The control problem of underactuated surface ships and underwater vehicles has attracted more and more attentions during the last years. Path following control aims at forcing the vehicles to converge and follow a desired path. Path following control of underactuated surface ships or underwater vehicles is an important issue to study nonlinear systems control, and it is also important in the practical implementation such as the guidance and control of marine vehicles. This paper proposes two nonlinear model predictive control algorithms to force an underactuated ship to follow a predefined path. One algorithm is based on state space model, the other is based on analytic model predictive control. In the first algorithm, the state space GPC (Generalized Predictive Control) method is used to design the path-following controller of underactuated ships. The nonlinear path following system of underactuated ships is discretized and re-arranged into state space model. Then states are augmented to get the new state space model with control increment as input. Thus the problem is becoming a typical state space GPC problem. Some characters of GPC such as cost function, receding optimization, prediction horizon and control horizon occur in the design procedure of path-following controller. The control law is derived in the form of control increment. In the second algorithm, an analytic model predictive control algorithm is used to study the path following problem of underactuated ships. In this path-following algorithm, the output-redefinition combined heading angle and cross-track error is introduced. As a result, the original single-input multiple-output (SIMO) system is transformed into an equivalent single-input single-output (SISO) system. For the transformed system, we use the analytic model predictive control method to get path-following control law in the analytical form. The analytic model predictive controller can be regarded as special feedback linearization method optimized by predictive control method. It provides a systematic method to compute control parameters rather than by try-and-error method which is often used in the exact feedback linearization control. Relative to GPC, the analytic model predictive control method provides an analytic optimal solution and decreases the computational burden, and the stability of closed-loop system is guaranteed. The path-following system of underactuated ships is guaranteed to follow and stabilize onto the desired path. Numerical simulations demonstrate the validity of the proposed control laws.
Feed-forward control of elastic-joint industrial robot based on hybrid inverse dynamic model
