Autonomous Vehicle Control Comparison

Abstract

Self-driving features rely upon autonomous control of vehicle kinetics, and this manuscript compares several disparate approaches to control predominant kinetics. Classical control using feedback of state position and velocities, open-loop optimal control, real-time optimal control, double-integrator patching filters with and without gain-tuning, and control law inversion patching filters accompanying velocity control are assessed in Simulink, and their performances are compared. Optimal controls are found via Pontryagin’s method of optimization utilizing three necessary conditions: Hamiltonian minimization, adjoint equations, and terminal transversality of the endpoint Lagrangian. It is found that real-time optimal control and control-law patching filter with velocity control incorporating optimization are the two best methods overall as judged in Monte Carlo analysis by means and standard deviations of position and rate errors and cost.

Inducing Performance of Commercial Surgical Robots in Space

“…Idealized double-integrator dynamics controlled by classical “velocity control”, sometimes labeled as P + V control, which is different than proportional + derivative or PD control. This method is articulated in Equation (3) as introduced by Banginwar in [ 39 ].…”

“…Idealized double-integrator dynamics controlled by state-of-the-art real-time optimal control techniques. This method is articulated in Equation (8) as introduced by Banginwar in [ 39 ].…”

“…Idealized double-integrator dynamics controlled by state-of-the-art real-time optimal control techniques recently augmented with automatic singular switching. This method is also articulated in Equation (8), where special attention is urged to the inversion of the potentially rank-deficient matrix [T] as proposed by Banginwar in [ 39 ].…”

Bilinear Interpolation of Three–Dimensional Gain–Scheduled Autopilots

“…Accordingly, such analysis is recommended for future research. Just last year, Banginwar [ 25 ] offered an initial proposal of such applications (still neglecting nonlinear coupling terms from the transport theorem), where follow–on efforts should incorporate the nonlinear coupling terms. Alternatively, a trajectory tracking control approach for an uncertain surface vessel using the new cascade structure of adaptive reinforcement learning algorithm and kinematic controller, feed-forward term was offered in [ 26 ], while an adaptive reinforcement learning optimal tracking control algorithm was presented in [ 27 , 28 ] for an underactuated surface vessel subject to modeling uncertainties and time-varying external disturbances.…”

“…This novel approach, the parametrization of gains, reduces the number of variables to four, allowing the system to compute the gains instantaneously with minimal computational burden. Consider a gain parametrized as a polynomial function of the scheduling variables [ 25 ]. The simplest way to tune the polynomial coefficients is to convert the polynomials into tunable surfaces in MATLAB.…”