Controller design and implementation for industrial robots withflexible joints

A control strategy which consists of feedforward and feedback compensation loops is proposed to improve the performance of industrial robots. The feedforward loop is similar to the usual inverse-dynamics compensation. The feedback control loop uses a frequency-domain optimal controller. The design starts from the single-link case and is extended to the control of multilinkage flexible-joint robots. An experimental system consisting of a single-link robot is constructed for verifying the proposed control strategies. Experiments show good performance of the proposed control strategy in stiffening the flexible joint and in tracking desired polynomial-type trajectories     

A robust distributed controller of a single-link SCARA⧹Cartesian smart materials robot

In this paper, dynamic modelling and controller design are presented for a SCARA⧹Cartesian smart materials robot, a flexible SCARA⧹Cartesian robot bonded with piezoelectric actuators and sensors for better control performance. Conventionally, controller design is usually carried out for a truncated model of partial differential equations (PDEs). In this paper, a novel distributed controller is developed directly based on PDEs, which can guarantee the globally exponential stability of the closed-loop system. Taking into account of bounded disturbances, a robust distributed controller is further developed and stability proof is also given. Different from the conventional approach, truncation is introduced at the stage of the controller implementation. Stability proofs show that the proposed controller can also stabilize the finite dimensional model with arbitrary number of flexible modes. Both simulation and experiment results verify that the robust controller can achieve good performance in the suppression of residual vibrations under the environment of disturbances.     

Vibration control of a single-link flexible manipulator using an array of fiber optic curvature sensors and PZT actuators

This paper presents a novel distributed sensing and actuation approach for actively suppressing vibrations within flexible link manipulators. Through vibration suppression, the method acts to regulate the shape of flexible links and, consequently, improves the performance of any independent trajectory controller being employed over the manipulator joints. To demonstrate the approach, a series of piezoceramic actuators (PZTs) are bonded to the surface of a single-link flexible manipulator. Slewing of the flexible link induces vibrations in the link that persist long after the hub stops rotating. The vibration suppression is achieved through a combined scheme of PD-based hub motion control and a PZT actuator controller that is a composite of linear and angular velocity feedback controllers. A Lyapunov approach is used to synthesize the composite controller, and a unique, commercially-available sensor, called ShapeTape^?, that provides the linear and angular velocity feedback. The sensor array is comprised of a series of fiber optic curvature sensors that are laminated on a long, thin ribbon tape which can be embedded into the flexible link and measures the bend and twist of the link’s centerline. Simulation and experimental results show the effectiveness of the proposed approach and the ability of the new sensor to provide the requisite feedback.     

Robust Iterative Learning Control Design: Application to a Robot Manipulator

This paper deals with robust iterative learning control design for uncertain single-input-single-output linear time-invariant systems. The design procedure is based upon solving the robust performance condition using the Youla parameterization and the mu-synthesis approach to obtain a feedback controller. Thereafter, a convergent iterative learning law is obtained by using the performance weighting function involved in the robust performance condition. Experimental results, on a CRS465 robot manipulator, are provided to illustrate the effectiveness of the proposed design method.     

Optimal and stable fuzzy controllers for nonlinear systems based on an improved genetic algorithm

This paper addresses the optimization and stabilization problems of nonlinear systems subject to parameter uncertainties. The methodology is based on a fuzzy logic approach and an improved genetic algorithm (GA). The TSK fuzzy plant model is employed to describe the dynamics of the uncertain nonlinear plant. A fuzzy controller is then obtained to close the feedback loop. The stability conditions are derived. The feedback gains of the fuzzy controller and the solution for meeting the stability conditions are determined using the improved GA. In order to obtain the optimal fuzzy controller, the membership functions are further tuned by minimizing a defined fitness function using the improved GA. An application example on stabilizing a two-link robot arm will be given.     

Hybrid learning control schemes with input shaping of a flexible manipulator system

The paper describes a practical approach to investigate and develop a hybrid iterative learning control scheme with input shaping. An experimental flexible manipulator rig and corresponding simulation environment are used to demonstrate the effectiveness of the proposed control strategy. A collocated proportional-derivative (PD) controller utilizing hub-angle and hub-velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate iterative learning control with acceleration feedback and genetic algorithms (GAs) for optimization of the learning parameters and a feedforward controller based on input shaping techniques for control of vibration (flexible motion) of the system. The system performance with the controllers is presented and analysed in the time and frequency domains. The performance of the hybrid learning control scheme with input shaping is assessed in terms of input tracking and level of vibration reduction. The effectiveness of the control schemes in handling various payloads is also studied.     

Hybrid fuzzy proportional-integral plus conventional derivativecontrol of linear and nonlinear systems

This paper presents a new approach toward the optimal design of a hybrid proportional-integral-derivative (PID) controller applicable for controlling linear as well as nonlinear systems using genetic algorithms (GAs). The proposed hybrid PID controller is derived by replacing the conventional PI controller by a two-input normalized linear fuzzy logic controller (FLC) and executing the conventional D controller in an incremental form. The salient features of the proposed controller are as follows: (1) the linearly defined FLC can generate nonlinear output so that high nonlinearities of complex systems can be handled; (2) only one well-defined linear fuzzy control space is required for both linear and nonlinear systems; (3) optimal tuning of the controller gains is carried out by using a GA; and (4) it is simple and easy to implement. Simulation results on a temperature control system (linear system) and a missile model (nonlinear system) demonstrate the effectiveness and robustness of the proposed controller     

Intelligent optimal control of single-link flexible robot arm

This paper addresses the design and properties of an intelligent optimal control for a nonlinear flexible robot arm that is driven by a permanent-magnet synchronous servo motor. First, the dynamic model of a flexible robot arm system with a tip mass is introduced. When the tip mass of the flexible robot arm is a rigid body, not only bending vibration but also torsional vibration are occurred. In this paper, the vibration states of the nonlinear system are assumed to he unmeasurable, i.e., only the actuator position can be acquired to feed into a suitable control system for stabilizing the vibration states indirectly. Then, an intelligent optimal control system is proposed to control the motor-mechanism coupling system for periodic motion. In the intelligent optimal control system a fuzzy neural network controller is used to learn a nonlinear function in the optimal control law, and a robust controller is designed to compensate the approximation error. Moreover, a simple adaptive algorithm is proposed to adjust the uncertain bound in the robust controller avoiding the chattering phenomena. The control laws of the intelligent optimal control system are derived in the sense of optimal control technique and Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. In addition, numerical simulation and experimental results are given to verify the effectiveness of the proposed control scheme.     

A neural-network-based controller for a single-link flexiblemanipulator using the inverse dynamics approach

This paper presents an intelligent-based control strategy for tip position tracking control of a single-link flexible manipulator. Motivated by the well-known inverse dynamics control strategy for rigid-link manipulators, two feedforward neural networks (NNs) are proposed to learn the nonlinearities of the flexible arm associated with the inverse dynamics controller. The redefined output approach is used by feeding back this output to guarantee the minimum phase behavior of the resulting closed-loop system. No a priori knowledge about the nonlinearities of the system is needed and the payload mass is also assumed to be unknown. The network weights are adjusted using a modified online error backpropagation algorithm that is based on the propagation of output tracking error, derivative of that error and the tip deflection of the manipulator. The real-time controller is implemented on an experimental test bed. The results achieved by the proposed NN-based controller are compared experimentally with conventional proportional-plus-derivative-type and standard inverse dynamics controls to substantiate and verify the advantages of our proposed scheme and its promising potential in identification and control of nonlinear systems     

Topology design and bandwidth allocation of embedded ATM networksusing genetic algorithm

A genetic algorithm (GA) is proposed for the design of an asynchronous transfer mode (ATM) network embedded in a digital cross connect network. This highly constrained optimization problem is shown to be better solved using GA. Improved results, both in network performance and computation speed, are obtained when comparing with the existing heuristic approach. A similar methodology can be applied for the design of other communication networks     

Structure Learning in Bayesian Networks Using Asexual Reproduction Optimization

A new structure learning approach for Bayesian networks based on asexual reproduction optimization (ARO) is proposed in this paper. ARO can be considered an evolutionary‐based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter, the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem: This leads to the fitter individual. The convergence measure of ARO is analyzed. The proposed method is applied to real‐world and benchmark applications, while its effectiveness is demonstrated through computer simulations. Results of simulations show that ARO outperforms genetic algorithm (GA) because ARO results in a good structure and fast convergence rate in comparison with GA.     

Passive filter design using genetic algorithms

The objective of this paper is to propose a new approach for designing a passive LC filter of the full-bridge rectifier by using genetic algorithms (GAs). The performance of the cost-effective passive LC filter for a constant load depends on the appropriate inductor and capacitor selection. Several design methods are reviewed and a novel design methodology using GAs is proposed in this paper. By using the proposed GA program, designer can quickly find appropriate parameter values to meet the desired circuit performance. Experimental results show that an appropriate combination of the inductor and capacitor selected by the proposed GA program can meet the desired power quality requirement. Different cases of design examples are shown in this paper to verify the performance of the proposed design methodology.     

A systematic design procedure of force controllers for industrialrobots

In this paper, the problem of designing a force controller for industrial robots with a positional interface is addressed. A systematic design procedure to compute structures and parameters of the controller is devised, to provide a useful tool for rapid and robust setup of force control at the industrial level. The proposed method for synthesis of the force controller simply requires technology parameters provided by the robot manufacturer and desired performance expressed in non-technical terms by the user. The automated design algorithm is described in detail and its effectiveness was proved by experiments on two different industrial robots. On the first robotic setup, the performance of the designed controllers was evaluated by analyzing the experimental results of responses to canonical reference signals; on the second setup, the controller reliability and applicability at the industrial level were demonstrated through the results of a mechanical parts mating task     

一种改进的遗传算法的实现及性能研究

在Visual C 环境下,用C语言实现了一种改进的遗传算法,该算法通过实数编码、改进的遗传操作算子,并引入精英保留策略,可克服常规算法中收敛速度慢、早熟及局部收敛等缺陷。经GA测试函数验证该算法是有效的。与MATLAB遗传算法优化工具箱和SGA的系统仿真结果进行了比较,结果显示该算法不仅显著提高了全局搜索能力,加快了收敛速度,而且也改善了求解的质量及其优化结果的可靠性,是求解复杂函数优化问题的一种有潜力的算法,结合自行研发的微波管CAD软件,可望用于微波管性能优化,提高微波管设计能力。     

Adaptive neural network based controller for robots

A new Adaptive Neural Network (ANN) controller for robot trajectory trackingproblem is developed. A novel and efficient training algorithm for the proposed controller ispresented in this paper. The proposed training algorithm is based on updating the weights of thenetwork each step by minimizing the quadrant tracking errors and their derivatives.A simulation study is carried out on a polar robot manipulator to assure the effectivenessof the proposed trajectory tracking robot control system. The effects of the new controllerparameters and noisy external load disturbances on the control performance are studied. Thesimulation results of the proposed adaptive ANN controller are compared with those of aconventional ANN controller. The obtained results assured the robustness of the proposed ANNcontroller for: (i) uncertainties of the robot arm dynamic model and/or parameters, (ii) variousnoisy external load disturbances. Also, the simulation results assure the effectiveness of theproposed adaptive ANN controller against the conventional ANN one.     

An FIR Equalizer Design for Nonlinear Channels Using Hybrid GA and LMI

In this paper, a finite impulse response (FIR) equalizer for nonlinear discrete-time channels is designed by employing a hybrid genetic algorithm (GA) and linear matrix inequality (LMI) approach from an H_∞ perspective. The GA technique is utilized to linearize the nonlinear channel model, and the approximate error can be viewed as a state uncertainty. Then, the design of the FIR equalizer is transformed into LMIs, and the coefficients of the FIR equalizer can be obtained by solving an LMI optimization problem. Finally, numerical examples are included to illustrate the effectiveness of the proposed methodology.     

Multiobjective optimization design of Yagi-Uda antenna

An optimization method, such as the steepest gradient methods, could not easily obtain globally optimum solutions for devising antenna design parameters that allow the antenna to simultaneously improve multiple performances such as gain, sidelobe level, and input impedance. The genetic algorithm (GA) is suitable for empirically solving optimization problems and is effective in designing an antenna. In particular, this method can solve the multiobjective optimization problem using various Pareto-optimal solutions in an extremely efficient manner. In this paper, the Pareto GA, by which various Pareto-optimal solutions for each objective function (performance) can be obtained in a single trial of a numerical simulation and which enables the selection of parameters in accordance with the design requirement, is applied to the multiobjective optimization design of the Yagi-Uda antenna. The effectiveness of the Pareto GA was demonstrated by comparing the performances obtained by the Pareto GA with those of the previously reported values, which were obtained by the conventional GA, and with the values of the design benchmark reference.     

Force control of a very lightweight single-link flexible arm based on coupling torque feedback

In this paper, a feedback control law is proposed for regulating the contact force exerted by a very lightweight single-link flexible manipulator when it comes into contact with a motionless object. This control law is based on a lumped-parameter model. The tracking of the desired contact force is obtained by using a feedback loop control of the coupling torque between the motor and the flexible arm. To achieve a good performance on the force control it is only necessary to measure the coupling torque at the root of the arm. Neither the contact force sensor nor the angular position sensor of the motor are used in the control method. A modified PID controller is proposed for this control law. In this work the force control problem is studied for both free and constrained motions of the flexible manipulator, and a collision detection algorithm is also described.     

Fault-tolerant control system of flexible arm for sensor fault by using reaction force observer

In recent years, control system reliability has received much attention with increase of situations where computer-controlled systems such as robot control systems are used. In order to improve reliability, control systems need to have abilities to detect a fault (fault detection) and to maintain the stability and the control performance (fault tolerance). In this paper, we address the vibration suppression control of a one-link flexible arm robot. Vibration suppression is realized by an additional feedback of a strain gauge sensor attached to the arm besides motor position. However, a sensor fault (e.g., disconnection) may degrade the control performance and make the control system unstable at its worst. In this paper, we propose a fault-tolerant control system for strain gauge sensor fault. The proposed control system estimates a strain gauge sensor signal based on the reaction force observer and detects the fault by monitoring the estimation error. After fault detection, the proposed control system exchanges the faulty sensor signal for the estimated one and switches to a fault-mode controller so as to maintain the stability and the control performance. We apply the proposed control system to the vibration suppression control system of a one-link flexible arm robot and confirm the effectiveness of the proposed control system by some experiments.