It can operate on its own without a human directly controlling it. The performances of PI controllers versus inverse neural models in mobile robot internal velocity control loops are demonstrated and compared in simulation experiment of navigation control task for line segment motion in plane.Īn autonomous robot is a machine able to extract information from its environment and use knowledge about its world to move safely in a meaningful and purposive manner. Neural models are trained off-line to act as an inverse dynamics of DC motors with particular load using data collected in simulation experiment for motor input voltage step changes within bounded operating area. In order to obtain relevant datasets for training of feed-forward multi-layer perceptron based neural network used as neural model, the mathematical model of mobile robot, that combines its kinematic and dynamic properties such as chassis dimensions, center of gravity offset, friction and actuator parameters is used.
The PI controller synthesis is based on linear approximation of actuators with equivalent load. This paper presents the application of an inverse neural models used as controllers in comparison to classical PI controllers for velocity tracking control task used in two-wheel, differentially driven mobile robot. The matching between these results proves the effectiveness and robustness of the proposed digital ANFIS and the excellent performance of the FPGA based controller. A comparison between the simulation and implementation results is made.
Fpga projects altera cyclone ii code#
The VHDL code for the controller is produced, aggregated and downloaded on the FPGA Spartan 3A/AN FPGA kit. A real-time FPGA implementation of the proposed digital ANFIS have been done and verified through Xilinx ISE 14.6 using the VHDL language. The reduced design reached an optimum size for this controller to utilize a smallest memory size. The reduced design minimizes the utilized slices from 366% to 3% and LUTs from 364% to 3%. A reduction is made for the designed digital ANFIS due to the used FPGA limitations. The designed controllers tested for cell cultures application at 37.5. Different controllers are designed and their results are compared using MATLAB program to show the ANFIS superiority. A novel design of digital ANFIS is presented here for the implementation process. The controller intended to control the temperature of medical oven. The design and realization of Adaptive Neuro-Fuzzy Inference System (ANFIS) controller based on Field Programmable Gate Array (FPGA) is presented in this paper. In the research, the only FPGA offering the possibility of adding more complex functions to the ability of the robot is used. In comparison with previous research, the application of robots based on the existing design is the presence of microcontroller such as Arduino with FPGA.
The design shows flexibility in hardware and software, where the design can be modified easily by inserting more complex function due to the capacity of FPGA in contrast to existing microcontroller or microprocessor -based designs. This system uses Nios II/e soft-core processor instantiated in ANN control of the motors based on the data provided by the sensors. This paper presents a mobile robot with an Artificial Neural Network (ANN) controller implemented on Altera FPGA mini-board with wireless capability to move to a specific distance by avoiding the obstacle. Therefore, the realization of efficient and robust robot system still a challenging task. The wireless capability, avoid obstacles and speed/position controllers have taken great interest in the design of mobile robots because of the extensive use in industrial and service fields. Such as the applications that robots need to Collection of information from complex conditions for their functioning which have become very common, especially in places that are hard to reach by humans. Mobile robots are expected to be used in harsh and non-organized environments.