Heading Backstepping Control Method, Controller and Autopilot Based on Navigation Data Correction

Abstract

A heading backstepping control method, a controller and an autopilot based on voyage data correction are disclosed. Voyage data including rudder angle and heading angle are obtained through a backstepping controller, and then the voyage data are used as the input of an identification system to correct and obtain new ship motion mathematical model manipulation parameters based on the previous round of identification parameter values. Then, the corrected manipulation parameters are substituted into the backstepping controller to form a closed-loop system that continuously circulates until the heading of the unmanned boat reaches the target heading. During the process of adjusting the heading of the unmanned boat by the closed-loop system, a disturbance observer is used to estimate and compensate for external disturbances and uncertainties of the ship motion mathematical model. The present invention innovatively adds a voyage data correction algorithm and a disturbance observer algorithm on the basis of backstepping control, realizes the adaptive optimization adjustment of manipulation parameters of the unmanned boat in the face of complex marine environments, and greatly improves the accuracy and stability of the heading control of the unmanned boat.

Description

Technical Field

[0001] The present invention belongs to the fields of marine vehicle automation engineering and unmanned surface vehicle autopilot, and particularly relates to an anti-backstepping control method for the heading of an unmanned surface vehicle based on navigation data correction, an unmanned surface vehicle heading controller, and an unmanned surface vehicle autopilot. Background Art

[0002] An unmanned surface vessel (USV), hereinafter referred to as an unmanned boat, is an intelligent unmanned marine vehicle. With its characteristics of light structure and flexible movement, it has broad prospects in commercial, military and other aspects, and has become a research hotspot in various countries around the world in recent years.

[0003] For an unmanned boat in high-speed navigation, to reach the target navigation point and avoid obstacles, etc., it depends on good heading automatic control ability. At the same time, the influence of complex marine environment disturbances such as wind, waves and currents in the sea area during navigation is more serious than that in medium- and low-speed navigation. Therefore, to ensure the stability and accuracy of the unmanned boat heading automatic control, this puts forward higher requirements for the reliability and self-adaptability of the heading automatic control system when dealing with unknown disturbances.

[0004] The PD control method is widely used in the field of ship automatic control. The traditional PD heading control system has good control effects for linear control systems, but has poor control effects for some systems with high nonlinearity and large hysteresis. The traditional PD heading control method not only is not sensitive enough to unknown disturbances, but also cannot accurately observe external disturbances in the case of controlling nonlinear systems and external disturbances, and cannot respond to unknown disturbances in a timely and accurate manner under complex sea conditions. There is often no mathematical law to follow between the control parameters of the traditional PD heading control method. During the navigation of a ship, the external environment and its own state are constantly changing, and this method cannot dynamically adjust the ship control parameters according to the current sea conditions and its own navigation state, with weak adaptability and unable to achieve the desired heading control effect. Summary of the Invention

[0005] Aiming at the three problems that the traditional PD heading control system often has poor response to unknown disturbances, cannot optimize control parameters, and has poor adaptability to unknown sea areas under the conditions of unknown sea area, unknown sea conditions, and unknown ship control parameters, the present invention proposes an anti-backstepping control method for heading based on navigation data correction, a heading controller, and an unmanned surface vehicle autopilot.

[0006] According to the first aspect of the embodiment, a heading backstepping control method based on navigation data correction is provided. Navigation data including rudder angle and heading angle is obtained through a backstepping controller, and then the navigation data is used as the input of an identification system. Based on the previous round of identified parameter values, the manipulated parameters of the new ship motion mathematical model are corrected. Then, the corrected manipulated parameters are substituted into the backstepping controller to form a closed-loop system that continuously cycles until the heading of the unmanned boat reaches the target heading. During the process of adjusting the heading of the unmanned boat by the closed-loop system, a disturbance observer is used to estimate and compensate for external disturbances and uncertainties in the ship motion mathematical model. The present invention dynamically adjusts and optimizes the ship's manipulated parameters through real-time navigation data information and then inputs them into the ship control module, enabling the system to have an adaptive ability when the external environment and its own state are constantly changing. On this basis, backstepping control is used and a disturbance observer is designed, so that when the unmanned boat sails in waters with a high degree of nonlinearity and external environmental disturbances, its control performance also has good stability and robustness. The present invention improves the accuracy and stability of the heading control of the unmanned boat, and at the same time realizes the adaptive adjustment of the unmanned boat to external disturbances, providing an effective solution for the automatic control of the heading of the unmanned boat.

[0007] According to the second aspect of the embodiment, a heading controller is provided, which uses the above-mentioned heading backstepping control method based on navigation data correction to control the unmanned boat.

[0008] According to the third aspect of the embodiment, an autopilot is provided, which has the above-mentioned heading controller.

[0009] Advantages of the present invention:

[0010] (1) The greatest advantage of the present invention is that a new online identification module based on real-time feedback of navigation information is adopted in the control system, realizing that the manipulated parameters of the unmanned boat change with the external environment and its own state during the navigation process. The present invention comprehensively considers the relationship between the ship control parameters and the current sea conditions and its own navigation state, can dynamically adjust the ship's manipulated control parameters, has stronger adaptive ability to unknown sea conditions, has better stability and robustness, and has a better heading control effect on the unmanned boat.

[0011] (3) Through the disturbance observer, the control system of the present invention can actively evaluate and compensate for disturbances and model uncertainties, greatly improving the sensitivity of the system to external disturbances compared with the traditional PD heading control method that reacts passively to disturbances, and greatly improving the tolerance of the ship heading control system during the navigation of the unmanned boat to uncertain disturbances.

[0012] (3) The present invention not only has a good effect on controlling linear systems, but also has a good control effect on some systems with high nonlinearity and large hysteresis, and can improve the control effect of the unmanned boat during navigation in unknown sea areas, unknown sea conditions, and unknown ship control parameters. Brief Description of the Drawings

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments will be briefly introduced below.

[0014] Figure 1 It is the control schematic diagram of the heading backstepping control system based on navigation data correction provided by an embodiment of the present invention.

[0015] Figure 2 It is the working schematic diagram of the parameter correction system based on navigation data provided by an embodiment of the present invention.

[0016] Figure 3 It is the heading control effect diagram of the heading backstepping control system based on navigation data correction provided by an embodiment of the present invention.

[0017] Figure 4 It is the comparison diagram of the bow angle control effects of the unmanned boat under two control systems provided by an embodiment of the present invention. Detailed Embodiment

[0018] The present invention adopts the sequence of first performing rudder angle control and then parameter correction. Figure 1 The control schematic diagram is shown. That is, the heading control module obtains navigation data such as rudder angle and heading angle through the backstepping controller, and then uses these navigation data as the input of the identification module to correct and obtain new manipulation parameters based on the previous round of identified parameter values. Then, the corrected manipulation parameters are substituted into the designed backstepping controller to form a closed-loop system that continuously cycles until the heading of the unmanned boat reaches the target heading, realizing the precise control of the heading of the unmanned boat.

[0019] Step (1): Preliminary control of the heading of the unmanned boat by the backstepping controller

[0020] The motion response model of the heading control system adopts the first-order nonlinear maneuvering model of the unmanned boat. This model is based on the traditional first-order linear response model of ships and introduces the nonlinear term αr 3 for correction, realizing the maneuverability analysis of the unmanned boat during high-speed navigation. It is a currently widely used unmanned boat motion model, and the model expression is as follows:

[0021]

[0022] In the formula, δ is the rudder angle; δ r is the anti-rudder angle; δ dis the interference value received; r is the heading angular velocity; α is the correction coefficient of the non - linear term; T is the time parameter; K is the rudder angle gain coefficient.

[0023] It can be seen that the first - order non - linear response model gives the non - linear relationship between the rudder angle of the unmanned boat and the heading angle, heading angular velocity and heading angular acceleration. According to the first - order non - linear maneuvering response model of the unmanned boat, the heading motion state equation of the unmanned boat can be obtained:

[0024]

[0025] Integrating the derivative of the heading angle (heading angular velocity) and the derivative of the heading angular velocity by using the fourth - order Runge - Kutta integration method can obtain the heading angle and the heading angular velocity r.

[0026] The backstepping method obtains the heading feedback controller by recursively constructing the Lyapunov function of the closed - loop system, calculates the rudder angle control law so that the derivative of the Lyapunov function along the trajectory of the closed - loop system ensures that the heading of the unmanned boat reaches the target heading, and guarantees the boundedness of the trajectory of the heading control system and its convergence to the equilibrium point.

[0027]

[0028] In the formula, is the desired heading angle; e is the input of the backstepping controller, that is k1, k2, k3 are control parameters. k1, k2, k3 are parameters assumed in the process of designing the Lyapunov function in the backstepping design to make the defined tracking error tend to zero. The specific numerical values are obtained by gradually debugging the response relationship between the rudder angle and the heading angle of the ship.

[0029] Steer the unmanned boat through the rudder angle control law output by the backstepping controller, update the heading deviation, and then feedback it to the input of the backstepping controller until the input heading error e = 0, then the control ends, realizing the preliminary control of the heading of the unmanned boat.

[0030] Step (2): Based on the real - time online identification system corrected by navigation data, realize the adaptive adjustment of the maneuvering parameters of the heading control system with the change of the external environment.

[0031] The principle of the online identification system is as Figure 2 shown. It can correct the maneuvering parameters (the maneuvering parameters in the first - order non - linear response model) during the navigation of the unmanned boat in real time. After obtaining a new system input - output each time, on the basis of the previous parameter identification value, use the new system input - output to correct the previous identified parameter value, and then recursively obtain the new parameter identification value. By introducing this online identification algorithm corrected by navigation data, dynamically adjust and optimize the maneuvering parameters, providing a practical, fast and efficient parameter optimization method.

[0032] Write the first - order nonlinear response model of the unmanned boat in matrix form:

[0033] Y = Hθ

[0034] Where Y is the output matrix of the system; H is the input matrix of the system, and θ is the parameter matrix of the system.

[0035]

[0036] By introducing the gain matrix and covariance matrix, recursively update the parameter identification result. The specific process is as follows:

[0037]

[0038] Where K(t) is the gain matrix; P(t) and P(t - 1) are the covariance matrices at time t and t - 1 respectively, P(t)=[P -1 (t - 1)+H(t) T H(t)] -1 ; H(t) represents the input matrix of the system at the current moment; I is the identity matrix; and are the estimated values at time t and t - 1 respectively of.

[0039] Through this model, the relevant maneuvering coefficients of the unmanned boat can be identified in real - time according to the navigation data of the unmanned boat, and dynamic adjustment and optimization can be carried out.

[0040] Step (3): Use the disturbance observer to estimate and compensate for the external disturbance and the model parameter uncertainty of the first - order nonlinear maneuvering model.

[0041] Due to the complexity of the unmanned boat's motion environment, it is often affected by external disturbances. The nonlinear disturbance observer can accurately estimate and compensate for the external disturbance and model parameter uncertainty. Therefore, in this invention, a nonlinear disturbance observer is designed to improve the system's tolerance to uncertain disturbances and improve the control ability of the unmanned boat's heading control system.

[0042] Design the following disturbance observer according to the previous first - order nonlinear response model of the unmanned boat:

[0043]

[0044] Where z is the constructed quantity; L is the disturbance observer bandwidth; is the disturbance observation value.

[0045] In one embodiment, a heading controller is further provided, which uses the heading backstepping control method corrected based on navigation data to control the unmanned boat. The heading controller includes two parts: a heading control module and a parameter correction module. The heading control module includes a backstepping controller (heading feedback controller). For the specific implementation manners of the heading control module and the parameter correction module, refer to the embodiments in the above method part, which will not be elaborated here.

[0046] In one embodiment, a steering gear is further provided, which has the above-mentioned heading controller. The steering gear of the present invention innovatively adds a navigation data correction algorithm and a disturbance observer algorithm on the basis of a backstepping control system, realizing that the unmanned boat can complete the adaptive optimization adjustment of the maneuvering parameters when facing a complex marine environment, and greatly improving the accuracy and stability of the heading control of the unmanned boat.

[0047] The key technology of the present invention is to innovatively add a navigation data correction algorithm and a disturbance observer algorithm in the backstepping heading control system, and to correct the ship's maneuvering parameters in real time through the ship's current navigation data, state information and current sea conditions, effectively solving the problems of uncertain parameters and no regular pattern of parameter changes in the traditional PD control method. At the same time, a disturbance observer is set to accurately estimate and compensate for external disturbances and model uncertainties, effectively improving the tolerance of the control system to uncertain disturbances, improving the control effect of the heading control of the unmanned boat, and enabling the unmanned boat to have a very good automatic control effect when sailing in waters with complex working conditions and high non-linearity and unknown sea conditions. Figure 3 The heading control effect diagram of the heading backstepping control system corrected based on navigation data is shown. Figure 4 The comparison diagram of the bow angle control effects of the unmanned boat under two control systems is shown.

[0048] The main technology of the present invention is reflected in the specific implementation process of the heading backstepping control algorithm corrected based on navigation data: firstly, using the backstepping controller to initially control the ship's heading through the change of the rudder angle to ensure the boundedness of the heading control system trajectory; secondly, it is the navigation data correction, taking the navigation data such as the rudder angle and bow angle output by the control module in the new round as the input to correct on the basis of the maneuvering parameters identified in the previous round, realizing the dynamic optimization adjustment of the maneuvering parameters, and directly reflecting the suppression effect on external disturbances into the control system; finally, through the disturbance observer, the sensitivity of the system to external environmental disturbances is improved, and external disturbances can be accurately estimated and compensated, greatly improving the tolerance of the system to uncertain disturbances. The heading backstepping control algorithm corrected based on navigation data combines the above advantages, and the newly designed unmanned boat heading controller also has very good stability and reliability, and also has good adaptability and anti-interference ability to external disturbances.

Claims

1. A heading backstepping control method based on voyage data correction, characterized in that, Navigation data including rudder angle and heading angle are obtained through a backstepping controller, and then the navigation data is used as the input of the identification system. Based on the previous round of identified parameter values, the manipulation parameters of the new ship motion mathematical model are corrected. Then, the corrected manipulation parameters are substituted into the backstepping controller to form a closed-loop system that continuously cycles until the heading of the unmanned boat reaches the target heading. During the process of adjusting the heading of the unmanned boat by the closed-loop system, a disturbance observer is used to estimate and compensate for external disturbances and uncertainties in the ship motion mathematical model. The steps include: Step (1): Preliminary control of the heading of the unmanned boat by the backstepping controller: Model the heading of the unmanned surface vehicle using a first-order nonlinear maneuvering model. The input of the model is the rudder angle , and the outputs are the heading angle and the heading angular velocity ; The backstepping controller generates the rudder angle control law by recursively constructing the Lyapunov function to steer the unmanned boat, updates the heading deviation, and then feeds it back to the input of the backstepping controller until the heading error e converges to zero, achieving the preliminary control of the heading of the unmanned boat. The expression of the rudder angle control law is as follows: In the formula, is the control parameter, , is the desired heading angle, is the rudder angle, is the anti - helm angle, is the interference value received, is the non - linear term correction coefficient, is the time parameter, is the rudder angle gain coefficient; Step (2): Real-time online identification system based on navigation data correction: Write the first-order nonlinear response model of the unmanned boat in matrix form: In the formula, is the identification system output matrix; is the identification system input matrix, is the identification system parameter matrix; Updating the parameter matrix by recursive calculation of the gain matrix and the covariance matrix , and its recursive formula is: Wherein, is the gain matrix; and are respectively the covariance matrix at time and time ; represents the input matrix at the current time; is the identity matrix; and are respectively the estimated values at time and time ; Substitute the corrected parameter matrix into the backstepping controller to achieve dynamic optimization of the manipulation parameters until the heading error converges to zero.

2. A heading controller, characterized in that, Use the heading backstepping control method based on navigation data correction described in claim 1 to control the unmanned boat.

3. An autopilot, characterized in that, Have the heading controller described in claim 2.

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