Fault Diagnosis in Robot Manipulators Using SVM and KNN

In this paper,Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based methods are to be applied on fault diagnosis in a robot manipulator.A comparative study between the two classifiers in terms of successfully detecting and isolating the seven classes of sensor faults is considered in this work.For both classifiers,the torque,the position and the speed of the manipulator have been employed as the input vector.However,it is to mention that a large database is needed and used for the training and testing phases.The SVM method used in this paper is based on the Gaussian kernel with the parametersγand the penalty margin parameter“C”,which were adjusted via the PSO algorithm to achieve a maximum accuracy diagnosis.Simulations were carried out on the model of a Selective Compliance Assembly Robot Arm(SCARA)robot manipulator,and the results showed that the Particle Swarm Optimization(PSO)increased the per-formance of the SVM algorithm with the 96.95%accuracy while the KNN algo-rithm achieved a correlation up to 94.62%.These results showed that the SVM algorithm with PSO was more precise than the KNN algorithm when was used in fault diagnosis on a robot manipulator.

CONTROLLING ROBOT MANIPULATORS BY DYNAMIC PROGRAMMING

A certain number of considerations should be taken into account in the dynamic control of robot manipulators as highly complex non-linear systems.In this article,we provide a detailed presentation of the mechanical and electrical impli- cations of robots equipped with DC motor actuators.This model takes into account all non-linear aspects of the system.Then,we develop computational algorithms for optimal control based on dynamic programming.The robot's trajectory must be predefined,but performance criteria and constraints applying to the system are not limited and we may adapt them freely to the robot and the task being studied.As an example,a manipulator arm with 3 degrees of freedom is analyzed.

Globally robust nonlinear PID controllers for robot manipulators with an uncertain Jacobian matrix

Based on a continuous piecewise-differentiable increasing functions vector, a class of robust nonlinear PID (RN-PID) controllers is proposed for setpoint control with uncertain Jacobian matrix. Globally asymptotic stability is guaranteed and only position and joint velocity measurements are required. And stability problem arising from integral action and integrator windup, are consequently resolved. Furthermore, RN-PID controllers can be of effective alternative for anti-integrator-wind-up, the control performance would not be very bad in the presence of rough parameter tuning.

Novel integrated optimization algorithm for trajectory planning of robot manipulators based on integrated evolutionary programming

Optimal trajectory planning for robot manipulators plays an important role in implementing the high productivity for robots. The performance indexes used in optimal trajectory planning are classified into two main categories: optimum traveling time and optimum mechanical energy of the actuators. The current trajectory planning algorithms are designed based on one of the above two performance indexes. So far, there have been few planning algorithms designed to satisfy two performance indexes simultaneously. On the other hand, some deficiencies arise in the existing integrated optimi2ation algorithms of trajectory planning. In order to overcome those deficiencies, the integrated optimization algorithms of trajectory planning are presented based on the complete analysis for trajectory planning of robot manipulators. In the algorithm, two object functions are designed based on the specific weight coefficient method and ' ideal point strategy. Moreover, based on the features of optimization problem, the intensified evolutionary programming is proposed to solve the corresponding optimization model. Especially, for the Stanford Robot,the high-quality solutions are found at a lower cost.

Robust scheme of global parallel force/position regulators for robot manipulators under environment uncertainty

A simple robust scheme of parallel force/position control is proposed in this paper to deal with two problems for non-planar constraint surface and nonlinear mechanical feature of environment： i） uncertainties in environment that are usually not available or difficult to be determined in most practical situations; ii） stability problem or/and integrator windup due to the integration of force error in the force dominance rule in parallel force/position control. It shows that this robust scheme is a good alternative for anti-windup. In the presence of environment uncertainties, global asymptotic stability of the resulting closed-loop system is guaranteed; it environment with complex characteristics. Finally, numerical robot manipulator. also shows robustness of the proposed controller to uncertain simulation verifies results via contact task of a two rigid-links

Robust Iterative Learning Controller for the Non-zero Initial Error Problem on Robot Manipulator

Industrial robot system is a kind of dynamic system w ith strong nonlinear coupling and high position precision. A lot of control ways , such as nonlinear feedbackdecomposition motion and adaptive control and so o n, have been used to control this kind of system, but there are some deficiencie s in those methods: some need accurate and some need complicated operation and e tc. In recent years, in need of controlling the industrial robots, aiming at com pletely tracking the ideal input for the controlled subject with repetitive character, a new research area, ILC (iterative learning control), has been devel oped in the control technology and theory. The iterative learning control method can make the controlled subject operate as desired in a definite time span, merely making use of the prior control experie nce of the system and searching for the desired control signal according to the practical and desired output signal. The process of searching is equal to that o f learning, during which we only need to measure the output signal to amend the control signal, not like the adaptive control strategy, which on line assesses t he complex parameters of the system. Besides, since the iterative learning contr ol relies little on the prior message of the subject, it has been well used in a lot of areas, especially the dynamic systems with strong non-linear coupling a nd high repetitive position precision and the control system with batch producti on. Since robot manipulator has the above-mentioned character, ILC can be very well used in robot manipulator. In the ILC, since the operation always begins with a certain initial state, init ial condition has been required in almost all convergence verification. Therefor e, in designing the controller, the initial state has to be restricted with some condition to guarantee the convergence of the algorithm. The settle of initial condition problem has long been pursued in the ILC. There are commonly two kinds of initial condition problems: one is zero initial error problem, another is non-zero initial error problem. In practice, the repe titive operation will invariably produce excursion of the iterative initial stat e from the desired initial state. As a result, the research on the second in itial problem has more practical meaning. In this paper, for the non-zero initial error problem, one novel robust ILC alg orithms, respectively combining PD type iterative learning control algorithm wit h the robust feedback control algorithm, has been presented. This novel robust ILC algorithm contain two parts: feedforward ILC algorithm and robust feedback algorithm, which can be used to restrain disturbance from param eter variation, mechanical nonlinearities and unmodeled dynamics and to achieve good performance as well. The feedforward ILC algorithm can be used to improve the tracking error and perf ormance of the system through iteratively learning from the previous operation, thus performing the tracking task very fast. The robust feedback algorithm could mainly be applied to make the real output of the system not deviate too much fr om the desired tracking trajectory, and guarantee the system’s robustness w hen there are exterior noises and variations of the system parameter. In this paper, in order to analyze the convergence of the algorithm, Lyapunov st ability theory has been used through properly selecting the Lyapunov function. T he result of the verification shows the feasibility of the novel robust iterativ e learning control in theory. Finally, aiming at the two-freedom rate robot, simulation has been made with th e MATLAB software. Furthermore, two groups of parameters are selected to validat e the robustness of the algorithm.

OPTIMAL DYNAMIC LOAD DISTRIBUTION OF MULTIPLE COOPERATING ROBOT MANIPULATORS

For the situation of multiple cooperating manipulators handling a single object,an equilibrium equation is presented in which the manipulator dynamics and control forces/torques are taken into account,and a expression is derived to allow the optimal dynamic load distribution of the combined system can be made.

Fractional synergetic tracking control for robot manipulator

This work takes advantage of synergetic control theory and fractional calculus to develop and propose fractional synergetic control(FSC)strategy for Four Degrees of Freedom(4-DOF)robot manipulator.The proposed fractional synergetic control is designed to track a joint space as well as workspace desired trajectories.Fractional calculus gives more flexibility in the design since it has a wider stability region.Added to that,as stated in the literature,compared to a similar approach such as sliding mode control,the synergetic control approach converges faster to the equilibrium point,without chattering with a fast response.This paper proposes a new control strategy that takes advantage of fractional calculus and synergetic control theory.This proposed control strategy is tested experimentally on a 4-DOF manipulator to study the performance of the proposed control scheme.The stability of the closed-loop system is proved using the Lyapunov approach.The experimental results have shown that the proposed FSC design has achieved a good tracking performance.

A Novel Disturbance Observer Based Fixed-Time Sliding Mode Control for Robotic Manipulators With Global Fast Convergence

This paper proposes a new global fixed-time sliding mode control strategy for the trajectory tracking control of uncertain robotic manipulators.First,a fixed-time disturbance observer(FTDO) is designed to deal with the adverse effects of model uncertainties and external disturbances in the manipulator systems.Then an adaptive scheme is used and the adaptive FTDO(AFTDO) is developed,so that the priori knowledge of the lumped disturbance is not required.Further,a new non-singular fast terminal sliding mode(NFTSM) surface is designed by using an arctan function,which helps to overcome the singularity problem and enhance the robustness of the system.Based on the estimation of the lumped disturbance by the AFTDO,a fixed-time non-singular fast terminal sliding mode controller(FTNFTSMC)is developed to guarantee the trajectory tracking errors converge to zero within a fixed time.The settling time is independent of the initial state of the system.In addition,the stability of the AFTDO and FTNFTSMC is strictly proved by using Lyapunov method.Finally,the fixed-time NFESM(FTNFTSM) algorithm is validated on a 2-link manipulator and comparisons with other existing sliding mode controllers(SMCs) are performed.The comparative results confirm that the FTNFTSMC has superior control performance.

Discrete-time based sliding-mode control of robot manipulators

Purpose-The purpose of this paper is to develop sliding mode control with linear and nonlinear manifolds in discrete-time domain for robot manipulators.Design/methodology/approach–First,a discrete linear sliding mode controller is designed to an n-link robot based on Gao’s reaching law.In the second step,a discrete terminal sliding mode controller is developed to design a finite time and high precision controller.The stability analysis of both controllers is presented in the presence of model uncertainties and external disturbances.Finally,sampling time effects on the continuous-time system outputs and sliding surfaces are discussed.Findings–Computer simulations on a three-link SCARA robot show that the proposed controllers are robust against model uncertainties and external disturbance.It was also shown that the sampling time has important effects on the closed loop system stability and convergence.Practical implications-The proposed controllers are low cost and easily implemented in practice in comparison with continuous-time ones.Originality/value-The novelty associated with this paper is the development of an approach to finite time and robust control of n-link robot manipulators in discrete-time domain.Also,obtaining an upper bound for the sampling time is another contribution of this work.

Takagi–Sugeno Fuzzy Modeling and Control for Effective Robotic Manipulator Motion

Robotic manipulators are widely used in applications that require fast and precise motion.Such devices,however,are prompt to nonlinear control issues due to the flexibility in joints and the friction in the motors within the dynamics of their rigid part.To address these issues,the Linear Matrix Inequalities(LMIs)and Parallel Distributed Compensation(PDC)approaches are implemented in the Takagy–Sugeno Fuzzy Model(T-SFM).We propose the following methodology;initially,the state space equations of the nonlinear manipulator model are derived.Next,a Takagy–Sugeno Fuzzy Model(T-SFM)technique is used for linearizing the state space equations of the nonlinear manipulator.The T-SFM controller is developed using the Parallel Distributed Compensation(PDC)method.The prime concept of the designed controller is to compensate for all the fuzzy rules.Furthermore,the Linear Matrix Inequalities(LMIs)are applied to generate adequate cases to ensure stability and control.Convex programming methods are applied to solve the developed LMIs problems.Simulations developed for the proposed model show that the proposed controller stabilized the system with zero tracking error in less than 1.5 s.

Robust H_∞ Control of Robot Manipulator

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FUZZY MODEL-FOLLOWING ADAPTIVE CONTROL DESIGN FOR ROBOT MANIPULATORS

The increasing demand on robotic system performance leads to the use of advanced con- trol strategies. This paper proposes a method of nonlinear feedback control introducing fuzzy infer- ence into model-following adaptive control for the nonlinear robot manipulator systems. The fuzzy inference is introduced to treat the nonlinearities of the control systems. Furthermore, the stability of the system is discussed by the fuzzy stability theory based on the Lyapunov's direct method. In the closed loop, the robotic system asymptotically converge to the reference trajectory with a pre- scribed transient response.

Adaptive Fuzzy Backstepping Tracking Control for Flexible Robotic Manipulator

In this paper,an adaptive fuzzy state feedback control method is proposed for the single-link robotic manipulator system.The considered system contains unknown nonlinearfunction and actuator saturation.Fuzzy logic systems(FLSs)and a smooth function are used to approximate the unknownnonlinearities and the actuator saturation,respectively.By com-bining the command-filter technique with the backsteppingdesign algorithm,a novel adaptive fuuzy tracking backsteppingcontrol method is developed.It is proved that the adaptive fuuzycontrol scheme can guarantee that all the variables in the closed-loop system are bounded,and the system output can track thegiven reference signal as close as possible.Simulation results areprovided to illustrate the effectiveness of the proposed approach.

Neural-network-based two-loop control of robotic manipulators including actuator dynamics in task space

A neural-network-based motion controller in task space is presented in this paper. The proposed controller is addressed as a two-loop cascade control scheme. The outer loop is given by kinematic control in the task space. It provides a joint velocity reference signal to the inner one. The inner loop implements a velocity servo loop at the robot joint level. A radial basis function network （RBFN） is integrated with proportional-integral （PI） control to construct a velocity tracking control scheme for the inner loop. Finally, a prototype technology based control system is designed for a robotic manipulator. The proposed control scheme is applied to the robotic manipulator. Experimental results confirm the validity of the proposed control scheme by comparing it with other control strategies.

ANALYTICAL MODEL ALGORITHM FOR DYNAMICS OF ROBOTIC MANIPULATORS

A simple analytical model method for dynamics of robotic manipulators is proposed.Problem of deriving model matrix elements is transformed into problem of solving for driving forceand driving torque under specified condition by recursive dynamic equations. Expressions of reaction force in arbitrary joint in numeric-symbolic form are also derived. The properties of modelmatrices are given. Corresponding software which can recognize and manipulate symbols is developed and can be used to generate model and real-time code of robotic dynamics.

Development of a Stereo Vision-based Pick and Place System for Robotic Manipulators

Conventional robotic manipulators consist of touch and vision sensors in order to pick and place differently shaped objects.Due to the technology development and degrading sensors over a long period,the stereo vision technique has become a promising alternative.In this study,a low-cost stereo vision-based system,and a gripper to be placed at the end of the robot arm(Fanuc M10 iA/12)are developed for position and orientation estimation of robotic manipulators to pick and place different shaped objects.The stereo vision system developed in this research is used to estimate the position(X,Y,Z),orientation(P_(y))of the Center of Volume of four standard objects(cube,cuboid,cylinder,and sphere)whereas the robot arm with the gripper is used to mechanically pick and place the objects.The stereo vision system is placed on the movable robot arm,and it consists of two cameras to capture two 2D views of a stationary object to derive 3D depth information in 3D space.Moreover,a graphical user interface is developed to train a linear regression model,live predict the coordinates of the objects,and check the accuracy of the predicted data.The graphical user interface can also send predicted coordinates and angles to the gripper and the robot arm.The project is facilitated with python programming language modules and image processing techniques.Identification of the stationary object and estimation of its coordinates is done using image processing techniques.The final product can be identified as a device that converts conventional robot arms without an image processing vision system into a highly precise and accurate robot arm with an image processing vision system.Experimental studies are performed to test the efficiency and effectiveness of used techniques and the gripper prototype.Necessary actions are taken to minimize the errors in position and orientation estimation.In addition,as a future implementation,an embedded system will be developed with a user-friendly software interface to install the vision system into the Fanuc M10 iA/12 robot arm and will upgrade the system to a device that can be implemented with any kind of customized robot arms available in the industry.

Recursive recurrent neural network:A novel model for manipulator control with different levels of physical constraints

Manipulators actuate joints to let end effectors to perform precise path tracking tasks.Recurrent neural network which is described by dynamic models with parallel processing capability,is a powerful tool for kinematic control of manipulators.Due to physical limitations and actuation saturation of manipulator joints,the involvement of joint constraints for kinematic control of manipulators is essential and critical.However,current existing manipulator control methods based on recurrent neural networks mainly handle with limited levels of joint angular constraints,and to the best of our knowledge,methods for kinematic control of manipulators with higher order joint constraints based on recurrent neural networks are not yet reported.In this study,for the first time,a novel recursive recurrent network model is proposed to solve the kinematic control issue for manipulators with different levels of physical constraints,and the proposed recursive recurrent neural network can be formulated as a new manifold system to ensure control solution within all of the joint constraints in different orders.The theoretical analysis shows the stability and the purposed recursive recurrent neural network and its convergence to solution.Simulation results further demonstrate the effectiveness of the proposed method in end‐effector path tracking control under different levels of joint constraints based on the Kuka manipulator system.Comparisons with other methods such as the pseudoinverse‐based method and conventional recurrent neural network method substantiate the superiority of the proposed method.

NEW APPROACHES FOR COMPUTING DYNAMIC LOAD-CARRYING CAPACITY OF MULTIPLE COOPERATING ROBOTIC MANIPULATORS

A novel unified method for computing the dynamic load carrying capacity(DLCC) of multiple cooperating robotic manipulators is developed.In this method,the kinematic constraints and the governing dynamic equations of the multiple robot system are formulated in the joint space by using the method of transference of dependence from one set of generalized coordinates to another,and the virtual work principle,which includes the readily available dynamics and joint torques of individual manipulators,and the dynamic of payload.Based on this dynamic model,the upper limit of the DLCC at any points on a given trajectory is obtained by solving a small size linear programming problem.This method is conceptually straightforward,and it is applicable also to the cases of multi fingered robot hands and multi legged walking machines.

Adaptive RBF neural network control of robot with actuator nonlinearities

In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinearities compensator. Since the actuator nonlinearities are usually included in the robot driving motor, a compensator using radial basis function (RBF) network is proposed to estimate the actuator nonlinearities and eliminate their effects. Subsequently, an adaptive neural network controller that neither requires the evaluation of inverse dynamical model nor the time-consuming training process is given. In addition, GL matrix and its product operator are introduced to help prove the stability of the closed control system. Considering the adaptive neural network controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded (UUB). The whole scheme provides a general procedure to control the robot manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.