Curved Path Following Controller for 4W Skid-Steering Mobile Robots Using Backstepping

Abstract

Path following is a fundamental problem in skid-steered mobile robots (SSMR). In this study, a Lyapunov stable curved path following controller was designed to generate the steering control command for an SSMR. In contrast to the existing path following controller design methods, either the complete dynamic model of the robot is considered or not, the steering dynamic characteristics approximated by a first-order model are considered in this study. Together with the kinematic model, a steering control law for following a curved path was designed by using the backstepping technique and Lyapunov stability theory. The proposed method was verified on a real SSMR platform to realize following the straight-line, square, and circular paths. Compared with the steering control law without considering the steering dynamics, the proposed method can make the robot converge to the predefined path faster with a smaller error overshoot.

Design and Analysis of an Input–Output Linearization-Based Trajectory Tracking Controller for Skid-Steering Mobile Robots

“…Assume that the control gains satisfy the conditions expressed in (42) and that φ(t) in ( 43) is a persistently exciting signal. Then, the solutions y(t) and η(t) of the external and internal dynamics in (26) and (35), respectively, are uniformly ultimately bounded.…”

“…the internal dynamics (35) have the same structure as the nonlinear system (12). For d 0 = 0, the control gains satisfying (42) and φ(t) in ( 43) are assumed to be a persistently exciting signal; the local exponential stability of the internal dynamics was proven in Proposition 1.…”

“…Paper [34] reported a predictive tracking controller using an error-based dynamic model approach, guaranteing motion control of a skid-steering vehicle. In [35], a Lyapunov stable path-following controller was designed to generate the steering control command for a skid-steering mobile robot. More recently, a complex nonlinear controller was introduced in [36] with the aim of ensuring trajectory tracking of a skid-steering mobile robot.…”

Adaptive Skid-Steering Control Approach for Robots on Uncertain Inclined Planes with Redundant Load-Bearing Mobility

“…Modelbased controllers rely on accurate mathematical models of robots to predict the response corresponding to control inputs and disturbances and generate appropriate control actions. Model-based controllers can be further categorized into two categories: (a) robust controllers, e.g., sliding mode controller (SMC) [18], adaptive controller [19], backstepping controller [20], H-infinity controller [21], and fuzzy logic [22], which ensure stability and satisfactory performance despite uncertainties. (b) Optimal controllers, e.g., model predictive controller (MPC) [23], linear quadratic regulator (LQR) [24], iterative learning controller [25], differential flatness-based controller [26], and nonlinear MPC [27] controller, which optimize specific performance criteria while adhering to constraints and system dynamics.…”

ROS-based Controller for a Two-Wheeled Self-Balancing Robot

“…Because LTI controls are susceptible to external disturbances, unmodeled dynamics, and uncertainties, different nonlinear control strategies have been designed. For example, Backstepping [51], [52], [53], [54]. Sliding mode control (SMC) [55], [56], [57], [58], adaptive control [63], [60], [62], [61], [63], fuzzy logic control in [69], [67], [68], [66], and antidisturbance control [64], [65].…”