This flexibility and efficiency is especially useful for trajectory planning.Ī multistage nonlinear MPC controller with prediction horizon p defines p+1 stages, representing time k (current time), k+1. Return end Path Planning Using Multistage Nonlinear MPCĬompared with the generic nonlinear MPC controller ( nlmpc object), multistage nonlinear MPC provides you with a more flexible and efficient way to implement MPC with staged costs and constraints. ) įprintf( 'Initial pose is not valid.\n') The following figure shows the truck and trailer nonlinear dynamic system.Ĭineq = TruckTrailerIneqConFcn(1,initialPose,u0. This example requires Optimization Toolbox™ and Robotics System Toolbox™ software. You can then pass the generated path to a low-level controller as a reference signal, so that it can execute the parking maneuver in real time. In this example, you design a nonlinear MPC controller that finds an optimal route to automatically park a truck with a single trailer from its initial position to its target position, which is between two static obstacles. Therefore, you need to use nonlinear MPC controller for problem formulation and solution. In such trajectory optimization problems the plant, cost function, and constraints can often be nonlinear. Because MPC finds the plant future trajectory at the same time, it can work as a powerful tool to solve trajectory optimization problems, such as autonomous parking of a vehicle and motion planning of a robot arm. Given the current states of the plant, based on the prediction model, MPC finds an optimal control sequence that minimizes cost and satisfies the constraints specified across the prediction horizon. An MPC controller uses an internal model to predict plant behavior.
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