推）方法和PID的控制策略。该策略在Backstepping控制器中加入模糊自适应部分,利用模糊系统逼近Backstepping设计过程中的未知非线性函数,模糊系统中的参数基于自适应律调整,解决了Backstepping控制器中因含有未知参数难以实现的困难,避免了两轮机器人系统不满足严格三角结构的问题。针对两轮机器人的仿真实验结果表明：采用设计的控制策略,可以实现两轮机器人的行走平衡控制任务。     

基于双种群鲨鱼优化算法的自抗扰机械臂轨迹跟踪

针对自抗扰控制器耦合参数过多,传统经验整定法难以整定的问题,提出一种改进的鲨鱼优化算法对参数进行在线整定.通过自适应控制因子、双种群寻优、位置废弃等策略改善传统鲨鱼优化算法易陷入局部最优和开发勘探能力不平衡的缺陷.将整定后的自抗扰控制器对机械臂进行轨迹跟踪实验.实验结果表明,优化后的自抗扰控制器有效降低了轨迹跟踪误差,提高了控制精度和抗扰动能力.     

两轮自平衡机器人的无源控制器设计

以两轮自平衡机器人为研究对象,基于其状态空间模型,利用线性矩阵不等式的方法,设计两轮自平衡机器人平衡的无源控制器,并给出了两轮自平衡机器人无源控制器存在的充分条件。仿真结果表明,设计的无源控制器对于机器人的平衡是有效的。     

改进教与学优化算法的LQR控制器优化设计

为了快速有效地确定线性二次最优控制(linear quadratic regulator,LQR)问题中的加权矩阵Q和R,针对主动悬架LQR控制器权系数设计问题,提出一种改进的教与学优化算法进行LQR优化设计。算法对基本教与学优化算法中的"教"与"学"阶段进行了进一步的改进,同时提出一种"自我学习"策略。通过仿真实验表明,和基本教与学算法、粒子群算法、遗传算法相比,本文算法在对主动悬架LQR控制器优化时,具有收敛速度快,求解精度高和稳定性强等优势。     

A control method to make LQR robust: A planes cluster approaching mode

Linear quadratic regulator (LQR) is an optimal controller being used for linear systems, and it can minimize the comprehensive quadratic performance index (QPI) with respect to convergence error and control consumption. However, LQR lacks the robust property to cope with parameter perturbations and external disturbances. Aiming at the above deficiency of LQR, a robust LQR (RLQR) is proposed for linear systems under the guideline of planes cluster approaching mode (PCAM). In the proposed RLQR, one nonlinear item is introduced into control law, and it cooperates with the other linear item to guarantee the global asymptotic stability in the presence of equivalent disturbances. The conditions of global asymptotic stability are deduced by the method of Lyapunov function. Simulation results present that, the chosen LTI plant using RLQR possesses smaller QPI in the existence of timevarying disturbances, compared with the conventional LQR and sliding mode controller (SMC).     

A Robotic Walker Based on a Two-Wheeled Inverted Pendulum

This paper presents the design and control of a robotic walker based on a two-wheeled inverted pendulum (TWIP) developed to assist mobility-impaired users with balance and stability. Traditional walkers use three or more contact points to create a solid base to augment a user’s balance. A TWIP walker can support a user’s balance through balance control. A robotic walker prototype has been developed to illustrate its ability to assist human gait and exploit the maneuverability of a two-wheeled mobile platform compared to multi-wheeled system. Presented is a linearized mathematical model of the two-wheeled system using Newtonian mechanics. A control strategy consisting of a decoupled linear quadratic regulator (LQR) controller and two state variable controllers is developed to stabilize the platform and regulate its behavior with robust disturbance rejection performance. Results are shown using a physical prototype to demonstrate the ability of the decoupled LQR controller to robustly balance the platform while the state variable controllers regulate the platform’s position with smooth, minimum jerk, control when used by a person during standing and walking.     

An iterative linear quadratic regulator based trajectory tracking controller for wheeled mobile robot

We present an iterative linear quadratic regulator(ILQR) method for trajectory tracking control of a wheeled mobile robot system.The proposed scheme involves a kinematic model linearization technique,a global trajectory generation algorithm,and trajectory tracking controller design.A lattice planner,which searches over a 3D(x,y,θ) configuration space,is adopted to generate the global trajectory.The ILQR method is used to design a local trajectory tracking controller.The effectiveness of the proposed method is demonstrated in simulation and experiment with a significantly asymmetric differential drive robot.The performance of the local controller is analyzed and compared with that of the existing linear quadratic regulator(LQR) method.According to the experiments,the new controller improves the control sequences(v,ω) iteratively and produces slightly better results.Specifically,two trajectories,’S’ and ’8’ courses,are followed with sufficient accuracy using the proposed controller.     

基于自适应机制改进蚁群算法的移动机器人全局路径规划

针对基本蚁群算法在二维静态栅格地图下进行移动机器人路径规划时出现的搜索效率低下、收敛速度缓慢、局部最优解等问题,提出一种自适应机制改进蚁群算法,用于移动机器人在二维栅格地图下的路径规划.首先采用伪随机状态转移规则进行路径选择,定义一种动态选择因子以自适应更新选择比例,引入距离参数计算转移概率,提高算法的全局搜索能力以及搜索效率;然后基于最大最小蚂蚁模型和精英蚂蚁模型,提出一种奖励惩罚机制更新信息素增量,提高算法收敛速度;最后定义一种信息素自适应挥发因子,限制信息素浓度的上下限,提高算法全局性的同时提高算法的收敛速度.在不同规格的二维静态栅格地图下进行移动机器人全局路径规划对比实验,实验结果表明自适应机制改进蚁群算法具有较快的收敛速度,搜索效率明显提高且具有较好的全局搜索能力,验证了所提算法的实用性和优越性.     

Adaptive control for input-constrained linear systems

This paper proposes a direct model reference adaptive control method for linear systems with unknown parameters in the presence of input constraints. First, we used the well-known linear quadratic regulator (LQR) technique to develop a modified reference model, which is the optimal model under input constraints. Second, a model reference adaptive controller, which tracked the modified reference model instead of the reference model, was designed to compensate for parametric uncertainties. Using Lyapunov stability theory, we proved that the modified reference model tracking error converges to zero. Simulation results demonstrate the effectiveness of the proposed controller.     

Adaptive PSO for optimal LQR tracking control of 2 DoF laboratory helicopter

This paper deals with the attitude tracking control problem for a 2 DoF laboratory helicopter using optimal linear quadratic regulator (LQR). As the performance of the LQR controller greatly depends on the weighting matrices (Q and R), it is important to select them optimally. However, normally the weighting matrices are selected based on trial and error approach, which not only makes the controller design tedious but also time consuming. Hence, to address the weighting matrices selection problem of LQR, in this paper we propose an adaptive particle swarm optimization (APSO) method to obtain the elements of Q and R matrices. Moreover, to enhance the convergence speed and precision of the conventional PSO, an adaptive inertia weight factor (AIWF) is introduced in the velocity update equation of PSO. One of the key features of the AIWF is that unlike the standard PSO in which the inertia weight is kept constant throughout the optimization process, the weights are varied adaptively according to the success rate of the particles towards the optimum value. The proposed APSO based LQR control strategy is applied for pitch and yaw axes control of 2 Degrees of Freedom (DoF) laboratory helicopter workstation, which is a highly nonlinear and unstable system. Experimental results substantiate that the weights optimized using APSO, compared to PSO, result in not only reduced tracking error but also improved tracking response with reduced oscillations.     

INTELLIGENT MOTION CONTROL FOR OMNIDIRECTIONAL MOBILE ROBOTS USING ANT COLONY OPTIMIZATION

This article presents an intelligent system-on-a-programmable-chip-based (SoPC) ant colony optimization (ACO) motion controller for embedded omnidirectional mobile robots with three independent driving wheels equally spaced at 120 degrees from one another. Both ACO parameter autotuner and kinematic motion controller are integrated in one field-programmable gate array (FPGA) chip to efficiently construct an experimental mobile robot. The optimal parameters of the motion controller are obtained by minimizing the performance index using the proposed SoPC-based ACO computing method. These optimal parameters are then employed in the ACO-based embedded kinematic controller in order to obtain better performance for omnidirectional mobile robots to achieve trajectory tracking and stabilization. Experimental results are conducted to show the effectiveness and merit of the proposed intelligent ACO-based embedded controller for omnidirectional mobile robots. These results indicate that the proposed ACO-based embedded optimal controller outperforms the nonoptimal controllers and the conventional genetic algorithm (GA) optimal controllers.     

面向机器人全局路径规划的改进蚁群算法研究

针对基本蚁群算法在机器人路径规划过程中路径转弯角度过大、易陷入局部极小值、收敛速度慢等问题,对其进行改进。在分析机器人路径规划环境建模方法基础上,将转角启发函数引入至节点选择概率公式,以增强路径选择指向性,提高算法搜索速度;通过引入当前节点与下一节点之间的距离和下一节点与目标节点距离之和的二次方对启发函数进行改进,使得算法搜索过程更有针对性,并降低陷入局部极小值概率;提出信息素挥发因子自适应更新策略,扩大算法搜索范围,提高收敛速度;利用遗传算法的交叉操作对移动路径进行二次优化,以增强算法的寻优能力,进而以Floyd算法为基础引入路径平滑操作,减少移动路径节点。在MATLAB中与其他算法通过求解多个单模测试函数与多模测试函数进行对比,并在栅格法环境建模中进行机器人全局路径规划仿真对比实验,以验证改进算法在路径寻优速度和质量上更具优越性。仿真结果表明,改进后的蚁群算法具有一定的可行性和有效性。