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One of the amazing successes of biological systems is the animal's ability to learn to control the complicated dynamics of their muscles and joints smoothly and efficiently. Traditional engineering control techniques, on the other hand, often do not perform well when confronted with intrinsically complex systems with many degrees of freedom, such as robot arm (human arm). In this paper, we propose a model of biological motor control for generation of goal-directed multi-joint arm movements, and study the formation of muscle control inputs and invariant kinematics features of movement. The model has a hierarchical structure that can determine the control inputs for a set of redundant muscles without any inverse computation. Calculation of motor commands is divided into two stages, each of which performs a transformation of motor commands from one coordinate system to another. At the first level, a central controller in the brain accepts instructions from higher centres, which represent the motor goal in the Cartesian space. The controller computes joint trajectories and excitation signals according to a minimum jerk criterion. At the second level, a neural network in the spinal cord translates the excitation signals and equilibrium trajectories into control commands to three pairs of antagonist muscles which are redundant for a two-joint arm. No inverse computation is required in the determination of individual muscle commands. This intelligent neuro-adaptive model is used as a hybrid force/position controller for a dual arm. To optimise the neural network learning strategy, a hybrid neuro-genetic algorithm is introduced and simulation results are given for comparisons.