Space-time adaptive processing (STAP) for airborne early warning radar has been a very active area of research since the late 1980's. An airborne rectangular planar array antenna is usually configured into subarrays and then partial adaptive processing is applied to the outputs of these subarrays. In practice, three kinds of errors are often encountered, i.e., the array gain and phase errors existing in each element, the channel gain and hase errors, and the clutter covariance matrix estimation errors due to insufficient secondary data samples. These errors not only degrade the clutter suppression performance, but also cause the adapted array patterns to suffer much distortion (high sidelobes and distorted mainbeams), which may result in the rise of falsealarm probability and make the adaptive monopulse tracking and sidelobe blankering more difficult. In this paper, the causes of the above three kinds of errors to array pattern distortion are discussed and a novel quadratic soft constraint factored approach is proposed to precisely control the peak sidelobe level of adapted patterns. The soft constraint factor can be determined explicitly according to the peak sidelobe level desired and the known or desired tolerant error standard deviations. Numerical results obtained by using high-fidelity simulated airborne radar clutter data are provided to illustrate the performance of the proposed approach. Although the method is presented for STAP, it can be directly applied to the conventional adaptive beamforming for rectangular planar arrays used to suppress jammers.