Improving vehicle performance using adaptive control techniques

This paper reviews a collaborative research programme aimed at improving vehicle performance using adaptive control techniques. Initially the design of active suspension systems is considered, and the benefits of using a non-linear controller model with an adaptive control scheme are discussed. Adaptive schemes for active roll control are then considered, and the merits of incorporating a Smith predictor to accommodate for system delays are high-lighted. Preliminary research in adaptive cruise control and collision avoidance is discussed and plans for further developments are outlined. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, 19–21 January 1998     

Automated part programming for CNC milling by artificial intelligence techniques

This paper discusses research which has led to a working program based on artificial intelligence techniques for automatically writing a part program for milling. The paper discusses the approach used and gives details of the implementation. The input to the program is the graphic representation of the part (a drawing), and user-defined items such as tool details, material type, and so on. The program has an initial state, the shape of the raw material, and a goal state, the shape of the part. The program solves the problem of achieving the goal state from the initial state by using machining moves, and hence writes the part program. The current implementation produces a part program for a 2 $\text{1}\text{2}\text{-}\text{dimensional}$ part on a 3-axis CNC milling machine.     

立铣加工过程中的微元铣削力建模分析

针对单纯通过高速切削技术制造某些大型飞机零件过程中,存在难以控制的加工变形和较强表面残余拉应力分布等突出问题,以直齿和螺旋齿立铣加工过程为研究对象,基于微元切削机理,通过对刀齿铣削过程的分析,建立动态铣削加工仿真模型,导出铣削面积、铣削力、主轴扭矩、铣削功率与切削用量的关系.数值模拟结果与试验值较吻合,表明该模型可以实现动态铣削力预测,优化切削用量.     

Power grasp force distribution control using artificial neural networks

In this article, methods for force distribution control of power grasp are developed. A power grasp is characterized by multiple points of contact between the object grasped and the surfaces of the fingers and palm. The grasp is highly stable because of form closure. However, modeling power grasps is difficult because of the resulting closed kinematic structure and the complexity of multiple contacts. The first method used to obtain the desired force distribution is based on linear programming. In particular, a model of the DIGITS grasping system, under development at The Ohio State University, is used, and constraint equations are formulated for force balance and actuator torque limits. Supervisory control of the desired forces at the contacts is achieved by prescribing a desired clinch level. The objective function is designed to achieve the desired clinch level, except in cases where the specified force is inadequate to stably hold the object. Although this method yields the desired force distribution, a second method based on artificial neural networks (ANNs) is developed to achieve constant-time solutions. Linear programming solutions are used to generate training data for a set of ANNs. Two techniques, modular networks and adaptive slopes, are also developed and employed in the training to improve the training time and accuracy of the ANNs. The results show that the ANNs learn the appropriate nonlinear mapping for the force distribution and provide stable grasp over a wide range of object sizes and clinch levels.     

Modeling and control of non-linear systems using soft computing techniques

This work is an attempt to illustrate the utility and effectiveness of soft computing approaches in handling the modeling and control of complex systems. Soft computing research is concerned with the integration of artificial intelligent tools (neural networks, fuzzy technology, evolutionary algorithms, …) in a complementary hybrid framework for solving real world problems. There are several approaches to integrate neural networks and fuzzy logic to form a neuro-fuzzy system. The present work will concentrate on the pioneering neuro-fuzzy system, Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is first used to model non-linear knee-joint dynamics from recorded clinical data. The established model is then used to predict the behavior of the underlying system and for the design and evaluation of various intelligent control strategies.     

Monitored robust force control of a milling process

This paper focuses on the important problems of tool wear or flexion that induce defects in the part. The contributions of the main control strategies are discussed. A monitored robust force control strategy is specified by a hybrid automaton. The force controller is designed from an identified plant model by a robust pole placement method based on iterative shaping of the input–output sensitivity functions. Two estimates of the cutting mean force based on the l₁ and l_∞ norms are proposed to take the great variations of the measured forces issued from the multiple teeth engaged in the part into account. The monitored robust force control strategy was implemented in a platform of experiments to evaluate two scenarios of experiments showing a good way to get more efficient performances for the monitored control strategy.     

Forming processes control by means of artificial intelligence techniques

Forming processes are manufacturing processes that use force and pressure in order to modify the shape of a material part until obtaining the final product. The wide range of non-linear factors that drive this sort of processes make them very complex and extremely difficult to be controlled. Traditional control techniques, like PID controllers, have not offered a reliable solution when global control has been pursued and the figure of the operator still remains present in most of the forming facilities. On the other hand, although operators have demonstrated to be a very successful strategy when controlling this type of processes, the actual market evolution towards the fabrication of more complex parts, made of lower formability materials at higher production rates, is decreasing their capacity of reaction when solving the daily problems. Thus, the development of new global control systems based not on traditional control techniques and mathematical models but on the control strategy that has been used successfully for many years, the control through the experience and knowledge is now even more necessary. In the present work, an intelligent control system based on one of the main techniques within the artificial intelligence, expert systems, has been developed. The main purpose of this intelligent control system is to emulate the decisions that expert operators take but in a quicker and more reliable way. The developed intelligent control system has been installed in a blanking facility and very good results have been achieved.     

Modeling and control of an atomic force microscope using a piezoelectric tuning fork for force sensing

This paper investigates modeling and control issues associated with an atomic force microscope which uses a piezoelectric tuning fork for atomic force sensing. In the modeling part, the dynamics of piezoelectric tuning fork and its atomic interaction with the test sample via the scanning tip are physically characterized. The modeling results explain not only the atomic force sensing mechanism but also the important characteristics observed in experimental frequency responses. In the control part, an LTR controller is designed to maximize the controller bandwidth and yet maintain robustness against unmodeled dynamics and different operating conditions. Scanning results indicate that the LTR controller exhibits superior performance than a conventional PI controller.     

Two-stage adaptive robot position/force control using fuzzy reasoning and neural networks

Many studies have been performed on the position/force control of robot manipulators. Since the desired position and force required to realize certain tasks are usually designated in the operational space, the controller should adapt itself to an environment and generate the control force vector in the operational space. On the other hand, the friction of each joint of a robot manipulator is a serious problem since it impedes control accuracy. Therefore, the friction should be effectively compensated for in order to realize precise control of robot manipulators. Recently, soft computing techniques (fuzzy reasoning, neural networks and genetic algorithms) have been playing an important role in the control of robots. Applying the fuzzy-neuro approach (a combination of fuzzy reasoning and neural networks), learning/adaptation ability and human knowledge can be incorporated into a robot controller. In this paper, we propose a two-stage adaptive robot manipulator position/force control method in which the uncertain/unknown dynamic of the environment is compensated for in the task space and the joint friction is effectively compensated for in the joint space using soft computing techniques. The effectiveness of the proposed control method was evaluated by experiments.     

Decentralized adaptive coordinated control of multiple robot arms without using a force sensor

This paper presents a distributed adaptive coordinated control method for multiple robot arms grasping a common object. The cases of rigid contact and rolling contact are analyzed. In the proposed controller, the dynamic parameters of both object and robot arms are estimated adaptively. The desired motions of the robot arms are generated by an estimated object reference model. The control method requires only the measurements of the positions and velocities of the object and robot arms, but not the measurements of forces and moments at contact points. The asymptotic convergence of trajectory is proven by the Lyapunov-like Lemma. Experiments involving two robot arms handling a common object are shown.     

Nonlinear control techniques for an atomic force microscope system

Two nonlinear control techniques are proposed for an atomic force microscope system. Initially, a learning-based control algorithm is developed for the microcantilever-sample system that achieves asymptotic cantilever tip tracking for periodic trajectories. Specifically, the control approach utilizes a learning-based feedforward term to compensate for periodic dynamics and high-gain terms to account for non-periodic dynamics. An adaptive control algorithm is then developed to achieve asymptotic cantilever tip tracking for bounded tip trajectories despite uncertainty throughout the system parameters. Simulation results are provided to illttstrate the efficacy and performance of the control strategies.     

Nonlinear control techniques for an atomic force

Two nonlinear control techniques are proposed for an atomic force microscope system. Initially ,a learning- based control algorithm is developed for the microcantilever-sample system that achieves asymptotic cantilever tip tracking for periodic trajectories. Specifically ,the control approach utilizes a learning- based feedforward term to compensate for periodic dynamics and high- gain terms to account for non-periodic dynamics. An adaptive control algorithm is then developed to achieve asymptotic cantilever tip tracking for bounded tip trajectories despite uncertainty throughout the system parameters. Simulation results are provided to illustrate the efficacy and performance of the control strategies.     

Research on adaptive power control parameter of a cold milling machine

The factors in the power consumption of the cold milling machine and the load feature of the engine are firstly analyzed. How to select the optimal control parameter to make good use of the engine power output is studied theoretically. Based on these analyses, a simulation model of the cold milling machine is built up with the software of AMEsim4.2. Whether the engine power output utilization of the cold milling machine which is equipped with the adaptive power control system is reasonable or not is verified through the simulation.The results show that using the machine speed of the cold milling machine as the control parameter is reasonable and effective; the engine of the cold milling machine can be self-adjusted to work at its rated power by the adaptive power control system. In this case, the rated power utilization ratio of the engine is improved more than 10.2% and the machine efficiency increases at least 6.32% in various operating conditions.The theoretical basis and novel practical methodology can be provided for making good use of the engine power and increasing the machine work efficiency.     

Neural control strategy of constant cutting force system in end milling

This paper discusses the application of neural adaptive control strategy to the problem of cutting force control in high speed end milling operations. The research is concerned with integrating adaptive control and a standard computer numerical controller (CNC) for optimizing a metal-cutting process. It is designed to adaptively maximize the feed rate subject to allowable cutting force on the tool, which is very beneficial for a time consuming complex shape machining. The purpose is to present a reliable, robust neural controller aimed at adaptively adjusting feed rate to prevent excessive tool wear, tool breakage and maintain a high chip removal rate. Numerous simulations and experiments are conducted to confirm the efficiency of this architecture.     

Achieving high-precision laparoscopic manipulation through adaptive force control

In this paper, we present a new solution to laparoscopic manipulation based on forcefeedback control. This method allows us to both explicitely control the forces applied to the patient through the trocar and to precisely control the position of the surgical instrument. It does not require any geometrical model of the operative environment nor any fine robot base placement prior to the instrument insertion. Different adaptive control strategies involving different kinds of sensory equipments are proposed. These strategies are experimentally validated on a laboratory apparatus. An experiment is also presented where a laparoscope held by the robot's arm tracks a target through visual servoing.     

Human-machine interaction force control: using a model-referenced adaptive impedance device to control an index finger exoskeleton

Exoskeleton robots and their control methods have been extensively developed to aid post-stroke rehabilitation. Most of the existing methods using linear controllers are designed for position control and are not suitable for human-machine interaction （HMI） force control, as the interaction system between the human body and exoskeleton is uncertain and nonlinear. We present an approach for HMI force control via model reference adaptive impedance control （MRAIC） to solve this problem in case of index finger exoskeleton control. First, a dynamic HMI model, which is based on a position control inner loop, is for- mulated. Second, the theoretical MRAC framework is implemented in the control system. Then, the adaptive controllers are designed according to the Lyapunov stability theory. To verify the performance of the proposed method, we compare it with a proportional-integral-derivative （PID） method in the time domain with real experiments and in the frequency domain with simu- lations. The results illustrate the effectiveness and robustness of the proposed method in solving the nonlinear HMI force control problem in hand exoskeleton.     

Modeling and adaptive coordination control of a two-Robot system

Multiple robots are usually required in a flexible manufacturing system or a complex working environment. In particular, when an object under processing is too big or too heavy, a single robot is insufficient to handle it. Two robots are applicable in such case. This article aims to develop a complete mathematical model and an adaptive controller for two robots carrying a common load. It will be shown that the dynamic model of the two-robot system turns out to be a singular system, taking into account the object dynamics. The condition for which the system model holds is also discussed. The adaptive controller will be used to overcome uncertainties in the object dynamics and robots. The distributed forces in the robot end effectors are determined by an optimal criterion. It will be shown that the adaptive controller surpasses the conventional computed torque controller.     

Adaptive hybrid force/position control of robot manipulators using an adaptive force estimator in the presence of parametric uncertainty

This study is devoted to sensorless adaptive force/position control of robot manipulators using a position-based adaptive force estimator (AFE) and a force-based adaptive environment compliance estimator. Unlike the other sensorless method in force control that uses disturbance observer and needs an accurate model of the manipulator, in this method, the unknown parameters of the robot can be estimated along with the force control. Even more, the environment compliance can be estimated simultaneously to achieve tracking force control. In fact, this study deals with three challenging problems: No force sensor is used, environment stiffness is unknown, and some parametric uncertainties exist in the robot model. A theorem offers control laws and updating laws for two control loops. In the inner loop, AFE estimates the exerted force, and then, the force control law in the outer loop modifies the desired trajectory of the manipulator for the adaptive tracking loop. Besides, an updating law updates the estimated compliance to provide an accurate tracking force control. Some experimental results of a PHANToM Premium robot are provided to validate the proposed scheme. In addition, some simulations are presented that verify the performance of the controller for different situations in interaction.     

Mechanistic modelling of the milling process using an adaptive depth buffer

A mechanistic model of the milling process based on an adaptive and local depth buffer is presented. This mechanistic model is needed for speedy computations of the cutting forces when machining surfaces on multi-axis milling machines. By adaptively orienting the depth buffer to match the current tool axis, the need for an extended Z-buffer is eliminated. This allows the mechanistic model to be implemented using standard graphics libraries, and gains the substantial benefit of hardware acceleration. Secondly, this method allows the depth buffer to be sized to the tool as opposed to the workpiece, and thus improves the depth buffer size to accuracy ratio drastically. The method calculates tangential and radial milling forces dependent on the in-process volume of material removed as determined by the rendering engine depth buffer. The method incorporates the effects of both cutting and edge forces and accounts for cutter runout. The simulated forces were verified with experimental data and found to agree closely. The error bounds of this process are also determined.