## 1 Introduction  
The robot gripper is a useful and important component of an automated system. It is often used to pick up and place a given object on an assembly line in production. It is also used for complex tasks such as assembly of microelectronic components, surgery, etc., or in areas that have hazardous conditions such as high temperature or toxic chemicals [1]. There are many actuation principles used in grippers with mechanical, pneumatic, hydraulic, electric, or piezoelectric actuators, etc. Along with the development of technology, the grippers have been brought to a new level. The grippers not only grip, pick up, and place objects to a new position but also are equipped with sensing capabilities to adapt to changing environments [2]. In automated production, one of the important requirements of grippers is the ability to safely grasp and hold fragile objects of varying stiffness and shapes. Using flexible grippers is one of the solutions. In [3], embedded sensors are used to ensure the safe and optimal behavior of the gripper. The authors establish soft computing methods including extreme learning machines and support vector regression to achieve the prediction of optimal input displacement of the gripper. The authors of [4] deal with a multiobjective optimization problem using a genetic algorithm, while [5] establishes a direct force control for a three-finger adaptive robot gripper by using a proportional-integral-derivative (PID) control to grasp objects without damaging them. [PERSON] [6] proposes a new design to control a robot gripper based on the grasping force method. The force controller is designed by using a PID control algorithm with different control gains and objects tuning by experiment methods. A high-speed multifinger reconfigurable gripper is presented in [7]. The gripper can grasp parts with varying geometrical and physical properties at high speed and accelerations. In [8], the authors use a force-sensitive resistor (FSR) to grasp novel objects adaptively with minimal gripping force. A laser-based optical slip sensor is embedded in its fingers to prevent the object from sliding down. The authors of [9] deal with a microgripper driven by piezoelectric actuators. The authors propose an adaptive online estimation scheme to calculate uncertain parameters in the dynamics model and the Kalman filter to predict the system output. Although there is a lot of research in literature, novel control applications are being studied to enhance gripper performance. This paper proposes an intelligent control approach for a robot gripper with the main objective of controlling the optimal gripping force in real time for unknown objects. The impedance control is used to keep the gripping force at the desired value. Impedance control is an indirect force control method and very popular in interaction control because of its robustness and feasibility [10; 11; 12; 13; 14]. However, in the impedance control method, the interaction force is changed from environment to environment and even within the same environment over time. Therefore, it is hard to determine the desired parameters of the impedance controller. In [15], the authors propose a new simple stable force tracking impedance control scheme. The main idea is to minimize the force error directly by using a simple adaptive gain when tracking an unknown environment. In [16], a novel adaptive impedance control is proposed for the robotic manipulator in assisting the operator to perform the human-robot cooperative task. It can optimize the impedance parameters with little information about the model. The authors of [17] use the equilibrium point control theory and reinforcement learning to determine the impedance parameters for contact tasks. In [18], The gradient-following and betterment schemes are employed to obtain the desired impedance model, subject to unknown environments. In [19; 20; 21; 22], the combination of fuzzy logic and traditional impedance control is proposed to enhance the control performance.