96 results about "Barrier lyapunov function" patented technology

The invention relates to the technical field of mechanical arm control, in particular to a self-adaptive fuzzy fixed time control method based on an output-limited mechanical arm, and the method comprises the following steps: S1, constructing a mechanical arm dynamic model; s2, setting constraint conditions of an output state of the mechanical arm dynamic model; s3, defining a tracking error and a virtual control law of the mechanical arm dynamic model; s4, on the basis of output state limitation and fixed time control, an actual control law is designed to serve as input of the mechanical arm dynamic model, and an adaptive law is constructed; and S5, a three-level logarithmic obstacle Lyapunov function is constructed, the three-level logarithmic obstacle Lyapunov function is combined with the virtual control law, the actual control law and the self-adaptive law step by step and substituted into the virtual control law, the actual control law and the self-adaptive law, and then it is guaranteed that the output state of the mechanical arm dynamic model does not exceed the preset safety boundary according to the actual fixed time control lemma. According to the invention, high-precision trajectory tracking can be realized on the premise of ensuring safe operation of the mechanical arm.

The invention discloses a shimmy suppression method for a four-wheel independent steer-by-wire system. The shimmy suppression method comprises the following steps: establishing a wheel shimmy kinetic model of the four-wheel independent steer-by-wire system; designing a barrier Lyapunov function, and converting the swing amplitude constraint into a state constraint condition; designing a backstepping sliding mode controller parameter self-tuning strategy based on the shimmy frequency; designing a back-stepping sliding mode controller with self-adaptive swing amplitude-frequency characteristics based on the state constraint condition; based on a designed backstepping sliding mode controller, active self-adaptive control is carried out on shimmy phenomena of wheels with different frequencies and amplitudes, and then suppression of the shimmy phenomenon of the four-wheel independent steer-by-wire system is completed. According to the amplitude-frequency characteristics of the shimmy phenomenon occurring in the operation process of the four-wheel independent steer-by-wire system, the shimmy phenomenon of different frequencies and amplitudes can be restrained in a self-adaptive mode, then the reliable operation capacity of the four-wheel independent steer-by-wire system is improved, and vehicle instability caused by excessive shimmy of wheels is avoided.

The invention relates to an optimal safe formation collision avoidance control method for multiple unmanned surface vessels under DoS attack. The method comprises the following steps: constructing an input saturation model; constructing a multi-unmanned surface vessel dynamic model with unknown nonlinear terms; in the obstacle avoidance control design, an integral multiplication tangent obstacle Lyapunov function is designed by using the tangent obstacle Lyapunov function; establishing an attack compensator; carrying out approximation on the unknown nonlinear term by utilizing a fuzzy logic system to obtain an approximation result; combining the approximation result with an attack compensator, constructing a fuzzy state observer with switching gain, and obtaining an observation error; an optimal performance index function is designed according to the consistency formation error, and then a Bellman residual error is constructed; and constructing a weight updating law, and designing an optimal safe formation collision avoidance controller based on a judgment-execution structure according to the weight updating law and an integral multiplication tangent obstacle Lyapunov function. According to the technical scheme, the problem that collaborative optimization of obstacle avoidance and formation control is difficult is solved.

The invention discloses an exoskeleton robot fixed time control method based on output constraint and disturbance observation, relates to the field of robot control, constructs a fixed time neural controller (FTNC), and ensures that a system is stable in fixed time by combining a universal obstacle Lyapunov function (UBLF) and a preset performance function (PPF), so as to improve the system stability. The convergence time is irrelevant to the initial state; meanwhile, a radial basis function neural network (RBFNN) and a nonlinear disturbance observer (NDO) are adopted to jointly compensate the uncertainty of the model and the man-machine interaction disturbance; by dynamically adjusting UBLF boundary conditions, joint angle errors are limited, and training safety is guaranteed. According to the method, a fixed time control theory is adopted, fixed time is combined with a preset performance function (PPF) and a universal barrier Lyapunov function (UBLF), and the output of the exoskeleton robot is strictly limited, so that the rapid convergence and transient performance requirements of an exoskeleton system are met at the same time.

The invention relates to a clamping force constraint backstepping sliding mode control method for an electronic mechanical braking system, and belongs to the related technical field of new energy automobile control. The method comprises the following steps of: establishing a state space model of the system by considering the nonlinear characteristic of the electronic mechanical braking system, and reasonably simplifying the state space model into a strict feedback form; a time-varying tangent barrier Lyapunov function (BLF) containing an error integral term is constructed, an exponentially decayed time-varying function is used as a constraint boundary of a clamping force tracking error, the integral term is introduced to eliminate a steady-state error caused by unmodeled dynamics, and the output constraint performance and tracking precision of a system are ensured; on the basis, a control law is designed by combining a backstepping method and non-singular fast terminal sliding mode control, and fast and accurate tracking of the braking clamping force is achieved; and proving the system stability based on the Lyapunov stability theorem. According to the invention, the braking response time and the clamping force control precision of the electronic mechanical braking system can be effectively improved.

The invention discloses a distributed adaptive fault-tolerant formation tracking control method. The method comprises the following steps: constructing a longitudinal dynamic model of the intelligent agent and decomposing the longitudinal dynamic model into a speed subsystem and an attitude subsystem; constructing an actuator fault model, and reconstructing lumped uncertainty items in combination with the uncertainty of a longitudinal dynamic model and external disturbance; self-adaptive laws of a speed subsystem and an attitude subsystem are determined respectively, so that active compensation is carried out on lumped uncertainty items through an RBF neural network; and respectively determining control protocols of the speed subsystem and the attitude subsystem through a recursive backstepping method in combination with a general obstacle Lyapunov function. According to the method, under the harsh conditions that uncertainty and actuator faults exist, the key state safety boundary is continuously met, meanwhile, the high-quality time-varying formation tracking performance is kept, and the implementation complexity of a control system is effectively reduced.

The invention provides a flexible intelligent material driver output constraint control method considering butterfly-shaped hysteresis input for an output constraint flexible intelligent material driving system with butterfly-shaped hysteresis input, aims to realize high-precision control in different application scenes, and comprises the following steps of: on the basis of a traditional KP model, selecting a KP model; according to the invention, a new butterfly-shaped KP (Butterfly Kranoselskii-Pokrovskii, BKP) kernel is obtained through derivation, and the KKP (Butterfly Kranoselskii-Pokrovskii, BKP) kernel is used as a kernel of the KKP. A new BKP model is established to describe butterfly hysteresis in the flexible intelligent material driver by performing weighted stacking on the BKP core; an unknown time delay function in the system is approached by using a radial basis function neural network and a finite coverage lemma, and the time delay problem of the control system is solved; a high-gain K-filter is designed to overcome the problem that the state in the system cannot be measured, and the problem of output constraint control of the butterfly hysteresis system is solved by combining a barrier Lyapunov function and an adaptive dynamic surface output feedback control algorithm; a butterfly pseudo-inverse algorithm is designed, the requirement for solving a hysteresis inverse model is avoided, and butterfly hysteresis nonlinearity is weakened to a great extent. Compared with a traditional control scheme, the method has a better effect on tracking performance and tracking errors, the tracking errors are effectively reduced, the control precision is remarkably improved, and precise control over the intelligent material driving system is achieved.

The invention relates to a mobile robot preset performance safety tracking control method with state constraint and input time delay, and belongs to the technical field of mobile robot intelligent control. Aiming at the problems of control singularity, state overrun risk, time delay sensitivity and uncontrollable tracking performance in the prior art, a dynamic modeling method based on a car-like coordinate system is provided, and a kinematics-dynamics hierarchical controller is constructed by combining a constraint safety reference signal generation mechanism and a Hermite interpolation smoothing technology. Error dynamic constraint is realized by designing a preset performance function and a barrier Lyapunov function, an auxiliary signal is adopted to compensate an input time delay effect, and reinforcement learning is fused to optimize an anti-interference strategy. The method effectively eliminates the control singularity, strictly guarantees the state safety boundary, improves the time delay robustness, achieves the high-precision tracking error convergence, and is suitable for the safe and stable control of the mobile robot in a complex environment.

The invention provides a sewage treatment process adaptive neural network control method with time-varying asymmetric constraints, is used for a sewage treatment process with unknown parameters, and belongs to the technical field of urban sewage treatment process intelligent control. A controller is designed by adopting a self-adaptive control method, and the concentration of dissolved oxygen and the concentration of nitrate nitrogen in the sewage treatment process are accurately controlled. Firstly, due to the fact that the fuzzy neural network has excellent robustness, unknown dynamics appearing in a sewage treatment plant can be effectively approached. Secondly, control input is designed by constructing a time-varying asymmetric obstacle type Lyapunov function, and a fault-tolerant control method is designed in consideration of an actuator fault condition. And finally, performing a simulation experiment by using the reference simulation model 1, and verifying the effectiveness of the proposed method by a simulation result.

The invention discloses a fixed time ship trajectory tracking control method considering state limitation. The method comprises the following steps: S1, establishing an under-actuated ship mathematical model considering input saturation; s2, limiting the actual position of the ship based on the set first obstacle Lyapunov function; limiting the expected position of the ship based on a set second obstacle Lyapunov function; s3, defining a ship position tracking error, and designing a longitudinal expected speed and a transverse expected speed which can enable the ship position tracking error to converge; s4, defining a ship speed tracking error, and designing a first fixed time sliding mode surface and a second fixed time sliding mode surface according to the ship speed tracking error; and S5, designing a longitudinal control force and a yawing control moment which can enable the first fixed time sliding mode surface and the second fixed time sliding mode surface to converge so as to enable the ship speed tracking error to realize fixed time convergence and solve the problem that the convergence rate of the speed tracking error is low.

The invention provides a finite time position tracking control method of a magnetic suspension system with output constraint, which comprises the following steps of: establishing a mathematical model of the magnetic suspension system, determining an error system, and obtaining upper and lower bounds of suspension air gap constraint and system state information; designing a fractional obstacle Lyapunov function by considering upper and lower bounds of suspension air gap constraint, and solving partial derivative to obtain a constraint processing mechanism function; designing a continuous finite time state feedback constraint controller by using system state information based on a constraint processing mechanism function by applying a finite time control technology and a homogeneous system theory; and based on the constraint processing mechanism function, when the system speed state information cannot be measured, designing a non-smooth filter, and designing a finite time output feedback controller by using the filter output information and the system output information. Unified finite time control of the magnetic suspension system under symmetric / asymmetric suspension gap constraint is realized, and the method has high tracking precision, strong anti-interference robustness and engineering practicability.

The invention provides a preset time adaptive fuzzy cooperative formation control method for a multi-water-surface-robot system, and the method comprises the steps: building a motion model containing uncertainty and actuator faults based on a speed state vector, and approaching the uncertainty through a fuzzy logic system; constructing a fuzzy logic expansion state observer containing a time-varying gain function to estimate an unmeasurable speed and compensate interference and uncertainty; based on observer output, distributed errors are defined, a barrier Lyapunov function constraint boundary is introduced, and uncertainty and faults are compensated in combination with fuzzy and multiplicative fault adaptive laws. According to the method, the multiple water surface robots can form and keep the expected formation within the preset time, the tracking error is converged to the small neighborhood of the original point, the method does not depend on initial conditions, and the control precision and robustness are improved.

The present invention discloses a finite-time control method for consistent tracking of a multi-link robotic arm. The method comprises the following steps: first, modeling actuator failures, and then establishing an uncertain multi-link robotic arm system model with actuator failures and full-state constraints. Next, a finite-time stable event-triggered control method is designed based on barrier Lyapunov functions, backstepping design, and neural network adaptive control technology. Finally, the proposed control method is simulated. The simulation results show that when an actuator failure occurs, each robotic arm can effectively track the leader's trajectory, with minimal tracking error and stability within the specified time, while also effectively saving communication resources.

The invention discloses an underwater manipulator trajectory tracking finite time sliding mode controller design method, a sliding mode controller and a control system, and belongs to the technical field of underwater robot control. Comprising the steps that a nonsingular terminal sliding mode surface control framework is constructed, the framework fuses factors such as system model uncertainty, external disturbance, output constraint and input saturation, and a foundation is laid for accurate control; in order to ensure that a system state is always in a time-varying constraint boundary in a complex marine environment, a time-varying barrier Lyapunov function is combined with sliding mode control, tracking error constraint is converted into sliding mode surface constraint through mathematical conversion, and effective constraint on the system state is realized through strict derivation and analysis; besides, a finite time auxiliary control system with a dynamic compensation strategy is designed for the problem of input saturation caused by physical limitation of the actuator, trajectory errors can be rapidly restrained, and the accuracy and stability of trajectory tracking of the underwater manipulator are remarkably improved.

The application discloses a kind of anti-network attack's multi-vehicle safety platoon control method and system, the method first establishes multi-vehicle queue dynamics model;Second, design fuzzy state observer to estimate vehicle state and unknown nonlinear dynamics;Then, introduce funnel function to build time-varying performance boundary, and the double constraints of vehicle anti-collision and keep communication are converted into funnel constraints of spacing tracking error;Then, based on backstepping method and funnel performance error, design virtual controller and adaptive law, and obtain adaptive safety controller to obtain control input;Finally, through Lyapunov stability analysis, the tolerance threshold condition that system can still maintain final consistent bounded stable after suffering replay attack is quantitatively obtained.The application simplifies design through funnel control, avoids complex barrier lyapunov function, and for the first time clearly provides quantified anti-interference ability to replay attack, enhances the network security and practicality of multi-vehicle queue system.

The invention discloses a ship automatic berthing state constraint control method based on speed setting, and the method comprises the following steps: designing a derivative of a speed setting error function, a berthing speed setting function and an expected berthing resultant speed to form a closed-loop stabilization system, and carrying out the automatic berthing state constraint control of a ship through the closed-loop stabilization system. The expected berthing speed is dynamically adjusted according to the real-time distance between the ship position and the berth, and path parameters are updated in a self-adaptive mode; an integral type obstacle Lyapunov function about the position state is constructed, and a virtual controller is designed according to a stability judgment method of a nonlinear system under the state constraint and used for controlling the ship to meet the position constraint condition; and constructing an integral obstacle Lyapunov function about the speed state, and designing by using a state constraint control algorithm to obtain a control law for controlling the ship to meet the speed constraint condition, so that the ship berths along the parameterized path under the condition that the ship meets the position constraint condition and the speed constraint condition. The problem of safe and automatic berthing of the ship is solved.

The invention relates to a mechanical arm controller design method considering full-state constraint and disturbance suppression. The mechanical arm controller design method comprises the steps that firstly, a mechanical arm system state space model is established, and error variables are defined; 2, defining a tangent full-state constrained obstacle Lyapunov function, and carrying out virtual control design; 3, designing a known controller of the model, and introducing a Moore-Pengos inverse matrix and a lemma 1 to prove stability; and 4, designing a neural network controller, inhibiting bounded disturbance, and then verifying the stability of the neural network controller. According to the neural network controller designed by the method, the position error can be limited in the time-varying constraint boundary, and the speed error can be limited in the constant static constraint boundary, so that the purpose of limiting the tracking error in the predefined range can be realized.

The invention discloses a piezoelectric driving system constraint control method based on a predetermined time observer, and the method comprises the steps: designing a predetermined time interference observer in combination with a predetermined time performance function, and guaranteeing that an interference estimation error is converged to a bounded range within a predetermined time; a state conversion function is designed based on the obstacle Lyapunov function, so that the system output can also realize preset time output constraint under the condition that the initial state does not meet the constraint condition; a predetermined time adaptive controller is designed by using a neural network approximation technology, a dynamic surface control technology, a constraint control technology and a predetermined time interference observer technology, so that a closed-loop system realizes predetermined time stability. The problem that an existing disturbance observer cannot be used for preset time control is solved, and the assumption that initial conditions of constrained variables need to meet constraint conditions in constraint control is avoided.

This invention discloses a fixed-time control method for a rotary-wing unmanned aerial vehicle (UAV) in unknown environments. The steps are as follows: 1. Constructing a nonlinear system model of the UAV's position loop and attitude loop, simultaneously considering system uncertainty, output constraints, and unknown external disturbances; 2. Designing a safe desired tracking signal, obtaining the remaining desired signals through inverse kinematics, calculating the errors of the position loop and attitude loop, and designing a barrier Lyapunov function based on the characteristics of the nonlinear system model of the UAV's position and attitude loops, and selecting appropriate upper and lower bounds; 3. Designing a tracking controller based on the barrier Lyapunov function, combined with a fuzzy logic system, a fixed-time disturbance observer, a filter, and an adaptive backstepping method. This invention has the advantage of enabling a rotary-wing UAV to track a preset desired tracking signal within a fixed time while ensuring system safety and stability, under the influence of output constraints, system uncertainty, and unknown external disturbances.

The application discloses an adaptive tracking control method for a random nonlinear system based on output feedback, comprising the following steps of S1, constructing a mathematical model of a random nonlinear system with hysteresis input, unknown control direction, arbitrary switching and output constraint; S2, designing a linear state observer, and reconstructing unmeasurable states of the random nonlinear system by using output and control input of the random nonlinear system; S3, introducing a Nussbaum type function to process the unknown control direction, and constructing a barrier Lyapunov function to ensure that the output constraint is not violated; and S4, designing a framework based on a backstepping method, combining a radial basis neural network to approximate unknown nonlinear functions, designing a virtual control law, an adaptive law and an actual control law, and realizing adaptive tracking control of the output of the random nonlinear system on a reference signal.

The invention relates to a PCB circuit of a three-degree-of-freedom Duffing type MEMS resonator and an event-triggered fuzzy neural network inversion control method, and belongs to the technical field of MEMS resonator control, and the method comprises the steps: constructing an electrostatic coupling three-degree-of-freedom Duffing type MEMS resonator based on a series mode, and building a mathematical model; the method comprises the following steps: designing an analog circuit of a three-degree-of-freedom Duffing type MEMS resonator, and constructing a PCB experiment platform; designing an inversion controller based on a logarithmic obstacle Lyapunov function, an acceleration function, an acceleration exponential integral tracking differentiator and a type-2 fuzzy wavelet neural network; and a switching threshold event triggering mechanism is introduced into the inversion controller.

The invention discloses a multi-unmanned ship collision avoidance formation control method based on a BLF under generalized noise. The method comprises the following steps: S1, constructing a communication topological graph of an unmanned ship cluster based on a graph theory, constructing a Laplacian matrix, and defining a communication connection mode of the unmanned ship cluster; s2, establishing a linear dynamic model of the unmanned ship in combination with generalized noise definition, and defining a BLF according to the linear dynamic model; based on the local formation tracking error of the unmanned surface vehicle and in combination with the BLF, defining a collision-free formation tracking control protocol; and S3, an integral multiplicative obstacle Lyapunov function is combined with a stochastic differential equation stability theory to verify whether the collision-free formation tracking control protocol can enable the formation tracking error to be stable according to probability noise-state. According to the method, the collision avoidance algorithm is designed based on the BLF, the generalized noise is analyzed by using the stochastic differential equation, and the anti-interference capability of the multi-unmanned-ship formation is realized while the two core technologies guarantee that the multi-unmanned-ship safe formation is free of collision.

The embodiment of this specification provides a single-link multi-manipulator adaptive event-triggered control method with given performance, which is characterized by comprising: modeling a multi-single-link manipulator system to obtain a single-link manipulator state equation with input dead zone and saturation constraints; designing the i-th single-link manipulator consistency tracking error, and designing a first virtual control law α i,1 and the adaptive law to design an adaptive event triggering mechanism; design a second virtual law α i,2 , adaptive laws, and simulation experiments were conducted using a Matlab experimental platform. To achieve the goal of convergence of the system to a given performance within a given time, the present invention combines the speed function and the performance function to obtain a speed-performance function. Through clever mixing and transformation, a transformation function and a barrier Lyapunov function are constructed, enabling the system to converge to a given performance within a given time while avoiding violations of state constraints.

The present invention belongs to the field of computer science and control technology, and discloses a virtual coupling train speed convergence cooperative control method based on a multi-agent system. The specific steps of the control method are as follows: S1: System initialization and parameter setting; S2: Distributed observer design; S3: Adaptive control algorithm construction; S4: Cooperative control algorithm implementation; S5: Global stability analysis; S6: Train formation dynamic formation; S7: Formation operation and dissolution. In the train formation system of the present invention, the distributed observer cleverly solves the communication problem, collects the states of adjacent trains in real time, dynamically compensates the data, ensures accurate mastery of the formation state. The adaptive control algorithm is based on the barrier Lyapunov function, solidifies the spacing requirements into hard constraints, combines the nonlinear control theory to cope with parameter uncertainties, and stabilizes the formation operation. The cooperative control algorithm is like a lubricant, promoting seamless cooperation between trains, synchronous convergence of speeds, and safe and stable spacing.

The invention discloses a flexible joint mechanical arm preset time fuzzy output feedback control method under output constraint. The method comprises the following steps that a state space model of a flexible joint mechanical arm is established; based on a preset time stability theory, designing a Lyapunov function framework which ensures that the system converges to a bounded set within preset time; a fuzzy logic system is adopted to carry out approximation on an unknown nonlinear dynamic function of the mechanical arm; a barrier Lyapunov function is introduced to process system output constraints, and it is ensured that output does not violate physical constraints; constructing a state observer based on a fuzzy logic system to estimate an unmeasurable state; designing a virtual control function, a controller and a parameter adaptive law; and a preset time filter is designed to reduce the calculation complexity, and finally stable control of the closed-loop system within the preset time is realized. According to the invention, the preset time stabilization control of the closed-loop system is successfully realized.

The application relates to the field of intelligent operation unmanned planes, and discloses a plant protection unmanned plane cooperative operation spraying area control method, which constructs a multi-unmanned plane cooperative hanging type spraying system; a Lagrange method is used to establish an underactuated dynamics model of a single plane hanging load system, and mutual constraints among the multi-unmanned planes are considered under the multi-unmanned plane framework; a barrier Lyapunov function (BLF) is designed, and full-range constraints are applied to single plane load displacement and relative distances among the multi-unmanned planes; RBF neural network is used to compensate system nonlinearity and external disturbance on line; an adaptive thrust control law of multi-plane coordination is designed, high-precision trajectory tracking of each unmanned plane, strict constraints of load displacement, and collision-free guarantee among the medicine boxes are realized; the application can effectively eliminate the collision risks among the unmanned planes and among the medicine boxes by pre-planning reasonable trajectories, and can avoid medicine waste caused by repeated spraying.

This invention relates to the field of automatic control technology, and in particular to a method and system for asymmetric preset performance control of a cascade continuous stirred reactor without initial conditions. The method includes establishing a mechanistic model of the cascade continuous stirred reactor, transforming it into an error equation for the cascade continuous stirred reactor, pre-specifying that the change in reactant concentration error at the output of the cascade continuous stirred reactor satisfies a banded constraint space, converting the banded constraint space into a symmetric constraint space, constructing a radical-type algebraic saturation function to map the symmetric constraint space into a bounded symmetric constraint space, superimposing an exponential finite-time decay function on the bounded symmetric constraint space to construct a bounded symmetric constraint space without initial conditions, constructing a barrier Lyapunov function based on the bounded symmetric constraint space without initial conditions, and designing an asymmetric preset performance controller without initial conditions using the backstepping method. This invention enables the stable, safe, and rapid operation of the cascade continuous stirred reactor.

The invention discloses a discrete neural network control method and system for an upper limb rehabilitation robot, and the method comprises the steps: taking each joint module of the upper limb rehabilitation robot as a subsystem, and constructing a self-adaptive neural network controller based on a discrete obstacle Lyapunov function; determining a weight update rate, determining a learning theory according to discretization, and constructing an experience-based learning controller; on the basis of an experience-based learning controller, realizing knowledge fusion by using a spatial constraint-based least square method, and constructing a knowledge fusion experience-based learning controller; and constructing a variable admittance control model, taking the output of the variable admittance control model as a reference trajectory of a learning controller based on knowledge and experience, and executing corresponding rehabilitation action training according to the rehabilitation demand of the user. According to the invention, intelligent and safe high-performance control of the upper limb rehabilitation robot in different individuals and different recovery periods is realized.

The application discloses a kind of upper limb rehabilitation robot discrete neural network control method and system, the method includes: each joint module of upper limb rehabilitation robot is regarded as a subsystem, adaptive neural network controller is constructed based on discrete barrier lyapunov function;Determine weight update rate, according to discrete determination learning theory, experience-based learning controller is constructed;On the basis of experience-based learning controller, the fusion of knowledge is realized using least square method based on space constraint, and experience-based learning controller based on knowledge fusion is constructed;Variable admittance control model is constructed, and the output of variable admittance control model is used as the reference trajectory of experience-based learning controller based on knowledge fusion, and the training of corresponding rehabilitation action is executed according to the rehabilitation demand of user.The application realizes the intelligentization and safety high-performance control of upper limb rehabilitation robot in different individuals and different recovery periods.

The invention discloses a fixed time preset performance trajectory tracking control method considering actuator characteristics. The method comprises the following steps: constructing an under-actuated ship mathematical model considering input saturation and a dead zone; setting an expected trajectory of the ship, obtaining a trajectory tracking error based on the actual trajectory and the expected trajectory of the ship, defining a barrier Lyapunov function limiting error, and further designing an expected longitudinal velocity and an expected transverse velocity; calculating a speed tracking error, and designing a fixed time sliding mode surface according to the speed tracking error; estimating a nonlinear term by using an RBF neural network; and combining a fixed time convergence theory, designing a ship longitudinal control force and a yawing control moment based on the estimated nonlinear term and the fixed time sliding mode surface, and controlling the ship by using a ship longitudinal and ship yawing fixed time controller to realize trajectory tracking control of the ship. The method can improve the control precision, solves the problems of low convergence speed and instability, improves the control efficiency, and guarantees the tracking precision.