In this paper, a new robust controller is developed and analyzed for an autonomous vehicle (AV) with nonholonomic dynamics driving in a 2D plane that can avoid colliding with a set of obstacles despite the presence of uncertainties in the mathematical model of the AV. The state variables and their velocities are considered to be measurable (two simple coordinates and three angles). The controller is based on the integral sliding mode (ISM) concept, which aims to minimize the current state’s convex (but not necessarily strongly convex) function. A cost function’s subgradient is likewise designed to be measured online. The averaged subgradient (ASG) technique is used to build and analyze an optimization algorithm. The major findings show that the intended regime (non-stationary analogue of sliding surface) can be reached from the start of the process and deriving an explicit upper bound for the cost function decrement, i.e., proving functional convergence and estimating the rate of convergence, thereby allowing for multiple obstacle avoidance. The proposed strategy is shown to perform effectively with a numerical example.
- Averaged subgradient method
- Car obstacle avoidance
- Integral sliding mode control
- Nonholonomic dynamic model