Farzin Piltan

Design New Online Tuning Intelligent Chattering Free Fuzzy Compensator

This research is focused on proposed adaptive fuzzy sliding mode algorithms with the adaptation laws derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. Adaptive MIMO fuzzy compensate fuzzy sliding mode method design a MIMO fuzzy system to compensate for the model uncertainties of the system, and chattering also solved by new adaption method. Since there is no tuning method to adjust the premise part of fuzzy rules so we presented a scheme to online tune consequence part of fuzzy rules. Classical sliding mode control is robust to control model uncertainties and external disturbances. A sliding mode method with a switching control low guarantees the stability of the certain and/or uncertain system, but the addition of the switching control low introduces chattering into the system. One of the main targets in this research to reduce or eliminate chattering is to insert online tuning method. Classical sliding mode control method has difficulty in handling unstructured model uncertainties. One can overcome this problem by combining a sliding mode controller and artificial intelligence (e.g. fuzzy logic). To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This large number of fuzzy rules will cause a high computation load. The addition of an adaptive law to a fuzzy sliding mode controller to online tune the parameters of the fuzzy rules in use will ensure a moderate computational load. The adaptive laws in this algorithm are designed based on the Lyapunov stability theorem. Asymptotic stability of the closed loop system is also proved in the sense of Lyapunov. This method is applied to continuum robot manipulator to have the best performance.

Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer

An effective bearing fault detection and diagnosis (FDD) model is important for ensuring the normal and safe operation of machines. This paper presents a reliable model-reference observer technique for FDD based on modeling of a bearing’s vibration data by analyzing the dynamic properties of the bearing and a higher-order super-twisting sliding mode observation (HOSTSMO) technique for making diagnostic decisions using these data models. The HOSTSMO technique can adaptively improve the performance of estimating nonlinear failures in rolling element bearings (REBs) over a linear approach by modeling 5 degrees of freedom under normal and faulty conditions. The effectiveness of the proposed technique is evaluated using a vibration dataset provided by Case Western Reserve University, which consists of vibration acceleration signals recorded for REBs with inner, outer, ball, and no faults, i.e., normal. Experimental results indicate that the proposed technique outperforms the ARX-Laguerre proportional integral observation (ALPIO) technique, yielding 18.82%, 16.825%, and 17.44% performance improvements for three levels of crack severity of 0.007, 0.014, and 0.021 inches, respectively.

Robust Composite High-Order Super-Twisting Sliding Mode Control of Robot Manipulators

This paper describes the design of a robust composite high-order super-twisting sliding mode controller (HOSTSMC) for robot manipulators. Robot manipulators are extensively used in industrial manufacturing for many complex and specialized applications. These applications require robots with nonlinear mechanical architectures, resulting in multiple control challenges in various applications. To address this issue, this paper focuses on designing a robust composite high-order super-twisting sliding mode controller by combining a higher-order super-twisting sliding mode controller as the main controller with a super-twisting higher-order sliding mode observer as unknown state measurement and uncertainty estimator in the presence of uncertainty. The proposed method adaptively improves the traditional sliding mode controller (TSMC) and the estimated state sliding mode controller (ESMC) to attenuate the chattering. The effectiveness of a HOSTSMC is tested over six degrees of freedom (DOF) using a Programmable Universal Manipulation Arm (PUMA) robot manipulator. The proposed method outperforms the TSMC and ESMC, yielding 4.9% and 2% average performance improvements in the output position root-mean-square (RMS) error and average error, respectively.

Nonlinear Model-Free Control and ARX Modeling of Industrial Motor

System identification is one of the main challenges in real time control. To design the best controller for linear or nonlinear systems, mathematical modeling is the main challenge. To solve this challenge conventional and intelligent identification are recommended. The second important challenge in the field of control theory is, design high-performance controller. To improve the performance of controller, two factors are very important: 1) high performance mathematical or intelligent modeling, 2) chose the best controller for the system. This paper has two main objectives: after data collection from position motor from industry the first objective is modeling and system identification based on Auto-Regressive with eXternal model input (ARX) and defined Z-function and Sfunction and the second objective is; design the high-performance controller to have the minimum rise time and error.

Design SPARTAN FPGA-Based PD Controller for FOD Systems

It goes without saying that, there is quite a diverse mixture of the linear controller. Nevertheless, I assume the most famous would probably be Proportional Integral (PI) controller. The things with PI controller are that most of PI controllers are reduction the error. As well as PI controllers, another kind of linear controller worth mentioning could be Proportional Derivative (PD) controller and the unique characteristic of PD controller is that PD controllers are high speed and independent of system’s dynamic modeling. In addition, there are the usual things like PID controllers and PI2D controllers. The main objective of this paper designs a minimum delay proportionalderivative (PD) controller to the control of first order delay (FOD) system. First order delay system has delay time about 4 seconds in certain and about 19 seconds in limited uncertain condition. To improve the flexibility, design high-speed and low-cost controller, the micro-electronic device (FPGA-Based) controller is used in this research. The proposed design is 30-bits FPGA-based controller for inputs and 35-bits for output. In this research, the maximum frequency is 63.629 MHz and the minimum period is 15.716 ns, the minimum input arrival time before the clock is 4.362 ns and the maximum output required time after clock is19.727 ns. In this algorithm, the delay time for the derivative algorithm is 15.526 ns which 87.8% is a logic delay and 12.2% is route delay.