Robust control of robots via linear estimated state feedback

In this note we propose a robust tracking controller for robots that requires only position measurements. The controller consists of two parts: a linear observer part that generates an estimated error state from the error on the joint position and a linear feedback part that utilizes this estimated state. It is shown that this computationally efficient controller yields semi-global uniform ultimate boundedness of the tracking error. An interesting feature of the controller is that it straightforwardly extends results on robust control of robots by linear state feedback to linear estimated-state feedback     

A novel fused neural network controller for lateral control of autonomous vehicles

We present a novel fused feed-forward neural network controller inspired by the notion of task decomposition principle. The controller is structurally simple and can be applied to a class of control systems that their control requires manipulation of two input variables. The benchmark problem of inverted pendulum is such example that its control requires availability of the angle as well as the displacement. We demonstrate that the lateral control of autonomous vehicles belongs to this class of systems and successfully apply the proposed controller to this problem. The parameters of the controller are encoded into real value chromosomes for genetic algorithm (GA) optimization. The neural network controller contains three neurons and six connection weights implying a small search space implying faster optimization time due to few controller parameters. The controller is also tested on two benchmark control problems of inverted pendulum and the ball-and-beam system. In particular, we apply the controller to lateral control of a prototype semi-autonomous vehicle. Simulation results suggest a good performance for all the tested systems. To demonstrate the robustness of the controller, we conduct Monte-Carlo evaluations when the system is subjected to random parameter uncertainty. Finally experimental studies on the lateral control of a prototype autonomous vehicle with different speed of operation are included. The simulation and experimental studies suggest the feasibility of this controller for numerous applications.     

Gain scheduling control of nonlinear systems based on approximate input-output linearization

The gain scheduling control mostly has been developed based on Jacobian linearization around the operating points related with scheduling variables. In this paper, We introduce a gain scheduling control method based on approximate input-output linearization. First, the nonlinear system is approximately input-output linearized via a diffeomorphism. Then, a gain scheduling controller with derivative information is developed. The proposed controller consists of two parts. The outer loop controller is like a feedback linearizing controller and the internal controller is a gain scheduling controller. It is shown that the overall resulting controller has a simple structure and at the same time achieves better tracking performance over the existing Jacobian-based gain scheduling controller.     

A two-degree-of-freedom PI controller based on events

In this paper, a completely event-based two-degree-of-freedom proportional-integral controller is presented. The architecture of the controller is based on event-based decoupled solutions for the set-point following and the load disturbance rejection tasks. For the first task, the solution is a design procedure that, by considering a first-order-plus-dead-time model of the process and a predesigned open-loop control action, produces an event-based feedforward controller that provides the required process variable transition with just two events. The second task is solved by applying two separate event-based proportional and integral control actions. Because the two tasks are initially solved independently, two solutions for coupling them are described. Illustrative examples of the performance of the controller are included as well as experimental results.     

神经网络PID工业温度控制系统研究

本文介绍了一种神经网络PID控制器,并将它与传统PID控制器、模糊控制器和神经网络控制器进行比较。神经网络PID控制器综合了PID控制器和神经网络控制器的优点并弥补了它们各自的不足之处。通过上述各种控制器在工业温度控制系统的仿真研究表明,神经网络PID控制器具有超调小,过度时间短,控制精度高的优点,对大滞后的温度控制系统有很好的控制效果。     

低阶鲁棒解耦控制器设计

提出了一种低阶鲁棒解耦控制器设计方法,能妥善处理多变量反馈系统的耦合性和方 向性.整个设计过程分两步完成:先将闭环系统的鲁棒性能要求转化为关于控制器参数的约束, 然后设计控制器逼近约束条件,最终得到低阶控制器.仿真结果表明,基于本方法的控制器,其 鲁棒性能接近μ-最优控制器,而远优于内模控制器.     

基于解析结构的模糊控制器的稳定性和一致逼近性分析

基于模糊控制的解析结构理论,设计了输入变量采用三角形、全交迭,等差间距分布的隶属函数,输出变量采用对称、均匀分布的单点隶属函数和线性控制规则的二维模糊控制器,推导了模糊控制器的解析表达式。利用Lyapunov第二法论证了该模糊控制系统在平衡点处的局部稳定性,并证明了模糊控制器的一致逼近性。     

台达DTE智能温控器在螺杆挤出机中的应用

本文重点介绍了台达两种不同型号的温度控制器在塑料挤出机上的应用。通过上述两种应用可知挤出机的温度控制器有了全新的选择。这也必将是挤出机方面的新趋势。     

A Robust Tracking Controller for Electrically Driven Robot Manipulators: Stability Analysis and Experiment

 In this paper, a robust controller for electrically driven robotic systems is developed. The controller is designed in a backstepping manner. The main features of the controller are: 1) Control strategy is developed at the voltage level and can deal with both mechanical and electrical uncertainties. 2) The proposed control law removes the restriction of previous robust methods on the upper bound of system uncertainties. 3) It also benefits from global asymptotic stability in the Lyapunov sense. It is worth to mention that the proposed controller can be utilized for constrained and nonconstrained robotic systems. The effectiveness of the proposed controller is verified by simulations for a two link robot manipulator and a four-bar linkage. In addition to simulation results, experimental results on a two link serial manipulator are included to demonstrate the performance of the proposed controller in tracking a given trajectory.     

A New Approach to Robust and Fault Tolerant Control

In this paper, we shall summarize a new approach to robust and fault tolerant control proposed recently by the author. This approach is based on a variation of all controller parametrization. This robust and fault-tolerant control design consists of two parts: a nominal performance controller and a robustness controller, and works in such a way that when a component (sensor, actuator, etc.) failure is detected, the controller structure is reconfigured by adding a robustness loop to compensate the fault. We shall illustrate how this strategy works under various situations.     

Nonlinear system compound inverse control method

A compound neural network is utilized to identify the dynamic nonlinear system. This network is composed of two parts： one is a linear neural network, and the other is a recurrent neural network. Based on the inverse theory a compound inverse control method is proposed. The controller has also two parts： a linear controller and a nonlinear neural network controller. The stability condition of the closed-loop neural network-based compound inverse control system is demonstrated .based on the Lyapunov theory. Simulation studies have shown that this scheme is simple and has good control accuracy and robustness.     

Nonlinear system compound inverse control method

A compound neural network is utilized to identify the dynamic nonlinear system.This network is composed of two parts: one is a linear neural network,and the other is a recurrent neural network.Based on the inverse theory a compound inverse control method is proposed.The controller has also two parts:a linear controller and a nonlinear neural network controller.The stability condition of the closed-loop neural network-based compound inverse control system is demonstrated based on the Lyapunov theory.Simulation studies have shown that this scheme is simple and has good control accuracy and robustness.     

Fuzzy learning control for a flexible-link robot

There are two main drawbacks in fuzzy control: 1) the design of fuzzy controllers is usually performed in an ad hoc manner where it is often difficult to choose some of the controller parameters; and 2) the fuzzy controller constructed for the nominal plant may later perform inadequately if significant and unpredictable plant parameter variations occur. In this paper we illustrate these two problems on a two-link flexible robot testbed by: 1) developing, implementing, and evaluating a fuzzy controller for the robotic mechanism, and 2) illustrating that payload variations can have negative effects on the performance of a well designed fuzzy control system. Next, we show how to develop and implement a fuzzy model reference learning controller for the flexible robot and illustrate that it can automatically synthesize a rule-base for a fuzzy controller that will achieve comparable performance to the case where it was manually constructed, and automatically tune the fuzzy controller so that it can adapt to variations in the payload     

Iterative learning variable structure controller for high-speed and high-precision point-to-point motion

In this paper, an iterative learning variable structure controller is proposed for the fast point-to-point motion. The motion is first divided into two stages: high-speed motion and high-precision positioning. Then the controllers are designed for these two stages, respectively. An iterative learning law is developed to determine the switch position. Based on this switch position a controller is proposed for the high-speed motion. Subsequently, a sliding controller is designed for the high-precision positioning stage. This controller is implemented on an X–Y positioning table. The results of the experiments show that it performs well and almost no overshoot exists if the discontinuous projection is used. The settling time is also reduced greatly, which is very important in the point-to-point motion in this paper.     

Swinging up the spherical pendulum via stabilization of its first integrals

This paper presents a new controller for swinging up the spherical pendulum. The problem of stabilizing the upright equilibrium of the spherical pendulum is typically solved in two steps: First, a controller is used to swing up the pendulum so that it enters some vicinity of the upright equilibrium, and then a locally stabilizing controller is used. The contribution of this paper is the design of a controller that solves the first subproblem for almost all initial conditions. The controller stabilizes the two-dimensional stable manifold of the hyperbolic upright equilibrium. The proposed technique is based on the passivity properties of the spherical pendulum.     

混合式智能调节器

本文通过分析模糊调节器和常规PID调节器各自的特点,提出了使用专家系统技术来协调模糊调节器和常规PID调节器的方法,设计了一种新型的混合式智能调节器.通过仿真试验表明这种调节器对一些复杂难控对象的控制是有效的。     

基于ARM的模糊自适应PID温度控制系统设计

为了解决传统PID控制器控制电冰箱温度所存在的响应速度慢、控制精度不高等问题,本文采用自整定模糊PID控制算法设计了模糊PID控制器,并在MATLAB环境中进行了仿真,与常规PID控制器的性能进行了对比,结果表明模糊自适应PID控制器的响应特性优于传统的PID控制器,响应速度更快,稳态精度更高,超调量更小,提高了电冰箱温度控制系统的性能.最后在ARM芯片上实现模糊控制算法,模糊控制表采用新方法二维矩阵存储,以便方便查询和管理,更好地满足温度调节的实时性.     

神经元滤波型2自由度PID控制器研究

本文给出了一种用神经元实现的给定值滤波型2自由度PID控制器．这种控制器利用 神经元权值自学习功能解决了2自由度PID控制器参数整定这个难题并使系统具有自适应能力 ．物理实验表明：神经元滤波型2自由度PID控制器具有应用价值．     

Stabilization of the PVTOL aircraft based on a sliding mode and a saturation function

This study presents a feedback control strategy for the regulation of a planar vertical takeoff and landing aircraft. To this end, two controllers that work simultaneously were designed. The first controller is devoted to stabilizing the vertical variable and is based on a simple feedback‐linearization procedure in combination with a saturation function. The second controller – based on a combination of the traditionally PD‐controller and a sliding mode controller – stabilizes both the horizontal and angular variables to the desired rest position. The performance of the closed‐loop system is demonstrated through simulation results. Copyright © 2016 John Wiley & Sons, Ltd.     

Gain scheduled state feedback velocity control of hydrostatic drive transmissions

In this paper, a velocity tracking controller for hydrostatic drive transmissions is developed. The solution is based on a state-dependent model that incorporates nonlinear characteristics of the system. A full state feedback controller is devised and the gains are scheduled on measured speed and pressures, together with approximated volumetric flow. The effects of uncertainties, especially those related to equilibrium values of pressures, are eliminated by utilizing so-called D-implementation. This technique eliminates the need for equilibrium values, which are model based and thus uncertain.To demonstrate the efficacy of the controller, the solution is implemented on a 4.5-ton wheel loader. For comparison purposes, a constant gain state feedback controller with integral action is devised, and also a linear PID controller is tuned. The results show that the benefits of the devised controller are significant when it is compared to these two controllers. Moreover, the controllability of the machine is maintained in every situation.