Hierarchical optimal force–position–contour control of machining processes

There has been a tremendous amount of research in machine tool servomechanism control, contour control, and machining force control; however, to date these technologies have not been tightly integrated. This paper develops a hierarchical optimal control methodology for the simultaneous regulation of servomechanism positions, contour error, and machining forces. The contour error and machining force process reside in the top level of the hierarchy where the goals are to (1) drive the contour error to zero to maximize quality and (2) maintain a constant cutting force to maximize productivity. These goals are systematically propagated to the bottom level, via aggregation relationships between the top and bottom-level states, and combined with the bottom-level goals of tracking reference servomechanism positions. A single controller is designed at the bottom level, where the physical control signals reside, that simultaneously meets both the top and bottom-level goals. The hierarchical optimal control methodology is extended to account for variations in force process model parameters and process parameters. Simulations are conducted for four machining operations that validate the developed methodology. The results illustrate the controller can simultaneously achieve both the top and bottom-level goals.     

Contouring controller design based on iterative contour error estimation for three-dimensional machining

Recently, there has been a growth of interest in high precision machining in multi-axis feed drive systems, subjected to problems such as friction, cutting force and incompatibility of individual driving axis dynamics. To guarantee high precision machining in modern computer numerical controlled (CNC) machines, CNC's controllers do its control efforts to reduce contour error. One of the common approaches is to design a controller based on the estimation of contour error in real time. However, for complex contours with severe curvatures, there is a lack of effective algorithms to calculate contour errors accurately. To address this problem, this paper proposes an accurate contour error estimation procedure for three-dimensional machining tasks. The proposed method is based on an iterative estimation of the instantaneous curvature of the reference trajectory and coordinates transformation approach, and hence, it is effective for complex reference trajectories with high curvatures. In addition, contour error controller is presented to reduce the estimated contour error. The feasibility and superiority of the proposed model as well as contour error controller are demonstrated through experimental system using a desk-top three-axis CNC machine.     

Estimation of tool orientation contour errors for five-axismachining

Recently, there has been a growth of interest in high-precision machining in multi-axis feed drive systems, which are subject to problems such as friction, cutting force and incompatibility of individual driving axis dynamics. Tracking errors in an individual driving axis during five-axis machining result in tool tip contour error and tool orientation contour error. Based on the conventional definition of the tool orientation contour error, that is, it is the deviation in the normal direction from the desired orientation in spherical coordinates, even if the tool tip and tool orientation contour errors are very small, a mismatch between the tool tip position and tool orientation causes an overcut or undercut when these errors are treated independently. To address this problem of mismatch, this paper presents a new definition of the actual tool orientation contour error. This definition considers synchronization between the tool tip and tool orientation contour errors. In addition, we propose an estimation model for the tool orientation contour error. Experimental results demonstrate that the proposed model provides a better indication of the actual tool orientation contour error than the conventional definition.     

Design and application of a sliding mode controller for accurate motion synchronization of dual servo systems

This paper presents a continuous time sliding mode controller (SMC) design to deal with the problem of motion synchronization in dual spindle servo systems. Synchronization error is defined as the differential position error between the two servo drives that follow identical reference motion trajectory. Proposed SMC controller penalizes three error states; namely individual axis tracking errors and the synchronization error for accurate synchronization. The control law is derived from Lyapunov energy function without switching condition. The controller shows robust motion synchronization against disturbances and parameter variations. Proposed SMC control is implemented in conventional double-sided machining operation.     

多模型自适应控制的分层递阶构造与覆盖性质分析

针对一类非最小相位系统,设计一种多模型自适应控制器.该控制器由固定控制器模型、常规自适应模型和可重新赋值自适应模型构成.固定控制器模型采用分层递阶结构用来减少模型集的数目,根据切换指标选出的上一层最优控制器,动态设计本层固定控制器模型实现对其参数变化范围的覆盖.该控制器采用直接自适应算法,通过加权多项式的选取,消除了稳态误差.文末对系统的覆盖性、模型数目等进行了分析.仿真结果表明当采用相同数目的模型时,其控制效果明显优于常规多模型控制器.     

Contour error reduction for free-form contour following tasks of biaxial motion control systems

In modern machining applications, reduction of contour error is an important issue concerning multi-axis contour following tasks. One popular approach to this problem is the cross-coupled controller (CCC). By exploiting the structure of CCC, an integrated control scheme is developed in this paper and an in-depth investigation on the issue of contour error reduction is also conducted. The proposed motion control scheme consists of a feedback controller, a feedforward controller, and a modified contour error controller (CCC equipped with a real-time contour error estimator). In addition, a fuzzy logic-based feedrate regulator is proposed to further reduce the contour error. The proposed feedrate regulator is designed based on the real-time estimated contour error and the curvature of the free-form parametric curves for machining. Several experiments are conducted to evaluate the performance of the proposed approach. Experimental results demonstrate the effectiveness of the proposed approach.     

A Synchronization Approach for the Minimization of Contouring Errors of CNC Machine Tools

This paper presents a synchronization control approach for the minimization of contouring errors of multi-axis CNC machine tools. The contouring errors are presented by the position synchronization errors that are defined as differential position errors between each axis and its adjacent ones. Using cross-coupling concept, a decentralized tracking controller is developed with feedback of both position and synchronization errors, formed with a combination of feedforward, feedback and a saturation control. It is proven that this controller can asymptotically stabilize both position and synchronization errors to zero. The proposed controller does not require significant use of the system dynamic models. Experiments performed on a multi-axis machine tool demonstrate improved performance especially in the contouring error minimization.     

Off-line feed rate scheduling using virtual CNC based on an evaluation of cutting performance

This paper presents an advanced technology to automatically determine optimum feed rates for 2 axis CNC machining, without requiring the expertise of a machinist or the information contained in a machining data handbook. Present CAM technology does not consider important physical properties such as cutting forces and machined surface errors. However, the virtual machining system developed in this study can simulate real machining for a given set of NC codes. An analytical model for off-line feed rate scheduling is formulated to improve productivity and machining accuracy. Using this model, it is possible to regulate the cutting force, which drastically improves the overall form accuracy of the machined surface.     

Neural Network Force Control for Industrial Robots

In this paper, we present a hierarchical force control framework consisting of a high level control system based on neural network and the existing motion control system of a manipulator in the low level. Inputs of the neural network are the contact force error and estimated stiffness of the contacted environment. The output of the neural network is the position command for the position controller of industrial robots. A MITSUBISHI MELFA RV-M1 industrial robot equipped with a BL Force/Torque sensor is utilized for implementing the hierarchical neural network force control system. Successful experiments for various contact motions are carried out. Additionally, the proposed neural network force controller together with the master/slave control method are used in dual-industrial robot systems. Successful experiments are carried out for the dual-robot system handling an object.     

Comparison of PI and MPC for control of a gas recovery unit

This study compares PI and MPC controls via a computer simulation for a gas recovery unit (GRU), which consists of three distillation columns operated in series: a de-ethanizer, a depropanizer and a debutanizer. In addition, the de-ethanizer feed is preheated by the bottoms product from the de-ethanizer, which causes additional process coupling. Rigorous models are developed for the columns including column pressure dynamics and heat transfer dynamics. The process is a highly coupled system and has interactive constraints that determine the feasible operating regions. A decentralized PI control system with override controls for the constraints was designed and implemented on the GRU simulator and was compared with an industrial MPC controller. The MPC controller was observed to outperform the decentralized control system due to its multivariable constraint control capability. Since the simulator is available to other university researchers, it can serve as a challenge problem for multivariable control and identification. Three MPC controllers with different strategies for controlling the bottom level of the first column were implemented on the GRU process. The first MPC controller does not directly control the level, the second one moves the setpoint to the PI level controller, and the third one controls the level directly by manipulating the flow. The results show that including level into the MPC controller improves composition control for cases in which the manipulated variable for the level control has a significant impact on compositions.     

Decentralized and centralized model predictive control to reduce the bullwhip effect in supply chain management

Mitigating the bullwhip effect is one of crucial problems in supply chain management. In this research, centralized and decentralized model predictive control strategies are applied to control inventory positions and to reduce the bullwhip effect in a benchmark four-echelon supply chain. The supply chain under consideration is described by discrete dynamic models characterized by balance equations on product and information flows with an ordering policy serving as the control schemes. In the decentralized control strategy, a MPC-EPSAC (Extended Prediction Self-Adaptive Control) approach is used to predict the changes in the inventory position levels. A closed-form solution of an optimal ordering decision for each echelon is obtained by locally minimizing a cost function, which consists of the errors between predicted inventory position levels and their setpoints, and a weighting function that penalizes orders. The single model predictive controller used in centralized control strategy optimizes globally and finds an optimal ordering policy for each echelon. The controller relies on a linear discrete-time state-space model to predict system outputs. But the predictions are approached by either of two multi-step predictors depending on whether the states of the controller model are directly observed or not. The objective function takes a quadratic form and thus the resulting optimization problem can be solved via standard quadratic programming method. The comparisons on performances of the two MPC strategies are illustrated with a numerical example.     

Decentralized adaptive iterative learning control for interconnected systems with uncertainties

In many applications,the system dynamics allows the decomposition into lower dimensional subsystems with interconnections among them.This decomposition is motivated by the ease and flexibility of the controller design for each subsystem.In this paper,a decentralized model reference adaptive iterative learning control scheme is developed for interconnected systems with model uncertainties.The interconnections in the dynamic equations of each subsystem are considered with unknown boundaries.The proposed controller of each subsystem depends only on local state variables without any information exchange with other subsystems.The adaptive parameters are updated along iteration axis to compensate the interconnections among subsystems.It is shown that by using the proposed decentralized controller,the states of the subsystems can track the desired reference model states iteratively.Simulation results demonstrate that,utilizing the proposed adaptive controller,the tracking error for each subsystem converges along the iteration axis.     

严格反馈互联系统分散式backstepping自适应迭代学习控制

基于Lyapunov分析方法,针对具有严格反馈形式的非线性互联系统,本文设计了一种分散式backstepping自适应迭代学习控制器.子系统之间的互联项为所有子系统输出项线性有界,为每个子系统设计的控制器仅采用该子系统的信息,不需要子系统之间相互传递信息.在控制器中,引入在时间轴和迭代轴上同时更新的自适应参数,以补偿子系统之间的互联项影响.通过采用本文给出的控制器,可使得每个子系统的输出跟踪相应的参考模型输出,仿真结果验证了本文算法的有效性.     

Robust adaptive motion/force tracking control design for uncertain constrained robot manipulators

In the presence of uncertain constraint and robot model, an adaptive controller with robust motion/force tracking performance for constrained robot manipulators is proposed. First, robust motion and force tracking is considered, where a performance criterion containing disturbance and estimated parameter attenuations is presented. Then the proposed controller utilizes an adaptive scheme and an auxiliary control law to deal with the uncertain environmental constraint, disturbances, and robotic modeling uncertainties. After solving a simple linear matrix inequality for gain conditions, the effect from disturbance and estimated parameter errors to motion/force errors is attenuated to an arbitrary prescribed level. Moreover, if the disturbance and estimated parameter errors are square-integrable, then an asymptotic motion tracking is achieved while the force error is as small as the inversion of control gain. Finally, numerical simulation results for a constrained planar robot illustrate the expected performance.     

A precise robust fuzzy control of robots using voltage control strategy

Fuzzy control of robot manipulators with a decentralized structure is facing a serious challenge. The state-space model of a robotic system including the robot manipulator and motors is in non-companion form, multivariable, highly nonlinear, and heavily coupled with a variable input gain matrix. Considering the problem, causes and solutions, we use voltage control strategy and convergence analysis to design a novel precise robust fuzzy control (PRFC) approach for electrically driven robot manipulators. The proposed fuzzy controller is Mamdani type and has a decentralized structure with guaranteed stability. In order to obtain a precise response, we regulate a fuzzy rule which governs the origin of the tracking space. The proposed design is verified by stability analysis. Simulations illustrate the superiority of the PRFC over a proprotional derivative like (PD-like) fuzzy controller applied on a selective compliant assembly robot arm (SCARA) driven by permanent magnet DC motors.     

Robust computationally efficient control of cooperative closed-chain manipulators with uncertain dynamics

This article presents a decentralized control scheme for the complex problem of simultaneous position and internal force control in cooperative multiple manipulator systems. The proposed controller is composed of a sliding mode control term and a force robustifying term to simultaneously control the payload's position/orientation as well as the internal forces induced in the system. This is accomplished independently of the manipulators dynamics. Unlike most controllers that do not require prior knowledge of the manipulators dynamics, the suggested controller does not use fuzzy logic inferencing and is computationally inexpensive. Using a Lyapunov stability approach, the controller is proven to be robust in the face of varying system's dynamics. The payload's position/orientation and the internal force errors are also shown to asymptotically converge to zero under such conditions.     

Multimodal Data fusion for SRGPS antenna motion error reduction

Antenna motion is a primary fault that degrades the integrity of shipboard relative GPS (SRGPS) systems, so we must investigate how to monitor and mitigate its impacts. Previous work proposed a single-baseline mode, but it had obvious problems, such as large motion errors, error measurement difficulties, and poor reliability. To solve these issues, we propose a multiple reference station architecture that deploys more than one reference station at different positions on ship. The observations are first translated to the ship reference point, and then integrated into a comprehensive measurement that accounts for the overall antenna motion errors. We consider two integrating methods: direct and weighted average methods. The direct average method is simple and intuitive, but the weighted average method more effectively reduced the overall antenna motion error by using the reciprocal standard deviation of historical measurements as weighting factors. All the antennas are mounted on the same ship body, so their correlations should also be considered when determining weighting factors. We used the ranking scores of the antennas derived using a graph-learning algorithm and fused them with the standard deviations to create new weighting factors. Finally, our experimental results demonstrated that the weighting factors based on the standard deviations and ranking scores reduced the overall antenna-motion error variance and improved the system integrity.

     

REAL‐TIME IMPLEMENTATION OF A DECENTRALIZED CONTROL FOR AN OPEN IRRIGATION CANAL PROTOTYPE

This paper presents a real‐time implementation of a decentralized LQG controller to regulate the downstream levels at the end of the pools in a four‐pool open irrigation canal prototype with an upstream control concept. The objective of the controller is to keep the downstream level at a constant target value in despite of flow disturbances. Controller synthesis uses a “black box” input‐output identified linear model. A previous interaction analysis, via Relative Gain Array “RGA”, carried on the process model was made to verify the feasibility to design a decentralized control. The real‐time close‐loop results show satisfactory performance and they are compared with those obtained with a centralized LQG controller.     

Cooperative energy management of automated vehicles

This paper presents a cooperative adaptive cruise controller that controls vehicles along a planned route in a possibly hilly terrain, while keeping safe distances among the vehicles. The controller consists of two predictive layers that may operate with different update frequencies, horizon lengths and model abstractions. The top layer plans kinetic energy in a centralized manner by solving a quadratic program, whereas the bottom layer optimizes gear in a decentralized manner by solving a dynamic program. The efficiency of the proposed controller is shown through several case studies with different horizon lengths and number of vehicles in the platoon.     

A LADRC based fuzzy PID approach to contour error control of networked motion control system with time‐varying delays

This paper investigate the contour error control problem for networked multi‐axis motion system (NMAMS) with time‐varying delays. Firstly, the uncertainties induced by the delays are modeled as a part of the total disturbance, and a linear active disturbance rejection controller (LADRC) is designed for the uniaxial trajectory tracking control. In the LADRC, a linear extended state observer (LESO) is designed to estimate the system state and the total disturbance simultaneously, and the effect of the total disturbance is eliminated by the designed linear feedback error control law. Then, the classical contour error estimation method is adopted, and a fuzzy PID controller is designed to compensate the contour error to achieve a better contour tracking performance. Finally, experiments are provided to verify the effectiveness of the proposed method.