Design methods of asymptotically stable adaptive consensus control of multi-agent systems composed of the first-order and the second-order regression models are presented based on inverse optimal control criterion. The proposed control schemes are derived as solutions of certain H_∞ control problems, where estimation errors of tuning parameters are regarded as external disturbances to the process. The resulting control systems are robust to uncertain system parameters and the desirable consensus tracking is achieved asymptotically via adaptation schemes and L₂-gain design parameters together with an introduction of a generating model of a leader.