Adaptive Robust Tracking Control of Pressure Trajectory Based on Kalman Filter

When adaptive robust control(ARC) strategy based on backstepping design is applied in pneumatic servo control, accurate pressure tracking in motion is especially necessary for both force and position trajectories tracking of rodless pneumatic cylinders, and therefore an adaptive robust pressure controller is developed in this paper to improve the tracking accuracy of pressure trajectory in the chamber when the pneumatic cylinder is moving. In the proposed adaptive robust pressure controller, off-line fitting of the orifice area and on-line parameter estimation of the flow coefficient are utilized to have improved model compensation, and meanwhile robust feedback and Kalman filter are used to have strong robustness against uncertain nonlinearities, parameter fluctuations and noise. Research results demonstrate that the adaptive robust pressure controller could not only track various pressure trajectories accurately even when the pneumatic cylinder is moving, but also obtain very smooth control input, which indicates the effectiveness of adaptive model compensation. Especially when a step pressure trajectory is tracked under the condition of the movement of a rodless pneumatic cylinder, maximum tracking error of ARC is 4.46 kPa and average tracking error is 0.99 kPa, and steady-state error of ARC could achieve 0.84 kPa, which is very close to the measurement accuracy of pressure transducer.     

Ultra-precise tracking control of piezoelectric actuators via a fuzzy hysteresis model

In this paper, a novel Takagi-Sugeno (T-S) fuzzy system based model is proposed for hysteresis in piezoelectric actuators. The antecedent and consequent structures of the fuzzy hysteresis model (FHM) can be, respectively, identified on-line through uniform partition approach and recursive least squares (RLS) algorithm. With respect to controller design, the inverse of FHM is used to develop a feedforward controller to cancel out the hysteresis effect. Then a hybrid controller is designed for high-performance tracking. It combines the feedforward controller with a proportional integral differential (PID) controller favourable for stabilization and disturbance compensation. To achieve nanometer-scale tracking precision, the enhanced adaptive hybrid controller is further developed. It uses real-time input and output data to update FHM, thus changing the feedforward controller to suit the on-site hysteresis character of the piezoelectric actuator. Finally, as to 3 cases of 50 Hz sinusoidal, multiple frequency sinusoidal and 50 Hz triangular trajectories tracking, experimental results demonstrate the efficiency of the proposed controllers. Especially, being only 0.35% of the maximum desired displacement, the maximum error of 50 Hz sinusoidal tracking is greatly reduced to 5.8 nm, which clearly shows the ultra-precise nanometer-scale tracking performance of the developed adaptive hybrid controller.     

Direct adaptive robust tracking control for 6 DOF industrial robot with enhanced accuracy

A direct adaptive robust tracking control is proposed for trajectory tracking of 6 DOF industrial robot in the presence of parametric uncertainties, external disturbances and uncertain nonlinearities. The controller is designed based on the dynamic characteristics in the working space of the end-effector of the 6 DOF robot. The controller includes robust control term and model compensation term that is developed directly based on the input reference or desired motion trajectory. A projection-type parametric adaptation law is also designed to compensate for parametric estimation errors for the adaptive robust control. The feasibility and effectiveness of the proposed direct adaptive robust control law and the associated projection-type parametric adaptation law have been comparatively evaluated based on two 6 DOF industrial robots. The test results demonstrate that the proposed control can be employed to better maintain the desired trajectory tracking even in the presence of large parametric uncertainties and external disturbances as compared with PD controller and nonlinear controller. The parametric estimates also eventually converge to the real values along with the convergence of tracking errors, which further validate the effectiveness of the proposed parametric adaption law.     

A robust approach for trajectory tracking control of a quadrotor with experimental validation

In this paper, a new control scheme for motion control of quadrotors is presented. The proposed model based controller makes use of simple estimations of vehicle parameters. Besides, the controller is developed under properties of the vehicle model. A rigorous analysis is developed to prove that the closed-loop system trajectories are uniformly ultimately bounded. The validity of the proposed control scheme is tested by numerical simulations and experimental results. This validation shows that the proposed controller guarantees that the quadrotor performs tracking of a desired pose (position and orientation) trajectory with good accuracy. The new scheme has been compared with respect to other robust controllers. The results indicate better tracking accuracy for the new scheme.     

Dynamics Modeling and Robust Trajectory Tracking Control for a Class of Hybrid Humanoid Arm Based on Neural Network

In order to solve the problem of trajectory tracking for a class of novel serial-parallel hybrid humanoid arm(HHA), which has parameters uncertainty, frictions, disturbance, abrasion and pulse forces derived from motors, a multistep dynamics modeling strategy is proposed and a robust controller based on neural network(NN)-adaptive algorithm is designed. At the first step of dynamics modeling, the dynamics model of the reduced HHA is established by Lagrange method. At the second step of dynamics modeling, the parameter uncertain part resulting mainly from the idealization of the HHA is learned by adaptive algorithm. In the trajectory tracking controller, the radial basis function(RBF) NN, whose optimal weights are learned online by adaptive algorithm, is used to learn the upper limit function of the total uncertainties including frictions, disturbances, abrasion and pulse forces. To a great extent, the conservatism of this robust trajectory tracking controller is reduced, and by this controller the HHA can impersonate mostly human actions. The proof and simulation results testify the validity of the adaptive strategy for parameter learning and the neural network-adaptive strategy for the trajectory tracking control.     

车辆横摆角速度跟踪控制仿真研究

采用二自由度车辆动力学状态方程建立了车辆横摆角速度跟踪控制模型。用横摆角速度与其期望值的差值及其变化率作为模糊控制器的输入,设计了模糊自适应PID控制器。基于模糊自适应PID控制器,进行了前轮转向阶跃输入、正弦输入仿真试验。仿真和分析结果表明,设计的模糊PID控制器可实现对参考模型横摆角速度的跟踪,车辆的操纵稳定性得到了有效改善。     

New hybrid adaptive neuro-fuzzy algorithms for manipulator control with uncertainties–Comparative study

Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. In this paper, some new hybrid adaptive neuro-fuzzy control algorithms (ANFIS) have been proposed for manipulator control with uncertainties. These hybrid controllers consist of adaptive neuro-fuzzy controllers and conventional controllers. The outputs of these controllers are applied to produce the final actuation signal based on current position and velocity errors. Numerical simulation using the dynamic model of six DOF puma robot arm with uncertainties shows the effectiveness of the approach in trajectory tracking problems. Performance indices of RMS error, maximum error are used for comparison. It is observed that the hybrid adaptive neuro-fuzzy controllers perform better than only conventional/adaptive controllers and in particular hybrid controller structure consisting of adaptive neuro-fuzzy controller and critically damped inverse dynamics controller.     

Adaptive robust motion trajectory tracking control of pneumatic cylinders with LuGre model-based friction compensation

Friction compensation is particularly important for motion trajectory tracking control of pneumatic cylinders at low speed movement. However, most of the existing model-based friction compensation schemes use simple classical models, which are not enough to address applications with high-accuracy position requirements. Furthermore, the friction force in the cylinder is time-varying, and there exist rather severe unmodelled dynamics and unknown disturbances in the pneumatic system. To deal with these problems effectively, an adaptive robust controller with LuGre model-based dynamic friction compensation is constructed. The proposed controller employs on-line recursive least squares estimation（RLSE） to reduce the extent of parametric uncertainties, and utilizes the sliding mode control method to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. In addition, in order to realize LuGre model-based friction compensation, the modified dual-observer structure for estimating immeasurable friction internal state is developed. Therefore, a prescribed motion tracking transient performance and final tracking accuracy can be guaranteed. Since the system model uncertainties are unmatched, the recursive backstepping design technology is applied. In order to solve the conflicts between the sliding mode control design and the adaptive control design, the projection mapping is used to condition the RLSE algorithm so that the parameter estimates are kept within a known bounded convex set. Finally, the proposed controller is tested for tracking sinusoidal trajectories and smooth square trajectory under different loads and sudden disturbance. The testing results demonstrate that the achievable performance of the proposed controller is excellent and is much better than most other studies in literature. Especially when a 0.5 Hz sinusoidal trajectory is tracked, the maximum tracking error is 0.96 mm and the average tracking error is 0.45 mm. This paper constructs an adaptive robust controller     

Robust iterative learning contouring controller with disturbance observer for machine tool feed drives

In feed drive systems, particularly machine tools, a contour error is more significant than the individual axial tracking errors from the view point of enhancing precision in manufacturing and production systems. The contour error must be within the permissible tolerance of given products. In machining complex or sharp-corner products, large contour errors occur mainly owing to discontinuous trajectories and the existence of nonlinear uncertainties. Therefore, it is indispensable to design robust controllers that can enhance the tracking ability of feed drive systems. In this study, an iterative learning contouring controller consisting of a classical Proportional-Derivative (PD) controller and disturbance observer is proposed. The proposed controller was evaluated experimentally by using a typical sharp-corner trajectory, and its performance was compared with that of conventional controllers. The results revealed that the maximum contour error can be reduced by about 37% on average.     

Sliding mode control with PID sliding surface and experimental application to an electromechanical plant

In this study, a sliding mode control system with a proportional+integral+derivative (PID) sliding surface is adopted to control the speed of an electromechanical plant. A robust sliding mode controller is derived so that the actual trajectory tracks the desired trajectory despite uncertainty, nonlinear dynamics, and external disturbances. The proposed sliding mode controller is chosen to ensure the stability of overall dynamics during the reaching phase and sliding phase. The stability of the system is guaranteed in the sense of the Lyapunov stability theorem. The chattering problem is overcome using a hyperbolic function for the sliding surface. Experimental results that are compared with the results of conventional PID verify that the proposed sliding mode controller can achieve favorable tracking performance, and it is robust with regard to uncertainties and disturbances.     

随机振动机械臂的自适应鲁棒滑模控制

为了解决存在外部不确定随机干扰情况下机械臂的高精度轨迹跟踪问题,提出了一种自适应鲁棒滑模控制方法,并用Lyapunov稳定性定理证明了其闭环系统的稳定性。采用饱和函数取代控制器中的符号函数,有效消除了控制器的抖振现象。仿真结果证明：与传统的PID控制器相比,提出的自适应鲁棒滑模控制器具有更高的鲁棒性、稳定性和精度。     

Design of a new PID controller using predictive functional control optimization for chamber pressure in a coke furnace

An improved proportional-integral-derivative (PID) controller based on predictive functional control (PFC) is proposed and tested on the chamber pressure in an industrial coke furnace. The proposed design is motivated by the fact that PID controllers for industrial processes with time delay may not achieve the desired control performance because of the unavoidable model/plant mismatches, while model predictive control (MPC) is suitable for such situations. In this paper, PID control and PFC algorithm are combined to form a new PID controller that has the basic characteristic of PFC algorithm and at the same time, the simple structure of traditional PID controller. The proposed controller was tested in terms of set-point tracking and disturbance rejection, where the obtained results showed that the proposed controller had the better ensemble performance compared with traditional PID controllers.     

直线电机精密定位平台轨迹跟踪控制器设计

为了实现直线电机精密定位平台的位置和速度的轨迹跟踪控制,本文基于内模控制（IMC）的基本原理,在直线电机精密定位平台参数辨识的基础上,设计了定位平台速度环的模型状态反馈（MSF）控制器和基于位置环PID和速度环MSF的级联控制器。将PID/MSF级联控制器与速度/加速度前馈控制（VFC/AFC）相结合,构成了PID/MSF+VFC/AFC的复合轨迹跟踪控制器。该复合轨迹跟踪控制器通过整定速度前馈的增益来改善位置环偏差控制的跟踪滞后现象和动态响应,增加控制系统的稳定性和伺服精度;通过整定加速度前馈的增益在不减小级联控制器位置环增益的前提下,减小速度前馈带来的超调量,提高轨迹跟踪精度。基于MATLAB/dSPACE实时仿真控制平台,实现了某直线电机平台的轨迹跟踪控制。仿真和实验结果表明,该轨迹跟踪控制器的轨迹跟踪精度为±0.028 mm,定位精度为±4 μm,满足直线电机精密定位平台轨迹跟踪控制的要求。     

Adaptive second-order fast nonsingular terminal sliding mode control for robotic manipulators

This paper presents an adaptive chattering-free sliding mode controller for trajectory tracking of robotic manipulators in the presence of external disturbances and inertia uncertainties. To achieve fast convergence and desirable tracking precision, a second-order fast nonsingular terminal sliding mode (SOFNTSM) controller is designed to guarantee system performance and robust stability. Chattering is eliminated using continuous control law due to high-frequency switching terms contained in the first derivative of actual control signals. Meanwhile, uncertainties are compensated by introducing the adaptive technique, whose prior knowledge about upper bound is not required. Finally, simulation results validate the effectiveness of the proposed control scheme.     

即时学习型迭代学习控制在励磁控制中的应用

将即时学习算法型迭代学习控制引入发电机的励磁控制。运用即时学习算法来解决系统的迭代学习控制初值问题,有效地估计初始控制量,加快了算法的收敛速度。仿真结果表明,所设计的励磁控制器与常规PID控制器和非即时学习型励磁控制器相比其收敛速度明显加快,具有更强的维持机端电压的能力。     

Experimental evaluation of adaptive three-tank level control

Liquid level control through regulation of mass flow rates is an important application in various areas of the power industry. Very often a PID controller is used for these applications. This paper compares a nonconventional PID controller and three different types of adaptive controller, a direct model reference adaptive controller (MRAC), an indirect MRAC with Lyapunov estimation, and an indirect MRAC with recursive least-squares (RLS) updating estimation, for liquid level control. By implementing all four controllers on a three-tank system, the performances of each are compared. All controllers track a sinusoidal input very well and overall exhibit somewhat varying performance. The direct MRAC and the indirect MRAC with RLS estimation give the best performance. With Lyapunov estimation and RLS estimation, all the system parameter estimates converge to the reference model values. However, RLS estimation has a much faster convergence. It is concluded that adaptive liquid level control is an improvement over traditional liquid level control when precise level control in three coupled tanks is desired.     

Adaptive nonsingular fast terminal sliding-mode control for the tracking problem of uncertain dynamical systems

In this paper, robust and adaptive nonsingular fast terminal sliding-mode (NFTSM) control schemes for the trajectory tracking problem are proposed with known or unknown upper bound of the system uncertainty and external disturbances. The developed controllers take the advantage of the NFTSM theory to ensure fast convergence rate, singularity avoidance, and robustness against uncertainties and external disturbances. First, a robust NFTSM controller is proposed which guarantees that sliding surface and equilibrium point can be reached in a short finite-time from any initial state. Then, in order to cope with the unknown upper bound of the system uncertainty which may be occurring in practical applications, a new adaptive NFTSM algorithm is developed. One feature of the proposed control law is their adaptation techniques where the prior knowledge of parameters uncertainty and disturbances is not needed. However, the adaptive tuning law can estimate the upper bound of these uncertainties using only position and velocity measurements. Moreover, the proposed controller eliminates the chattering effect without losing the robustness property and the precision. Stability analysis is performed using the Lyapunov stability theory, and simulation studies are conducted to verify the effectiveness of the developed control schemes.     

Dynamic path planning and trajectory tracking using MPC for satellite with collision avoidance

This paper proposes a dynamic path planning and trajectory tracking algorithm for an autonomous satellite, released from the space station, to get to the desired position for performing space tasks. The complex construction of the space station results in the presence of a geometric channel constraint for the obstacles avoidance. In addition, a three dimension B-spline template with minimizing the curvature of the path is designed, which could guarantee the continuity of the curvature to make the trajectory smooth and avoid the satellite from stopping at discontinuities waypoints. Then, the reference states and inputs are solved by a new projection method, which provides a foundation for the subsequent trajectory tracking. Subsequently, a finite horizon model predictive control method is constructed for the path tracking. The benefits of this approach are to take constraints into consideration, and to get optimal performance by minimizing the fuel consumption compared with other tracking controllers. The closed-loop stability is guaranteed by the feedback controller, terminal penalty, and a newly terminal constraint set. In simulation experiments, results illustrate the effectiveness and practicality of the algorithm.     

双缸同步提升电液系统建模和控制

以浙江大学研制的智能钢管捆成形系统为背景,针对电液提升系统的双缸同步运动问题,提出一种两级非线性控制算法.该控制器的外环级采用一个线性多输入多输出的鲁棒控制器,用于获得双缸运动同步控制所需的各缸期望负载压力;内环级采用两个基于扰动估计观测的单输入单输出液压缸非线性压力控制器,用于在有扰动情况下实现对每个提升缸期望负载压力的精确跟踪控制.实际的控制运行结果表明,该控制策略优于常规的PID控制,可以有效地实现双缸运动同步控制.