Abstarct: In this thesis, the issue of the flexibility in joints and its effects is reviewed. Since the fuzzy controller has shown a good performance in the flexible-joint robot control, we introduce and design the fuzzy controller for the flexible-joint robot manipulator. Despite of the advantages of fuzzy controller, adjustment of the fuzzy control parameters by an expert and complexity of stability analysis show always challenges. Therefore, the use of intelligent algorithms in the optimal design of the fuzzy controller has been used. Among the intelligent optimization algorithms, particle swarm optimization algorithm has an appropriate response in optimization of the complex and non-linear problems with a high convergence rate. Hence, this thesis presents the design of controllers with guaranteed stability by using a particle swarm optimization algorithm. In this regard, designing an optimal linear controller with guaranteed stability by employing particle swarm optimization algorithm is presented for linear systems. Then, the proposed method is extended for nonlinear systems such as a robotic manipulator. After that, by applying particle swarm optimization algorithm the fuzzy controller is designed somehow its stability is guaranteed and the controller provides an optimal performance. The design of optimal fuzzy controller are presented for off-line and online types. Innovation of this thesis is the use of particle swarm optimization algorithm to design the guaranteed stability fuzzy controller. Its advantage is the simplicity of design compared to conventional Lyapunov method which is difficult to analyze the fuzzy control system applied on the flexible joint robot manipulator equipped with electric motors. Also, a linear optimal controller design with guaranteed stability by applying particle swarm optimization algorithm for linear systems and its generalization for non-linear systems such as robot manipulator is novel.