Guoqing Zhang

Robust Neural Control for Dynamic Positioning Ships With the Optimum-Seeking Guidance

This paper deals with the optimum dynamic positioning control problem for marine ships in the presence of actuator gain uncertainties and unknown environmental disturbances. The proposed approach is formulated as two modules, i.e., the guidance part and the control part. By utilizing the improved extremum seeking algorithm, the optimum-seeking guidance is developed in this note to generate the reasonable heading guidance for dynamic positioning ships. The main purpose of this design is to ensure the closed-loop system running efficiently and environment-friendly in practice. Combined with the proposed guidance principle, a robust neural control algorithm is developed based on the dynamic surface control, neural networks, and the robust neural damping technique. In this algorithm, the strong couplings of state variables and the gain uncertainty of actuators are tackled, and the system uncertainties are compensated requiring less (or no) information of the hydrodynamic structure, the actuator model and the external disturbances. Considerable effort is made to guarantee the semiglobal uniform ultimate bounded stability by employing the Lyapunov theory. The advantages of the proposed control scheme could be summarized as two points. First, the control approach is with the properties of optimization and energy-saving, which is meaningful for applying the theoretical algorithm. Second, the pitch ratio of thrusters is selected as the control inputs of interest, which is measurable in the practical plant. These characteristics would facilitate the implementation of the algorithm in engineering. Two examples are provided to verify the performance of the proposed scheme.

Robust neural output-feedback stabilization for stochastic nonlinear process with time-varying delay and unknown dead zone

This article investigates the output-feedback control of a class of stochastic nonlinear system with time-varying delay and unknown dead zone. A robust neural stabilizing algorithm is proposed by using the circle criterion, the NNs approximation and the MLP (minimum learning parameter) technique. In the scheme, the nonlinear observer is first designed to estimate the unmeasurable states and the assumption “linear growth” of the nonlinear function is released. Furthermore, the uncertainty of the whole system (including the perturbation of time-varying delay) is lumped and compensated by employing one RBF NNs (radial basis function neural networks). Though, only two weight-norm related parameters are required to be updated online for the merit of the MLP technique. And the gain-inversion related adaptive law is targetly designed to mitigate the adverse effect of unknown dead zone. Comparing with the previous work, the proposed algorithm obtains the advantage: a concise form and easy to implementation due to its less computational burden. The theoretical analysis and comparison example demonstrate the substantial effectiveness of the proposed scheme.

Leader-follower formation control of underactuated surface vehicles based on sliding mode control and parameter estimation

This paper studies the leader-follower formation control of underactuated surface vehicles with model uncertainties and environmental disturbances. A parameter estimation and upper bound estimation based sliding mode control scheme is proposed to solve the problem of the unknown plant parameters and environmental disturbances. For each of these leader-follower formation systems, the dynamic equations of position and attitude are analyzed using coordinate transformation with the aid of the backstepping technique. All the variables are guaranteed to be uniformly ultimately bounded stable in the closed-loop system, which is proven by the distribution design Lyapunov function synthesis. The main advantages of this approach are that: first, parameter estimation based sliding mode control can enhance the robustness of the closed-loop system in presence of model uncertainties and environmental disturbances; second, a continuous function is developed to replace the signum function in the design of sliding mode scheme, which devotes to reduce the chattering of the control system. Finally, numerical simulations are given to demonstrate the effectiveness of the proposed method.

Adaptive output-feedback formation control for underactuated surface vessels

ABSTRACT This paper investigates the leader–follower formation problem of underactuated surface vessels. Velocities of both leader and follower vessels are unavailable. Model uncertainties and ocean disturbances are also considered. By incorporating adaptive control, neural networks (NNs), the high-gain observer (HGO) and the minimal learning parameter (MLP) algorithm in the backstepping procedure, a novel adaptive output-feedback formation control scheme is developed. We show that formation errors can be guaranteed to be semiglobally uniformly ultimately bounded (SGUUB) with the proposed controller. Compared with existing methods, the formation can be achieved only with position and yaw angle of both leader and follower. Meanwhile, the developed scheme can enhance the robustness of the closed-loop system with less computational effort, where only two online parameters need to be tuned. Simulation and comparison results are provided to illustrate the effectiveness of theoretical results.

MLP-based adaptive neural control of nonlinear time-delay systems with the unknown hysteresis

ABSTRACT In this note, the authors study the tracking problem for uncertain nonlinear time-delay systems with unknown non-smooth hysteresis described by the generalised Prandtl–Ishlinskii (P-I) model. A minimal learning parameters (MLP)-based adaptive neural algorithm is developed by fusion of the Lyapunov–Krasovskii functional, dynamic surface control technique and MLP approach without constructing a hysteresis inverse. Unlike the existing results, the main innovation can be summarised as that the proposed algorithm requires less knowledge of the plant and independent of the P-I hysteresis operator, i.e. the hysteresis effect is unknown for the control design. Thus, the outstanding advantage of the corresponding scheme is that the control law is with a concise form and easy to implement in practice due to less computational burden. The proposed controller guarantees that the tracking error converges to a small neighbourhood of zero and all states of the closed-loop system are stabilised. A simulation example demonstrates the effectiveness of the proposed scheme.

Performance Improvement of Consensus Tracking for Linear Multiagent Systems With Input Saturation: A Gain Scheduled Approach

For leader-following multiagent systems with input saturation, the existing protocols use a low gain feedback approach to achieve semi-global consensus. The main drawback of this approach is the ineffective utilization of the actuator potential, resulting in bad performance. To improve the transient performance of the consensus tracking, this paper proposes a gain scheduled approach for multiagent systems subject to the saturator saturations. A novel kind of scheduler-based protocols are proposed, which consists of state feedback controllers with time-varying gain and parameter schedulers. The role of the controllers is to achieve the consensus tracking, while the schedulers can accelerate this consensus progress by enlarging the gain parameter. To remove the dependence of the schedulers on global information, a minimum-value-based consensus algorithm is put forward, with idea of driving all values of agents throughout the network to their minimum value. Its implementation is guaranteed by the network-topology connectivity. Finally, our approach is further extended to the case where the leader’s control input is nonzero, time-varying, and bounded. The discontinuous protocol and its continuous approximation counterpart are designed, yielding the exact- and quasi-consensus tracking, respectively. Simulation results verify the theoretical analysis.