Nonlinear model predictive control of ironless linear motors

\u3cp\u3eAs the demands for accuracy and throughput in industrial positioning systems are increasing, classical control of ironless linear motors (ILMs) is facing its limit. Classical control scheme of an ILM typically consists of a simple sinusoidal commutation algorithm and a PID feedback controller. Classical commutation cannot compensate for parasitic effects, while classical PID feedback controller cannot guarantee constraints satisfaction. This problem can be addressed by replacing classical commutation with optimal commutation and PID controller with linear model predictive controller (LMPC). However, this LMPC and optimal commutation scheme requires solving two separate optimization problems, which is not optimal and can lead to infeasibility. In this paper we present a nonlinear model predictive control (NMPC) scheme for ILMs. The scheme requires solving only a single optimization problem. It can guarantee constraints satisfaction and is capable of compensating for parasitic forces. Simulation results are presented for demonstration.\u3c/p\u3

Nonlinear model predictive control of ironless linear motors

As the demands for accuracy and throughput in industrial positioning systems are increasing, classical control of ironless linear motors (ILMs) is facing its limit. Classical control scheme of an ILM typically consists of a simple sinusoidal commutation algorithm and a PID feedback controller. Classical commutation cannot compensate for parasitic effects, while classical PID feedback controller cannot guarantee constraints satisfaction. This problem can be addressed by replacing classical commutation with optimal commutation and PID controller with linear model predictive controller (LMPC). However, this LMPC and optimal commutation scheme requires solving two separate optimization problems, which is not optimal and can lead to infeasibility. In this paper we present a nonlinear model predictive control (NMPC) scheme for ILMs. The scheme requires solving only a single optimization problem. It can guarantee constraints satisfaction and is capable of compensating for parasitic forces. Simulation results are presented for demonstration