In the face of large-scale parametric uncertainties, the single-model (SM)-based sliding mode control (SMC) approach demands high gains for the observer, controller, and adaptation to achieve satisfactory tracking performance. The main practical problem of having high-gain-based design is that it amplifies the input and output disturbance as well as excites hidden unmodeled dynamics, causing poor tracking performance. In this paper, a multiple model/control-based SMC technique is proposed to reduce the level of parametric uncertainty to reduce observercontroller gains. To this end, we split uniformly the compact set of unknown parameters into a finite number of smaller compact subsets. Then, we design a candidate SMC corresponding to each of these smaller subsets. The derivative of the Lyapunov function candidate is used as a resetting criterion to identify a candidate model that approximates closely the plant at each instant of time.The key idea is to allow the parameter estimate of conventional adaptive sliding mode control design to be reset into a model that best estimates the plant among a finite set of candidate models. The proposed method is evaluated on a 2-DOF robot manipulator to demonstrate the effectiveness of the theoretical development.

In this paper we present the implementation of proportional integral derivative (PID) controlled mobile robot. we propose a mobile robot control method which based on optical encoder. First, the mobile robot counts the number of pulses from optical sensor. This system rejects the denouncing and optical sensor noises and makes a precise count of both the wheel rotation at the same time. Then through an adaptive PID control loop it controls the speed of both the motors to reach user defined set point. PID auto tuning methods are performed in different situations like on air, on ground and with load increase. This system detects the situation the robot at present and switches between its different modes to achieve the quickest maximum efficiency. Speed of each motor is controlled by Pulse Width Modulation (PWM). Finally, we experiment in a real environment, and verify the utility of the propose method.

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