Tracking control of direct-drive servos

Abstract

Multi-objective optimization, data-intensive analysis and hardware-software co-design are the major challenging themes in the concurrent design of high-performance electromechanical systems. Direct-drive servos guarantee superior torque and force densities, efficiency, robustness, simplicity and other enabling performance quantities. Nano-, micro-, mini- and macro-scale axial and radial actuators exist in a great variety, e.g., from living organisms to various engineered electromechanical systems. Permanent-magnet actuators and servos are widely used in aerospace, automotive, biotechnology, energy, medical, power, robotic and other applications. The major goal of this paper is to report and apply advanced concepts in design and implementation of tracking control laws. These control laws are designed using the state transformation method applying the Hamilton-Jacobi optimization and Lyapunov stability theory. We design and evaluate high-performance drives and servos. Various servo-systems with radial- and axial-topology actuators are demonstrated and characterized by evaluating analog and digital tracking control laws. The studied direct-drive actuators with SmCo permanent-magnet arrays guarantee high toque density, high efficiency, reliability, fast dynamics, etc. The controllers designed guarantee stability, high precision and robustness. The high-frequency PWM drivers vary the voltage applied by changing the duty ratio of FETs. High-accuracy sensors measure angular velocity and displacement. Linear and nonlinear analog control laws guarantee superior performance, enabling capabilities, minimal complexity, simplicity, noise immunity, etc. The analog control laws can be discretized and implemented using microcontrollers and DSPs. The studied drives and servos are applicable in many applications, including hard drives, high-precision pointing systems, rotating tables, manipulators, etc. This paper examines and solves a spectrum of pertinent problems in design and implementation of enabling minimal-complexity control laws and controllers which guarantee near-optimal system performance