Dynamic Identification of Industrial Robot Based on Nonlinear Friction Model and LS-SOS Algorithm

Due to friction characteristics of clutch, the driveline is prone to cause a judder during vehicle starting, and then to cause the vehicle body to vibrate, which affects driving quality. In order to analyze the judder phenomenon, a nonlinear numerical friction model based on the Gaussian friction model is established in this paper. For the driveline of a front-wheel-drive vehicle, a five-degree-of-freedom (5DOF) lumped parameter model including a nonlinear friction element is established. The complex mode of the driveline during the clutch in slip condition is calculated. The key parameters affecting the driveline stability are analyzed. The self-excited judder and pressure-induced judder of the driveline are numerically simulated, and the corresponding causes are analyzed. The nonlinear friction torque of the clutch is also calculated. Furthermore, the effects of the key parameters such as the torsional stiffness and damping of the clutch and drive shaft suppressing the self-excited judder and pressure-induced judder are numerically studied respectively. Compared with the widely used Karnopp friction model, the nonlinear numerical friction model established in this paper comprehensively includes the stribeck effect in slip and the friction torque characteristics in stick. The phenomena of the judder and stick-slip of the driveline during vehicle starting are more accurately simulated. The simulation results are in good agreement with the experimental results, which verify the accuracy and effectiveness of the dynamic model including the nonlinear friction element established in this paper.

Aiming at the shortcomings of the current dynamic parameter identification methods in fitting nonlinear friction characteristics, the satisfaction of physically-consistent constraints, and the efficiency of dynamic calculation of the minimum inertial set, a physical consistent dynamic full parameter identification method based on the nonlinear friction model is proposed. Firstly, according to the characteristics of each component of the robot's dynamic equation, the joint trajectory that can cancel the non-friction moment term during the forward and reverse rotation of the joint is designed, and the friction moment is separated from the measured moment; Secondly, the nonlinear friction model is used to model the friction torque, and the friction parameters are obtained by solving the nonlinear least square problem; Finally, the regularized weighted least squares problem with physically-consistent constraints is solved by convex optimization method, and the full parameter set of robot dynamics including friction parameters and inertia parameters is obtained. The experimental results show that the RMS error of the proposed dynamic parameter identification method is the smallest compared with the CAD numerical calculation method and the penalty optimization identification method.

The symbiotic organisms search (SOS) algorithm is an effective metaheuristic developed in 2014, which mimics the symbiotic relationship among the living beings, such as mutualism, commensalism, and parasitism, to survive in the ecosystem. In this study, three modified versions of the SOS algorithm are proposed by introducing adaptive benefit factors in the basic SOS algorithm to improve its efficiency. The basic SOS algorithm only considers benefit factors, whereas the proposed variants of the SOS algorithm, consider effective combinations of adaptive benefit factors and benefit factors to study their competence to lay down a good balance between exploration and exploitation of the search space. The proposed algorithms are tested to suit its applications to the engineering structures subjected to dynamic excitation, which may lead to undesirable vibrations. Structure optimization problems become more challenging if the shape and size variables are taken into account along with the frequency. To check the feasibility and effectiveness of the proposed algorithms, six different planar and space trusses are subjected to experimental analysis. The results obtained using the proposed methods are compared with those obtained using other optimization methods well established in the literature. The results reveal that the adaptive SOS algorithm is more reliable and efficient than the basic SOS algorithm and other state-of-the-art algorithms. Highlights Correlation between organisms, optimization and engineering. Adaptive symbiotic organisms search (SOS) algorithm is proposed. Implementation on structural design problems. Effective over other methods.

This paper addresses the problem of makespan minimization on unrelated parallel machines with sequence dependent setup times. The symbiotic organisms search (SOS) algorithm is a new and popular global optimization technique that has received wide acceptance in recent years from researchers in continuous and discrete optimization domains. An improved SOS algorithm is developed to solve the parallel machine scheduling problem. Since the standard SOS algorithm was originally developed to solve continuous optimization problems, a new solution representation and decoding procedure is designed to make the SOS algorithm suitable for the unrelated parallel machine scheduling problem (UPMSP). Similarly, to enhance the solution quality of the SOS algorithm, an iterated local search strategy based on combining variable numbers of insertion and swap moves is incorporated into the SOS algorithm. More so, to further improve the SOS optimization speed and performance, the longest processing time first (LPT) rule is used to design a machine assignment heuristic that assigns processing machines to jobs based on the machine dynamic load-balancing mechanism. Subsequently, the machine assignment scheme is incorporated into SOS algorithms and used to solve the UPMSP. The performances of the proposed methods are evaluated by comparing their solutions with other existing techniques from the literature. A number of statistical tests were also conducted to determine the variations in performance for each of the techniques. The experimental results showed that the SOS with LPT (SOS-LPT) heuristic has the best performance compared to other tested method, which is closely followed by SOS algorithm, indicating that the two proposed algorithms’ solution approaches are reasonable and effective for solving large-scale UPMSPs.

Identification of dynamic parameters of a 3-DOF RPS parallel manipulator

Symbiotic Organism Search (SOS) algorithm is highly praised by researchers for its excellent convergence performance, global optimization ability and simplicity in solving various continuous practical problems. However, in the real world, there are many binary problems, which can only take values of 0 and 1, that still need to be solved. Since the original SOS algorithm cannot directly solve the binary problem, the original ASOS Binary SOS (BSOS) algorithm has the disadvantage of premature convergence. In order to improve the limitations of the ASBSOS algorithm, we propose an Improved BSOS (IBSOS) algorithm. As we all know, the transfer function is very important in the binarization of continuous optimization algorithms. Therefore, we used 9 transfer functions in the IBSOS algorithm to binarize the continuous SOS algorithm and analyzed the impact of each transfer function on the performance of the BSOS algorithm. Moreover, we use the same three biological symbiosis strategies as the continuous SOS algorithm in our proposed IBSOS algorithm to binarize the SOS algorithm to improve The diversity of the algorithm execution process and the ability to balance algorithm exploration and development. In order to verify the performance of IBSOS using different transfer functions, we use 13 benchmark functions to show the global optimization capability and convergence speed of the BSOS algorithm. Finally, we apply the algorithm to feature selection in the ten data sets of UCI. The experimental results with low classification error and few features further verify the excellent performance of the IBSOS algorithm.

An enhanced symbiotic organisms search (ESOS) algorithm is developed and presented. Modifications to the basic symbiotic organisms search algorithm are carried out in all three phases of the algorithm with the aim of balancing the exploitation and exploration capabilities of the algorithm. To verify validity and capability of the ESOS algorithm in solving general optimization problems, the CEC2014 set of 22 benchmark functions is first optimized and the results are compared with other metaheuristic algorithms. The ESOS algorithm is then used to optimize the sizing and shape of five benchmark trusses with multiple frequency constraints. The best (minimum) mass, mean mass, standard deviation of the mass, total number of function evaluations, and the values of frequency constraints are then compared with those of a number of other metaheuristic solutions available in the literature. It is shown that the proposed ESOS algorithm is generally more efficient in optimizing the shape and sizing of trusses with dynamic frequency constraints compared to other reported metaheuristic algorithms, including the basic symbiotic organisms search and its other recently proposed improved variants such as the improved symbiotic organisms search algorithm (ISOS) and modified symbiotic organisms search algorithm (MSOS).

In today’s competitive electricity market, managing transmission congestion in deregulated power system has created challenges for independent system operators to operate the transmission lines reliably within the limits. This paper proposes a new meta-heuristic algorithm, called as symbiotic organisms search (SOS) algorithm, for congestion management (CM) problem in pool based electricity market by real power rescheduling of generators. Inspired by interactions among organisms in ecosystem, SOS algorithm is a recent population based algorithm which does not require any algorithm specific control parameters unlike other algorithms. Various security constraints such as load bus voltage and line loading are taken into account while dealing with the CM problem. In this paper, the proposed SOS algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results, thus, obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the proposed SOS algorithm for obtaining the higher quality solution is also established.

This article addresses simultaneous scheduling of machines, AGVs and tools where machines are allowed to share the tools considering transfer times of jobs and tools between machines, to generate best optimal sequences that minimize makespan in a multi-machine Flexible Manufacturing System (FMS). Performance of FMS is expected to improve by effective utilization of its resources, by proper integration and synchronization of their scheduling. Symbiotic Organisms Search (SOS) algorithm is a potent tool which is a better alternative for solving optimization problems like scheduling and proven itself. The proposed SOS algorithm is tested on 22 job sets with makespan as objective for scheduling of machines and tools where machines are allowed to share tools without considering transfer times of jobs and tools and the results are compared with the results of existing methods. The results show that the SOS has outperformed. The same SOS algorithm is used for simultaneous scheduling of machines, AGVs and tools where machines are allowed to share tools considering transfer times of jobs and tools to determine the best optimal sequences that minimize makespan.

In this paper, a hybrid optimization algorithm is proposed to identify the dynamic parameters of a 6-DOF electro-hydraulic parallel platform. The dynamic model of a parallel platform with arbitrary geometry, inertia distribution and frictions is obtained based on a structured Boltzmann–Hamel–d’Alembert formulation, and then the estimation equations are explicitly expressed in terms of a linear form with respect to the identified inertial and the friction coefficients in accordance with a linear friction model. However, when nonlinear friction models are considered, the parameter identification of the electro-hydraulic parallel platform is considered as an optimization process with an objective function minimizing the errors between the measurement and identification, and then an effective combination of the particle swarm optimization (PSO) method and the local quasi-Newton method is proposed to solve the identification problem. Experimental identification processes are carried out for the identified parameters, and the identified models are compared by the predicted forces between the LS method and the optimization technique as well as between the linear and nonlinear friction models.

Twin Rotor Aero-dynamical System (TRAS) include non-negligible nonlinearities due to frictions in the joints. In this study, joint frictions of the 2 DOF Double Dual Twin Rotor Aero-dynamical System (TDDTRAS) were estimated based on experimental data and simulation dynamic responses. Three different friction estimation models such as Non-Conservative, Linear, and a Non-linear friction models were compared to estimate the joint frictions of the (TDDTRAS) developed in our laboratory. Based on comparative experimental friction analysis, the joint frictions of the (TDDTRAS) are estimated better using Non-Linear friction model (NLFM). The mathematical model with NLFM is developed for simulation and controller. A robust dynamics model was obtained by the NLFM which will be used in the inverse dynamic model for the PID controller. PID controllers designed based on the mathematical model with nonlinear friction model. Accurate inverse model and the joint friction compensation improved PID controller performance for the TDDTRAS.

This paper examines a frequency response analysis (FRA) method which aims at obtaining an precise linear plant model for a controller design, for fast and precise positioning of mechatronic systems with nonlinear friction. As well known, friction generated at linear guideways and/or bearings shows nonlinear and complicated behaviors, which causes deterioration of the motion control performance. In addition, the frequency response of the plant system dynamically changes depending on the measurement condition such as displacement and/or excitation force in the frequency domain analysis. The dynamic change of the frequency response makes it difficult to identify the actual linear plant characteristic for design of a precise simulator and/or an effective compensator. In this study, therefore, effects of parameter identification errors due to the dynamic frequency property on the fine positioning performance are clarified by simulation analyses with a nonlinear friction model. Then, a fundamental friction model-based FRA method is introduced, and the effectiveness of the presented FRA and effects of the friction model errors are demonstrated by frequency-domain simulations.

This paper concerns the problem of dynamical identification for an industrial robot manipulator and presents an identification procedure based on an improved cuckoo search algorithm. Firstly, a dynamical model of a 6-DOF industrial serial robot has been derived. And a nonlinear friction model is added to describe the friction characteristic at motion reversal. Secondly, we use a cuckoo search algorithm to identify the unknown parameters. To enhance the performance of the original algorithm, both chaotic operator and emotion operator are employed to help the algorithm jump out of local optimum. Then, the proposed algorithm has been implemented on the first three joints of the ER-16 robot manipulator through an identification experiment. The results show that (1) the proposed algorithm has higher identification accuracy over the cuckoo search algorithm or particle swarm optimization algorithm and (2) compared to linear friction model the nonlinear model can describe the friction characteristic of joints better.

The symbiotic organism search (SOS) algorithm is a promising meta-heuristic evolutionary algorithm. Its excellent quality of global optimization solution has aroused the interest of many researchers. In this work, we not only applied the strategy of multi-group communication and quantum behavior to the SOS algorithm, but also formed a novel global optimization algorithm called the MQSOS algorithm. It has speed and convergence ability and plays a good role in solving practical problems with multiple arguments. We also compared MQSOS with other intelligent algorithms under the CEC2013 large-scale optimization test suite, such as particle swarm optimization (PSO), parallel PSO (PPSO), adaptive PSO (APSO), QUasi-Affine TRansformation Evolutionary (QUATRE), and oppositional SOS (OSOS). The experimental results show that MQSOS algorithm had better performance than the other intelligent algorithms. In addition, we combined and optimized the DV-hop algorithm for node localization in wireless sensor networks, and also improved the DV-hop localization algorithm to achieve higher localization accuracy than some existing algorithms.

In this paper a new method of modeling nonlinear friction as a function of both position and velocity is introduced. The proposed empirical model uses spectral analysis to identify the main sources of position dependent friction in complex mechanisms. In addition, the model describes the contribution to the overall friction of every moving part of the mechanism. In the paper, the proposed spectral-based modeling technique is used to describe static friction and sliding friction (which includes negative damping friction) of the first joint of PUMA 560 robot. This is the first friction model truly capable of describing nonlinear friction in complex mechanical systems.KeywordsAngular VelocityStatic FrictionFriction TorqueFriction CharacteristicNonlinear FrictionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.