Recently, the passivity results for linear time-invariant systems were successfully extended to nonlinear and nonstationary systems, thus guaranteeing stability of adaptive control of nonlinear square systems. Based on this theoretical development, this paper presents the development of a new class of direct adaptive controllers, which employ a new decentralized adaptation law mechanism that is developed from the simple adaptive control technique. The resulting direct adaptive control methodology is referred to as decentralized simple adaptive control. A simplification of this new control algorithm, referred to as decentralized modified simple adaptive control, is also presented. In addition, it is shown that both control methodologies can be modified to avoid divergence in practical situations, where the trajectory tracking errors cannot reach zero. Using Lyapunov direct method and Lasalle's invariance principle for nonautonomous systems, the formal proof of stability is established. As well, a numerical simulation study for a trajectory tracking problem by a rigid-joint manipulator is presented to illustrate the new adaptive control approaches. DECENTRALIZED SIMPLE ADAPTIVE CONTROL 751 CB be a positive definite symmetric (PDS). A recent algebraic proof of this important statement can be found in [10].Over the years, a variety of direct adaptive control laws have been developed to address the problem of time varying the gains of a controller so that the plant closed-loop characteristics match those defined by a reference model. However, most of the research in this area is based on the assumption that prior knowledge of the unknown plant to be controlled is available, and/or requires the plant to be of the same order as the reference model, and/or requires full-state feedback or observers. To mitigate these stringent requirements, the simple adaptive control (SAC) approach was developed by Sobel et al. [11], Barkana et al. [12] and Barkana and Kaufman [13]. Using the ASP results, the stability of the SAC technique for square LTI systems was rigorously established by Kaufman, Barkana and Sobel [14]. This direct adaptive output feedback method is based upon the command generator tracker methodology [15] and requires the plant to track the ideal model, which is an ideal representation of the plant only as far as its outputs represent the desired output behavior of the plant. For this reason, this direct adaptive control methodology has been successfully applied for the control of number of large-scale systems without requiring large-order adaptive controllers.However, although greatly reduced when compared with standard model following techniques, in some applications the SAC technique may still present a design complexity issue arising from the large number of parameters and coefficients to select. In fact, the calculation of the control input involves a stabilizing output feedback control gain and two feedforward control gains, each calculated as the summation of a proportional and an integral control gain compo...