Combining the benefits of robust situational awareness of human operators with the efficiency and precision of automatic control has been an important topic of human-machine shared control. The emphasis is on keeping human operators in the loop while automatic control providing assistance to improve task performance. Given a task with specific subgoals, execution of a task using blended shared control involves predicting the operator's intent of subgoal transitions and deciding the blending weights for inputs from the human operator and automatic control. In this paper we address the problem of subgoal adjustment in blended shared control which is typically initiated by the operator's intent and necessary to sustain the shared control performance for changing subgoal conditions. First, we provide a method to predict operator's intent of visiting a subgoal. Based on intent prediction, we propose a method for subgoal adjustment where the adjustment is encoded by a hyperrectangle. The volume of the hyper-rectangle is obtained by using a hyperbolic slope transition function which is based on the distance between subgoals. The adjustment actions within the hyper-rectangle are facilitated by a skill-weighted action integral that takes into consideration the skill level of the operator. The approach is tested on a scaled hydraulic excavator platform with multiple novice operators and a skilled operator. Experimental results are presented and discussed.