Balancing control energy and tracking error for fuzzy rule emulated adaptive controller

In this article, an adaptive controller, which can minimize both tracking error and control energy, is introduced by fuzzy rule emulated network (FREN) for a class of non-affine discrete time systems. The controlled plant can be assumed as fully unknown system dynamic. Only the estimated boundary of pseudo partial derivative (PPD) is required for an on-line learning phase. The update law is derived to guarantee the convergence of tuned parameters. Lyapunov techniques are utilized to demonstrate the performance of a closed-loop system regarding the integration of the infinite cost function. The computer simulation and electronic circuit system validate the effectiveness of the proposed control scheme.     

Fuzzy-rule emulated networks, based on reinforcement learning for nonlinear discrete-time controllers

This article introduces an adaptive controller for a class of nonlinear discrete-time systems, based on self adjustable networks called Multi-Input Fuzzy Rules Emulated Networks (MIFRENs), and its reinforcement learning algorithm. Because of the universal function approximation of MIFREN, the first MIFREN called MIFREN(c) is used to estimate a long-term cost function, which demonstrates as a performance index for the tuning procedure. Another network or MIFREN(a) is designed as a direct controller via the human knowledge through defined If-Then rules. The selection procedure for any system parameters, such as learning rates and some constant parameters, is represented by the proof of proposed theorems. The system's performance is demonstrated by computer simulations via selected nonlinear discrete-time systems, and comparison results with other controllers to validate theoretical development.     

A discrete-time stable controller for an omni-directional mobile robot based on an approximated model

An adaptive controller based on multi-input fuzzy rules emulated networks (MIFRENs) is introduced for omni-directional mobile robot systems in the discrete-time domain without any kinematic or dynamic models. An approximated model for unknown systems is developed by using two MIFRENs with an online learning algorithm in addition to the stability analysis. The main theorem in this model is proposed to guarantee closed-loop performance and system robustness for all adjustable parameters inside MIFRENs. The system is validated by an experimental setup with a FESTO omni-directional mobile robot called Robotino^®. The proposed algorithm is shown to have superior performance compared to that of an algorithm that uses only an embedded controller. The advantage of the MIFREN initial setting is verified comparing its results with those of a controller that is based on neural networks.     

Adaptive control based on IF–THEN rules for grasping force regulation with unknown contact mechanism

An industrial gripping application with unknown contact mechanism is considered as a class of unknown nonlinear discrete-time systems. The control scheme is developed by an adaptive network called multi-input fuzzy rules emulated network (MiFREN) within discrete-time domain. The network structure is directly constructed regarding to IF–THEN rules related to gripper and contact mechanism properties. All adjustable parameters require only the on-line learning phase to improve the closed loop performance. The time varying learning rate is devised for gradient reach with the proof of stability analysis. Furthermore, the estimated sensitivity of system dynamic is directly considered within the parameter adaptation. The experimental system with an industrial parallel grip model WSG-50 validates the performance of the proposed controller.     

Discrete-time direct adaptive control for robotic systems based on model-free and if–then rules operation

An adaptive controller for a class of nonlinear discrete-time systems is proposed for robotic systems under the assumption that the parameters and structure of system dynamics are all unknown. This controller is designed with the concept of model-free adaptive control requiring only the input–output of the unknown plant. The robotic system has been generalized to be a nonaffine discrete-time system under reasonable assumptions. The adaptive scheme called fuzzy rules emulated network (FREN) is implemented as a direct controller. The IF–THEN rules for FREN have been defined by the knowledge according to the relation between input and output of the robotic system without any compensator for the unknown mathematical model or nonlinearities. The underlying physical specifications of robotic system such as the operating range, maximum joint velocity, and so on have been considered to initialize the membership functions and adjustable parameters of FREN. The adaptation scheme is developed according to convergence analysis established for both adjustable parameters and the tracking error. The performance of the proposed controller is validated by the experimental system with a 7-degrees-of-freedom robotic arm operated in velocity-mode control.     

Discrete-time prescribed performance controller based on affine data-driven model

The noncontinuous behavior of the controlled plant occurring as both positive and negative control directions is observed from the prototyping robotic system. By considering the controlled plant as a class of unknown nonlinear discrete-time systems, the affine data-driven model (ADM) is developed by a multi-input fuzzy rule emulated network (MiFREN) when the property of a continuous function is omitted. Therefore, the controller is established by the result of ADM when the specification of tracking error can be designed by the prescribed boundaries. The theoretical principle is utilized for the closed-loop analysis which guarantees the performance by designing the setting parameters. For the practical aspect, the design procedure and the performance are demonstrated by the experimental results.     

Biological systems drug infusion controller using FREN with sliding bounds

In this paper, a direct adaptive control for drug infusion of biological systems is presented. The proposed controller is accomplished using our adaptive network called Fuzzy Rules Emulated Network (FREN). The structure of FREN resembles the human knowledge in the form of fuzzy IF-THEN rules. After selecting the initial value of network's parameters, an on-line adaptive process based on Lyapunov's criteria is performed to improve the controller performance. The control signal from FREN is designed to keep in the region which is calculated by the modified Sliding Mode Control (SMC). The simulation results indicate that the proposed algorithm can satisfy the setting point and the robust performance.     

Nonlinear discrete-time controller based on fuzzy-rule emulated network and shuttering condition

This article introduces the adaptive controller for a class of nonlinear discrete-time systems based on the sliding shuttering condition and the self adjustable network called Multi-Input Fuzzy Rules Emulated Network (MIFREN). By using only the online learning phase, MIFREN’s functional is the nonlinear discrete-tine function approximation and the disturbance estimation together. The proposed theorem is introduced for the designing procedure of all controller’s parameters and MIFREN’s adaptation gain. Simulation results demonstrate the justification of the theorem for the tracking performance and the unknown disturbance rejection.     

Grasping force controller for parallel grip with fuzzy rules emulated networks

A grasping force regulation for industrial parallel grips is developed without any requirement of mathematic model regarding to the contact mechanism and system dynamic. The physical system including the grasping dynamic and contact mechanism is considered as a class of unknown nonlinear discrete-time systems. An adaptive network called multi-input fuzzy rules emulated network (MiFREN) is implemented as the controller. This control scheme is performed by if-then rules which can be directly defined by human knowledge regarding to the gripper’s specification and objects. The learning algorithm based on gradient search is developed to tune all adjustable parameters inside MiFREN. The system performance and stability can be guaranteed by the time-varying learning rate. An industrial parallel grip SCHUNK-WSG 50 with the proposed controller demonstrates the performance via the experimental setup. Furthermore, the performance can be spontaneously improved within the next iteration of the learning process.