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Abstmct --Simplified adaptive control (SAC) is origiually proposed for the class of plant which satisfies almost strictly positive real (ASPR) condition. There have been two main methods t o circumvent this condition. One is t o add parallel feedforward compensation t o an original plant resulting in n new augmented plant which satisffes t h e ASPR condition. T h e other is t o contrive t h e signal in parameter adjustment law which is a linear cornbinatiori of o u t p u t error and control law. One of t h e authors has proposed a discrete-time algorithm which belongs t o t h e former. I n this paper, a new discrete-time algorithm is proposed which belongs t o t h e latter. This algorithm can reduce t h e o u t p u t e r r o r between plant a n d model to b e bounded and moreover t o zero if t h e steady s t a t e gain of t h e plant is kept constant under parameter variation. T h e stability of the algorithm is proved by using a Eyapunov function. This algorithm is applied t o the position control of a DC servomotor. Experimental results illustrate satisfactory performance even if its load is largely changed.

A new self-tuning neuro-PID control architecture is proposed and applied to the stabilization of double inverted pendulum. The gain parameters of the PID controller are tuned using a neural network. The effectiveness of the proposed method is shown through simulation and experiment.

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