Nevertheless, it has been established that in certain states and disturbances go unmeasured; the disturbance-rejection may not happen even though a bounded output might exist. It is clear from the above discussion that there is a need for the design of a robust controller that can handle a system operating under uncertainties. In this context, this work presents the analysis and design of a systematic nonlinear control technique. Its goal is to ensure the global asymptotical stability of an uncertain system operating on feedback. It is characterized by output monitoring and disturbance decoupling for a set of nonlinear control plants having parameters and model uncertainties. To address this challenge, an advanced SMC method is proposed for a nonlinear forecast system [34; 35; 36]. It was recognized that the projected regulator is flexible and handles undetermined disturbances. In addition, it does not have the typical SMC chattering problem. Moreover, an analytical form of the I/O FBL control is developed. The controller integrates PSO optimization with conventional FBL and fuzzy logic. This work comprises the development of a fundamental control technique for FBL using SMC. Next, a fuzzy-PSO technique is integrated to ensure that the sliding mode state is reached even during external disturbance or uncertainties in the control parameters. For sliding mode, this fuzzy-logic-based PSO technique is used to enhance control ability and decrease chattering. Generally, the feedback linearization technique (FBL) has satisfactory performance, provided that disturbances, modeling errors and parameter uncertainties are ignored. This is acceptable in an ideal scenario. However, in practice, external disturbances and/orinherent parameter variations do occur, resulting in the degradation of the performance of the FBL. Moreover, the FBL is mainly based on the complicated differential geometry theory, which creates major difficulties for engineers when implementing it in practical situations. By employing the Taylor series to represent the nonlinear systems by a polynomial model, computing the decoupling control input becomes generic. We believe that the proposed polynomial solution of the FBL problem is one of the major contributions of this paper. It is worth mentioning that the performance of the FBL control will hardly be affected by injecting disturbances (whether internal or external) into the model. That is to say; the model does not correct for the disturbances. The SMC approach is, therefore, used to minimize the effects of these disturbances leading to the enhancement of the dynamic behavior of the control and state variables. Indeed, the system's closed-loop will be stabilized in the neighborhood of the focused equilibrium point while employing the feedback control design. However, the system dynamics become affected by chattering, which is unacceptable in terms of physical process constraints. In addition, because of the uncertainties in the model, complete elimination of the disturbances cannot be achieved using only conventional SMC. Consequently, the computed values of the state variables, which also influence the SMC through the reaching law, remain incorrect. In this respect and in order to attain the optimal controller gains associated with the above problems, one must reduce the long gap of the sliding surface, which is a delicate control task. To solve this problem, a fuzzy technique along with a PSO method will be implemented to allow accurate tuning of the controller gains. The robustness and steady-state accuracy will be established in an efficient way, even in the case of a hard desired output trajectory. This latter objective will construct the key contribution of this work. This paper explicitly emphasizes controlling a CSTR by taking temperature measurements against concentration measurements. It is required that the controller regulate the temperature to keep it below a maximum threshold.