Nonlinear model predictive control of a class of continuum robots using kinematic and dynamic models

This paper examines the side brush control technologies for a novelty semi-autonomous road sweeper design. This study proposes a side brush structure and offers a brush control solution to improve working efficiency and reduce abrasive brush. For the mechanical system using a parallelogram mechanism, the direction of movement when raising and lowering the brush is always parallel to the road surface. The modeling of the side brush mechanism shows that this is a nonlinear system. Therefore, the Sliding Mode Control(SMC) was proposed and established from the dynamics equation. The Lyapunov theorem demonstrates its stability. Besides, we also consider the proportional-integral-derivative (PID) controller to evaluate the responsiveness of the linear controller for a nonlinear system. Finally, the parameters of the controllers are optimized by a genetic algorithm to consider the response of the sliding mode control compared to the PID controller to control the road sweeper side brush with different references.

Section: The Dynamic Of the Side Brushmentioning

confidence: 99%

Control of the side brush street sweeper for various road surfaces using PID and sliding mode controllers

Thanh,

Viet,

Duc

et al. 2023

“…The gains' parameters of both BSC and FOPID were tuned using a modified version of a beetle swarm optimization. In [6], a nonlinear model predictive controller (NMPC) was presented to solve the trajectory tracking problem of a continuum robot. A particle swarm optimization (PSO) algorithm was proposed to solve the limited computational burden of the NMPC.…”

Section: Introductionmentioning

confidence: 99%

Design a new hybrid controller based on an improvement version of grey wolf optimization for trajectory tracking of wheeled mobile robot

Hussein¹

2023

Nonholonomic wheeled mobile robots are considered to be multi-input multi-output systems that are performed in varying environments. This work presents the trajectory tracking control of a nonholonomic wheeled mobile robot (WMR). The Kinematic and the dynamic models of the robot were derived. A new hybrid controller was designed, which consisted of two controllers based on an optimization algorithm to solve the trajectory tracking problem. The first controller is the Fractional order PID controller, which is based on the kinematic model and has been applied to control the linear and the angular robot velocities, while the second controller is a linear quadratic regulator (LQR) and is based on the dynamic model used to control the motors' torques. A new, improved version of grey wolf optimization wasadopted to tune the parameters of the hybrid controller. The main goals of this improvement are rapid convergence towards a solution, reducing the effect of the wolves' random motion, and balancing exploitation and exploration processes. MATLAB software was used to simulate the results under an S-shape trajectory and to evaluate the robustness and performance of the proposed control unit. The simulation results demonstrated the adopted control system's activity and efficiency based on the mean square error (MSE) between the desired and actual trajectory. The values of MSE of trajectory in the X and Y directions and the orientation are [6.589*10-4(m) 8.421*10-5(m) 0.00401(rad)]T . Also, the adopted control system can generate smooth values of the control input signals without sharp spikes. The performance of the presented control system has been verified and compared with the results obtained from the other two control systems, and the simulation results have offered the superiority and effectiveness of the hybrid controller of this work.

“…MPC is also known as receding horizon control, in which a finite-horizon optimal control problem (OCP) is solved online at every control cycle. The optimal solution's first control action is used as the control input for the real system [20][21][22][23]. The main advantage of the MPC algorithm is that constraints on inputs and states are considered in the optimal problem.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive collision-free path following control for nonholonomic mobile robots

Hiep¹,

Cong²,

Phương³

2023

In this research, a model predictive collision-free path following controller is developed and applied for an omnidirectional mobile robot (OMR). The mobile robot is controlled to track a reference path while avoiding collision with obstacles. The path-following problem is reformulated into the regulation problem of an extended plant by introducing a virtual degree of freedom, the path parameter of a geometric reference curve. Then a Model Predictive Controller (MPC) is then applied to steer the mobile robot. The optimization cost function is established from the difference between the state of the robot and the parameter path. The solution of MPC can be obtained by repeatedly solving an optimal control problem (OCP) to reduce the optimization cost function to a minimum value, making the robot state as close to the state of the path as possible. Obstacle avoidance is considered by adding terms as a function of the gap between the mobile robot and the objects in front of the robot. Constraints on the states and inputs of the system are also easily considered in the optimal control problem of MPC. This makes the control inputs not exceed the allowable limits of the robot. Simulations are carried out to reveal the controller's efficiency and show how to choose the right parameters to synchronize path tracking and obstacle avoidance tasks.