A performance oriented multi-loop approach to the tracking control of linear motor drive systems with input saturation, state constraints, parametric uncertainties and input disturbances is proposed. In the inner loop, a constrained adaptive robust control (ARC) law is synthesized to achieve the required robust tracking performances with respect to on-line replanned trajectory in the presence of input saturation and various types of uncertainties. In the middle loop, a set-membership identification (SMI) algorithm is implemented to obtain a monotonically decreasing estimate of the upper bound of the inertia so that more aggressive trajectory replanning can be done. In the outer loop, a replanned trajectory is generated to minimize the converging time of the overall system response to the desired target while not violating various constraints. It is theoretically shown that the resulting closed-loop system can track any feasible desired trajectory with a guaranteed converging time and steady-state tracking accuracy without violating the state constraints. Experimental results obtained on a HIWIN linear motor show that the proposed algorithm indeed achieves closed-loop stability and small steady-state tracking errors with a transient performance much better than that of the unconstrained ARC algorithm.