## 1 Introduction
applied the combination to the secondary voltage and frequency control of islanded microgrid, where each distributed generator was individually controlled by a discrete-time MPC cascaded with an input-output FL to have a sparse communication network [14]. [PERSON] et al. applied the combination to the hydrogen excess ratio regulation of proton exchange membrane fuel cells, where a pseudo-reference discrete-time MPC cascaded with an input-output FL was developed to reduce the overshoots of responses [15]. [PERSON] et al. applied their combination to the eddy current de-tumbling of space tumbling targets, where an input-output FL cascaded with a discrete-time MPC with input constraints was developed to form a quadratic programming problem [16]. [PERSON] et al. applied the combination to a quadcopter, where a discrete-time MPC cascaded with an input-output feedback linearization was applied to perform a trajectory tracking control. Furthermore, a disturbance observer was designed to estimate wind disturbances [17]. [PERSON] et al. applied the combination to a pressurized water reactor, where a dynamic neural network model of the reactor was identified by using the quasi-Newton algorithm, and a discrete-time MPC cascaded with an input-output FL was applied, based on the identified neural network model [18]. In review of literature, there are diverse combinations of the MPC and FL applied to nonlinear systems with constraints. However, it is difficult to implement the complex formulations in the real systems, and the required computational cost is high. Moreover, there are a limited number of papers addressing input constraints. In this study, the combination of the continuous-time MPC and FLis still proposed, but the combination aims to simplify the formulations and reduce computational cost for trajectory tracking control of nonlinear systems with constraints. The proposed approach first applies input-state feedback linearization to obtain the canonical form of a linear system, and the control signal is converted to be a virtual input signal, which is a function of the states of the original nonlinear system. Further to this, the input constraints are converted to be state-dependent. To simplify the formulations, and to reduce the computational cost, a limited number of Laguerre functions are utilized to approximate the control signals in the MPC, and then the approximated signals are substituted into the input constraints and the linear state equations. Therefore, a constrained quadratic optimization problem can be formulated, where the determined design parameters are the coefficients associated with the Laguerre functions, and the [PERSON]'s quadratic programming algorithm is used to determine the coefficients. Moreover, this study also summarizes several schemes to perform the linearization of nonlinear systems, and the illustrative examples show the control performance comparisons of the schemes, combined with linear control strategies. The rest of the paper is organized as follows. Section 2 presents common linear or linear-like formulation schemes. Section 3 proposes a new combination of the MPC and feedback linearization. Section 4 demonstrates two examples, the SISO and MIMO systems. Section 5 summarizes significant findings.