Off-Policy Data-Driven Adaptive Fault-Tolerant Tracking Control for Nonaffine Nonlinear Systems with State Constraints

This paper investigates fault-tolerant tracking for continuous-time nonlinear systems subject to input nonaffinity and prescribed state inequality constraints. A slack-function-based transformation and a dynamic precompensator are adopted to handle state constraints and input nonaffinity, respectively. The fault model applies to redundant auxiliary-drive channels of the precompensator and includes stuck, outage, bias, and loss-of-effectiveness modes. Under fault-free operation, an off-policy integral Bellman equation is formed from measured data, and the critic and actor weights are computed by regularized projected least squares. Online, the learned actor is embedded in an adaptive fault-tolerant controller. The stability analysis gives a local forward-invariance condition and retains policy-approximation, regression, and derivative-filter errors in the ultimate bound. Direct closed-loop simulations of a nonaffine continuous stirred tank reactor include single-drive outage, repeated fault switching, and measurement noise. The supplied code and raw trajectories reproduce all comparisons and quantify the regression conditioning and iteration residuals.