AbstrPct--The design and implementation of a new adaptive nonlinear predictive controller is presented using a general nonlinear model and variable transformations. The resulting controller is similar in form to standard linear model predictive controllers and can be tuned analogously. Alternatively, the controller can be tuned using a single parameter. The design is computationally efficient. The controller is updated on-line without recalculating the controller gain matrix, which involves a matrix inversion. The new controller is compared to a PI controller and to an adaptive linear predictive controller through simulations of a continuous stirred-tank reactor. The effects of modeling errors on the new controller are also shown with simulations. 1. MTRODUaION Computer process control, beginning in the 196Os, initially used traditional linear control algorithms. However, the highly nonlinear characteristics of many processes caused problems for these controllers. Consequently, in the 197Os, self-regulating controllers Cl, 23 were developed to enhance explicit and implicit model-based controllers and controller tuning. The process industries, however, found few successes with the early, hard-to-tune adaptive techniques [3]. Meanwhile, moving-horizon and iinear-programming methods were being revived in the nonadaptive, model-based predictive controllers (MPC), dynamic matrix control (DMC) [4], and model-predictive heuristic control [SJ. Industrial successes with MPCs renewed academic interest in these methods and new formulations of MPCs emerged [6]. These multivariable controllers were based on easily understood process models, could incorporate constraints, and were relatively easy to tune. In addition, their performance seemed less sensitive to varying time delays, one of the major limitations of the early adaptive controllers. To improve the robustness of the adaptive controllers, some researchers began to employ extended-horizon strategies [7]. Likewise, predictive controllers were improved by incorporating adaptive techniques [87. During the past several years, some researchers have started to focus on strategies that directly compensate for process nonlinearities in controller design [9, lo]. Naturally, these nonlinear control concepts have been introduced into adaptive-type controllers [ 11, 127 and into predictive-type controllers [ 13-161. The nonlinear control problem has also been approached numerically using nonlinear optimization software packages [ 17, 181. In this paper, an adaptive