Controlling the rotor position is no longer the only goal in modern High Performance Drives (HPD) such as robotics, guided manipulation and supervised actuation. HPD systems are quite different from the positional control applications where only the final value of the rotor position and/or speed is controlled with no or minimal control on the traveling time or overshoots. It is essential that the rotor of the HPD system follows a preselected track at all time. A multi-robot system performing a complementing function must have the end effectors move about the space of operation according to a pre-selected time tagged trajectory. To achieve this, every motor in the robot arm must follow a specific track so that the aggregated motion of all motors keeps the end effector alongside its trajectory at all time. This must be achieved even when the system loads, inertia and parameters are varying. Any HPD system must have three basic components: an electric motor; a broad performance high speed solid-state switching converter and an advanced controller. The control strategy must be adaptive, robust, accurate, and simple to implement. Unlike most of the conventional positional controllers, the controllers for high performance tracking do not employ constant parameters. The structure and/or the parameters of the controllers must be adaptively tuned to achieve two basic objectives: 1) to provide the best possible tracking performance without overstressing the hardware; and 2) to enhance the system robustness. In the applications where the parameters of the load or the drive system are changing, the robustness of the controller is a basic requirement. Fixed parameters' controllers, such as the PID can not be considered robust. Some of the adaptive control techniques such as the variable structure and the self-tuning do not employ a physical model for the system dynamics. The dynamic model is developed based on the input/output response of the system under control. These models are usually linear but updated every several sampling intervals. Although, these adaptive controllers can be effective, they are complex to develop and require elaborate hardware to implement. With the introduction of improved training algorithms and new network topologies, the Neural Networks (NN) have demonstrated its feasibility and practicality in several applications including electric drives. Artificial Neural Network using parallel and distributed processing units can achieve the functions of system modeling and control. NN has several key features that make it highly suitable for HPD applications.
For more information, please contact M. A. El-Sharkawi