The output processer of interval type-2 fuzzy logic systems (IT2FLSs) is a complex operator which performs type-reduction plus defuzzification (TR+D) tasks. In this paper, a complexity-reduced yet high-performance TR+D for IT2FLSs based on Maclaurin series approximation is utilized within a feedback-error-learning (FEL) control structure for controlling linear move stages. IT2FLSs are widely used for control purposes, as they provide extra degrees of freedom to increase control accuracies. FEL benefits from a classical controller, which is responsible for providing overall system stability, as well as a guideline for the training mechanism for IT2FLSs. The Kalman filter approach is utilized to tune IT2FLS parameters in this FEL structure. The proposed control method is applied to a linear stage in real time. Using an identification process, a model of the real-time linear stage is developed. Simulation results indicate that the proposed FEL approach using the Kalman filter as an estimator is an effective approach that outperforms the gradient descent-based FEL method and the proportional derivative (PD) classical controller. Motivated by the performance of the proposed Kalman filter-based FEL approach, it is used to control a linear move stage in real time. The position feedback of the move stage is provided by a precision laser interferometer capable of performing measurements with an accuracy of less than $1\mathsf{\mu }\mathrm{m}$. Using this measurement system in a feedback loop with the proposed control algorithm, the overall steady state of the system is less than $20\mathsf{\mu }\mathrm{m}$. The results illustrate the high-precision control capability of the proposed controller in real-time.

Highly accurate positioning of industrial robots is crucial to performing industrial operations with high quality. This paper presents a mechanical modification to an industrial robot aiming at enhancing the system actuation resolution, thereby enhancing its positional accuracy. The industrial robot under consideration is a six-degrees of freedom (DoF) robot with revolute joints. By integrating a linear stage, a prismatic joint is introduced to the robot’s end effector, reconfiguring it into a 7 DoF system with more precise step size capabilities. To improve the positional accuracy of the overall system, a closed-loop control structure is chosen. Positional feedback is provided using an industrial laser tracker. Initially, a multi-layer perceptron neural network (MLPNN) is used to identify the forward kinematics (FK) of the overall 6RP robotic system. The FK of the industrial robot using the pretrained MLPNN is then used online to compute the real-time sensitivity of positional error to changes in the joint angle values of the industrial robot and displacements of the prismatic joint. Different trajectories are used to test the accuracy of the proposed positioning algorithm. From the implementation results obtained using the proposed control structure, it is observed that the accuracy of the industrial robot improves significantly. Statistical results for five different points selected from the ISO 9283 trajectory over 30 times of measurements show an 82% improvement for the measurements using the proposed approach as compared to the original industrial robot controller.

Precision component displacement, processing, and manipulation in an industrial environment require the high-precision positioning and orientation of industrial robots. However, industrial robots’ positioning includes uncertainties due to assembly and manufacturing tolerances. It is therefore required to use calibration techniques for industrial robot parameters. One of the major sources of uncertainty is the one associated with industrial robot geometrical parameter values. In this paper, using multi-objective meta-heuristic optimization approaches and optical metrology measurements, more accurate Denavit–Hartenberg (DH) geometrical parameters of an industrial robot are estimated. The sensor data used to perform this calibration are the absolute 3D position readings using a highly accurate laser tracker (LT) and industrial robot joint angle readings. Other than position accuracy, the mean absolute deviation of the DH parameters from the manufacturer’s given parameters is considered as the second objective function. Therefore, the optimization problem investigated in this paper is a multi-objective one. The solution to the multi-objective optimization problem is obtained using different evolutionary and swarm optimization approaches. The evolutionary optimization approaches are nondominated sorting genetic algorithms and a multi-objective evolutionary algorithm based on decomposition. The swarm optimization approach considered in this paper is multi-objective particle swarm optimization. It is observed that NSGAII outperforms the other two optimization algorithms in terms of a more diverse Pareto front and the function corresponding to the positional accuracy. It is further observed that through using NSGAII for calibration purposes, the root mean squared for positional error has been improved significantly compared with nominal values.

Sunulan çalışmada; kullanıcı tarafından kontrol edilen bir robot el sistemi için çalışma yapılmıştır. Sistemin nesne kavrama kuvveti hassasiyetinin sağlanması amacıyla kavrama kuvvetinin kontrol sorunu üzerine durulmuştur. Bunun yanı sıra nesneye uygulanan kuvvetin kullanıcıya geri bildirimi için haptik geri bildirim sistemi oluşturulmuştur. Robot elin kullanıcı komutu doğrultusunda nesnelere uyguladığı kuvvetin kontrolünde, bulanık mantık önerilmiş ve uygulanmıştır. Kullanıcı bulanık mantık kontrolcüye tut, bırak, sık gibi farklı kavrama komutlarını gönderebilmektedir. Bulanık mantık kontrolcü ise kullanıcı komutu doğrultusunda objeye uygulanan kuvveti kontrol etmektedir. Ayrıca kuvvet sensöründen gelen veriler bulanık mantık kontrolcü ile değerlendirilerek haptik geri bildirim sisteminde bulunan titreşim motorlarının titreşim şiddeti ayarlanmaktadır. Böylelikle kullanıcı, hem objeleri ne kadar kuvvetle sıktığını hissedebilmekte hem de sadece bir kavrama komutu göndererek nesnenin uygun kuvvette kavranmasını sağlayabilmektedir. Oluşturulan sistem objelerin kavranması esnasında oluşan kavrama kuvvetinin kontrol sorunu ve his geri bildirim eksikliği sorunlarına bir çözüm niteliğindedir.