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Adaptive dual model predictive control (DMPC) for linear systems with constant parametric uncertainties is investigated in this paper. In particular, chance constraints on output and input are considered. The online set‐membership identification and recursive least squares are utilized for shrinking the uncertain parameter set and getting the estimation point of the parameters. The dual effect represented by the parameters' prediction error is considered in the receding horizon optimization problem for easing the impact of the uncertainties and improving the performance simultaneously. Chance constraints on output are tackled by converting into convex alternatives. The output tracking problem is transformed into a quadratically constrained quadratic‐programming (QCQP) problem, which is computationally tractable. A numerical example is provided to illustrate the effectiveness of the proposed method.