Jiangqiang Hu

ROBUST ADAPTIVE FUZZY TRACKING CONTROL FOR A CLASS OF PERTURBED UNCERTAIN NONLINEAR SYSTEMS WITH UVCGF

In this paper, a robust adaptive fuzzy controller is presented for a wide class of perturbed uncertain nonlinear system with unknown virtual control gain function (UVCGF). The Mamdani fuzzy system is used to approximate unstructured uncertain functions in the system. The proposed algorithm, which incorporated Nussbaum-type gain into the decoupled backstepping approach, does not require a priori knowledge of the sign of UVCGF, and circumvents the controller-singularity problem gracefully in some existing literatures. It proved that the tracking error can be driven to a small residual set while keeping all signals in the closed-loop system semi-globally uniformly ultimately bounded (SGUUB). Simulation results are presented to validate the effectiveness of the proposed controller.

Robust adaptive backstepping design for course-keeping control of ship with parameter uncertainty and input saturation

In this paper, a novel robust adaptive control algorithm is proposed for a class of ship course autopilot with parameter uncertainty and input saturation. With the help of Lyapunov stability theory and adaptive backstepping technique, one controller is constructed by considering parameter uncertainty and actuator saturation constraints, and the stability analysis subject to the effect of input saturation constrains is conducted by employing an auxiliary design system. It is also proved that the proposed algorithm could guarantee the closed-loop system to be uniformly ultimately bounded and the output converges to a small neighborhood of zero. Simulation results are given to illustrate the effectiveness and the performance of the proposed scheme.

Direct adaptive NN control of ship course autopilot with input saturation

In this paper, a novel direct adaptive NN control algorithm is proposed for a class of ship course autopilot with input saturation. Neural networks (NNs) are used to tackle unknown nonlinear function, and then an adaptive NN controller is constructed by combining Lyapunov function and the backstepping technique. By utilizing a special property of the affine term, the developed scheme avoids the controller singularity problem completely, and the stability analysis subject to the effect of input saturation constrains is conducted by employing an auxiliary design system. It is also proved that the proposed algorithm could guarantee the closed-loop system to be uniformly ultimately bounded and the output of the system is proven to converge to a small neighborhood of the desired trajectory. Finally, simulation studies are given to illustrate the effectiveness and the performance of the proposed scheme.

A Mutation-Classified, Parameter-Dynamic Immunological Algorithm for Global Optimization

Based on the artificial immune system, a new clonal selection algorithm is proposed to perform global optimization. The concept of classified mutation is defined and the dynamic adjustment methods of some evolution parameters are introduced. The proposed algorithm is applied to several benchmark problems, and its performance is compared with other approaches in the literature. The results indicate that the new algorithm is a significant advance in clonal selection and a viable alternative.

Adaptive control for ship steering based on clonal selection model identification

Using clonal selection model identification, an adaptive PD control algorithm with disturbance compensation is proposed for uncertain and nonlinear system in ship steering. Actual ship with disturbance and model uncertainty, regarded as a black box, is dynamically identified as second-order linear model with disturbance terms; then, certainty equivalence principle is applied to tune the PD control parameters while the disturbance is compensated. The combination of previous elitist reservation and stochastic initialization for the initial population in clonal selection algorithm improves the optimization efficiency for dynamic problem. Simulations on the third-order nonlinear cargo vessel verify that the proposed adaptive control algorithm possesses strong robustness and improves the capability of PD controller in inhibiting stable error.

Path following and stabilization of underactuated surface ships

To solve the path following and stabilization problems for underactuated surface ships with nonintegrable acceleration constraint, a nonlinear feedback algorithm is presented using decoupling control method and iterative nonlinear sliding mode designing approach. The saturations on actuators and unknown environmental disturbances are also explicitly considered. Integrating with simple increment feedback control laws, a dynamic control strategy is developed to fulfill the underactuated following and stabilization objectives with only surge force and yaw movement available. Numerical simulation results on a full nonlinear hydrodynamic model of a training ship are presented to validate the effectiveness and robustness of the proposed controller.

Path following of underactuated surface ships

The path following problem is concerned for conventional surface ships with second order nonholonomic constraints. A nonlinear feedback algorithm is presented using decoupling control method. The cross track error and heading error are stabilized by means of the rudder alone and the thruster is left to adjust the forward speed. The underactuated following control objective is achieved without a reference orientation generated by a ship model. The estimation of systemic uncertainties and disturbances and the yaw velocity PE (persistent excitation) conditions are not required. Computer simulation results on a full nonlinear hydrodynamic ship model of M.V. YULONG are provided to validate the effectiveness and robustness of the proposed controller.

FNN-based Robust Adaptive Tracking Control for a Class of Uncertain Nonlinear Systems

An FNN (fuzzy neural network)-based robust adaptive controller is presented for a class of perturbed uncertain nonlinear system with unknown virtual control gain functions (UVCGF). The FNN is used to approximate unstructured uncertain functions. The proposed algorithm, which combined Nussbaum gain with the decoupled backstepping techniques, does not require a priori knowledge of the signs of the UVCGF, and circumvents the controller-singularity problem gracefully. It proved that the tracking error can be driven to a small residual set while keeping all signals in the closed loop semi-globally uniformly ultimately bounded (SGUUB). Numerical simulation results are presented to validate the effectiveness

An adaptive genetic algorithm based on arctangent function

To speed up convergence rate and improve local convergence in genetic algorithm, nonlinear adaptive crossover probability and mutation probability function are designed. They are based on the arctangent function with three parameters of maximal fitness, minimal fitness and average fitness. An improved adaptive genetic algorithm is proposed based on the two designed functions. Simulation results prove that the proposed improved adaptive genetic algorithm possesses faster convergence speed than GA and AGA presented by Srinvas, stronger optimization ability and avoid the premature effectively.

A hybrid clonal selection algorithm for solving job-shop scheduling problems

An improved clonal selection algorithm (ICSA) combined with neighborhood local search approach is proposed for solving job shop scheduling problems in this paper. In ICSA, the antibody population is decomposed into three subsets: the bests, mediums and worsts. The bests aim to find the local optimum by mutation. The mediums experience crossover with a randomly selected best antibody to explore the global optima space, and the randomly generated antibodies replace the worsts to ensure the population diversity. Only when the ICSA seems stagnating its permitted inserting local search procedure based on Nowicki and Smutnickis neighborhood to further exploit the local optima while the current mutation scheme is alternated by another. Furthermore, after each generation of ICSA and local search procedure, the population diversity is checked, and then one of candidates with the same schedule is preserved and the others are regenerated randomly. The proposed algorithm is examined using some well-known benchmark problems and numerical results validate its effectiveness.