In this paper, we report some results on hardware and software co-design of an adaptive linear neuron (ADALINE) based control system. A discrete-time Proportional-Integral-Derivative (PID) controller is designed based on the mathematical model of the plant. The parameters of the plant model are identified on-line by an ADALINE neural network. In order to efficiently and economically implement the designed control system, a Field Programmable Gate Array (FPGA) chip is employed to process the

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... ured data and generate control signals. Moreover, a microprocessor is exploited to perform the core computation of the ADALINE algorithm. Throughout the paper, we design and test the control system for a permanent magnetic DC motor. Our experiment results demonstrate the effectiveness of the proposed approach. It is worth noting that the experimental bed in the present paper can also be used as a low-cost general prototype to satisfactorily test adaptive control systems, owing to the benefit of software and hardware co-design.