Abstract
In the field of automatic control system design, adaptive inverse is a powerful control technique. It identifies the system model and controls automatically without having prior knowledge about the dynamics of plant. In this paper neural network based adaptive inverse controller is proposed to control a MIMO system. Multi layer perception and back propagation are combinedly used in this investigation to design the NN learning algorithm. The developed structure represents the ability to identify and control the MIMO system. Mathematical derivation and simulation results for both plant identification and control are shown in this paper. Further, to prove the superiority of the proposed technique, performances are compared with recursive least square (RLS) method for the same MIMO system. RLS based adaptive inverse scheme is discussed in this paper for plant identification and control. Also the obtained simulated results are compared for both plant parameter estimation and tracking trajectory performance.
Highlights
Prior knowledge is an important factor for almost every conventional control system
Adaptive inverse control technique is considered in this paper, which is based on neural network using multi layer perception for multi input and multi output (MIMO) system
Transfer function of a MIMO system is used for this investigation
Summary
Prior knowledge is an important factor for almost every conventional control system. Such as in continuous time system, number of poles and zeros or the limit of upper bounds on the order of the plant are assumed to be known [1], [2], [3], [4]. The known time delay is crucial for discretetime systems [5], [6], [7] To overcome these difficulties, the adaptive control methods were developed. A different neural network technique is considered using a feed-forward inverse recurrent method based PD controller [15]. Adaptive inverse control technique is considered in this paper, which is based on neural network using multi layer perception for MIMO system.
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