Unmanned ship dynamics controller based on adaptive extended state observer

Abstract

The invention discloses an unmanned ship dynamics controller based on a self-adaptive extended state observer, which belongs to the technical field of unmanned ship dynamics control and comprises a controller module, a predictor module and an ESO module. The controller module is used for tracking the kinematics guidance signal and outputting a control signal of the unmanned ship; the predictor module is used for outputting an unmanned ship course angle estimation signal to the controller module according to the control signal output by the controller module and an actual speed signal of the unmanned ship; and the ESO module is used for outputting an unmanned ship disturbance estimation signal to the controller module according to the control signal, an actual speed signal of the unmanned ship and an unmanned ship course angle observation signal output by the predictor module. The model parameter information and the lumped disturbance can be estimated online at the same time, and the design of the model-free anti-interference controller of the unmanned ship is realized according to the estimation result.

Description

Technical Field

[0001] The present invention relates to the field of unmanned ship dynamics control, and in particular to an unmanned ship dynamics controller based on an adaptive extended state observer. Background Art

[0002] In recent years, with the progress and development of science and technology, ship automation technology has also entered a stage of rapid development. Unmanned ships have attracted widespread attention for their high degree of automation, strong safety, and low operating costs. In the face of complex marine environments and changeable operating environments, autonomous navigation control of unmanned ships has always been a research hotspot for many scholars at home and abroad. In order to achieve high-precision autonomous control of unmanned ships, many ship motion control methods have been proposed, such as proportional integral differential control, sliding mode control, fuzzy control, and active disturbance rejection control. Among them, the active disturbance rejection control method has been widely used because it can treat environmental disturbances caused by wind, waves and currents and internal uncertainties of the ship as lumped disturbances and simple parameter adjustment, and thus has been widely used. However, the defect of this method is that most of the existing active disturbance rejection control methods require prior model parameter information, especially the quality coefficient. In practice, the acquisition of model parameter information usually requires complicated experiments. In addition, in practical applications, the model parameters of unmanned ships will also change with the load of the unmanned ship, such as the pressure and discharge of ballast water, and the change of cargo capacity. Therefore, from the perspective of practical application, it is very necessary to estimate the model parameter information and lumped disturbance online without knowing the model parameter information, so as to realize stable unmanned ship self-disturbance rejection control.

[0003] In summary, the existing unmanned ship motion control methods have the following problems:

[0004] First, in the existing unmanned ship anti-disturbance control method, ESO can only estimate the uncertainty information in the unmanned ship dynamic model, but cannot estimate the model parameter information at the same time.

[0005] Second, in the existing unmanned ship anti-disturbance control method, the controller usually needs to design the control gain based on the model parameter information. When the control gain is unknown and changes, it is impossible to output an effective control signal.

[0006] Third, most of the existing unmanned ship model parameter information estimation methods require huge amounts of calculations and complex parameter adjustments, which are not suitable for practical applications. Summary of the invention

[0007] In view of the shortcomings of the prior art, the present invention provides an unmanned ship dynamic controller based on an adaptive extended state observer, which can not only simultaneously perform online estimation of model parameter information and lumped disturbances, but also implement the design of an unmanned ship model-free anti-disturbance controller according to the estimation results. The present invention adopts a modular design, which significantly reduces the complexity of the controller structure design, greatly reduces the hardware calculation load, and is easy to implement in engineering, thereby making it easier to complete the unmanned ship self-disturbance control under unknown and changing model parameters.

[0008] The technical solution of the present invention is as follows:

[0009] An unmanned ship dynamics controller based on an adaptive extended state observer comprises: a controller module, an estimator module and an ESO module; wherein:

[0010] The controller module is used to track the kinematic guidance signal and output the control signal of the unmanned ship, wherein the control signal includes the forward thrust and bow thrust signals of the unmanned ship;

[0011] The estimator module is used to output an unmanned ship heading angle estimation signal to the controller module according to the control signal output by the controller module and the actual speed signal of the unmanned ship; the estimator module includes a first speed estimation unit and a parameter estimation unit;

[0012] The ESO module is used to output an unmanned ship disturbance estimation signal to the controller module according to the control signal, the actual speed signal of the unmanned ship and the unmanned ship heading angle observation signal output by the estimator module; the ESO module includes a second speed estimation unit and a disturbance estimation unit.

[0013] Furthermore, the first input end of the controller module is used to receive the guidance signal, the second input end is used to receive the actual speed signal of the unmanned ship, the third input end is used to receive the unmanned ship heading angle estimation signal output by the parameter estimation unit, and the fourth input end is used to receive the unmanned ship disturbance estimation signal output by the disturbance estimation unit, and the output end of the controller module is connected to the control end of the unmanned ship;

[0014] The first input end of the first speed estimation unit is connected to the output end of the controller, the second input end is connected to the output end of the unmanned ship, and the third input end is connected to the output end of the parameter estimation unit;

[0015] The first input end of the parameter estimation unit is connected to the output end of the controller, the second input end is connected to the output end of the speed estimation unit, and the third input end is connected to the output end of the unmanned ship;

[0016] The first input end of the second speed estimation unit is connected to the output end of the controller, the second input end is connected to the output end of the unmanned ship, the third input end is connected to the output end of the parameter estimation unit, and the fourth input end is connected to the output end of the disturbance estimation unit;

[0017] The first input end of the disturbance estimation unit is connected to the output end of the unmanned ship, and the second input end is connected to the output end of the speed estimation unit.

[0018] Furthermore, the unmanned ship is an underactuated unmanned ship, and the dynamic model of the underactuated unmanned ship is:

[0019]

[0020] Y u =[Y u1 ,τ u ],Y r =[Y r1 ,τ r ]

[0021] Y u1 =[υr,u,1],Y r1 =[uυ,r,1]

[0022] θ u =[θ u1 ,θ u2 ],

[0023] θ r =[θ r1 ,θ r2 ],

[0024]

[0025] Among them, u is the forward speed of the unmanned ship, υ is the transverse drift speed of the unmanned ship, r is the bow angular velocity of the unmanned ship, and m 11 is the inertia parameter in the forward velocity direction, m 22 is the inertia parameter in the direction of drift velocity, m 33 is the inertial parameter in the direction of the yaw angular velocity, d 11 is the hydrodynamic damping parameter in the forward velocity direction, d 22 is the hydrodynamic damping parameter in the drift velocity direction, d 33 is the hydrodynamic damping parameter in the direction of the yaw angular velocity, is the model parameter vector θ in the forward velocity direction u The estimated value of is the model parameter vector θ of the yaw angular velocity direction r The estimated value of u is the lumped disturbance in the forward velocity direction, σr is the lumped disturbance in the direction of the yaw angular velocity, τ u is the forward thrust of the underactuated unmanned ship, τ r is the bow thrust of the underactuated unmanned ship, τ wu represents the external disturbance in the u direction, τ wr represents the external disturbance in the r direction.

[0026] Furthermore, the estimator module model is as follows:

[0027]

[0028] in, is the control gain of the predictor forward velocity estimation unit, is the control gain of the predictor yaw rate estimation unit, Γ u is the control gain of the predictor forward direction parameter speed estimation unit, Γ r is the control gain of the velocity estimation unit of the heading direction parameter of the predictor, and Γ u , and Γ r are all positive numbers; Proj(g) represents the projection operator.

[0029] Furthermore, the ESO module model is as follows:

[0030]

[0031] in, is the control gain of the ESO forward velocity estimation unit, is the control gain of the ESO yaw rate estimation unit, is the control gain of the disturbance estimation unit in the forward direction of the ESO, is the control gain of the ESO heading direction disturbance estimation unit, and and All are normal numbers; and Respectively represent σ u and σ r Observed value of .

[0032] Furthermore, the model of the controller module is as follows:

[0033]

[0034] Among them, μ u is the control gain of the forward direction dynamics controller, μ r is the control gain of the yaw direction dynamics controller, and μ u and μ r are all constants greater than 0, ur is the guidance signal in the forward direction, r r It is the guidance signal of the heading direction. is the derivative information of the guidance signal in the forward direction, It is the derivative information of the guidance signal in the heading direction.

[0035] Compared with the prior art, the present invention has the following advantages:

[0036] First, the existing ESO-based unmanned ship anti-disturbance control method generally requires some model parameter information to be known in advance, while the adaptive ESO proposed in the present invention does not require any prior model parameters.

[0037] Second, the existing ESO-based unmanned ship anti-disturbance control method is generally based on fixed control gain parameter information. The adaptive ESO proposed in the present invention can estimate the changing control gain parameters and ensure the control accuracy even if the model parameters change.

[0038] Third, compared with the existing ESO-based unmanned ship self-disturbance rejection control method, the adaptive ESO proposed in the present invention performs online parameter approximation based on the estimator, which greatly reduces the calculation load and is easier to implement in engineering.

[0039] In summary, the present invention can realize the anti-disturbance control of unmanned ship dynamics when the model parameter information is unknown and changing, which solves the problem that the existing unmanned ship self-disturbance anti-dynamic controller depends on a priori known model parameters. It also adopts a modular design, which significantly reduces the computational load and facilitates engineering implementation. The controller design concept of the present invention can also be extended to other unmanned robot fields that require high-precision motion control. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0041] Figure 1 The present invention is a schematic diagram of the structure of an unmanned ship dynamics controller based on an adaptive extended state observer.

[0042] Figure 2 It is a schematic diagram of the unmanned ship dynamic tracking error in the present invention.

[0043] Figure 3 This is a diagram showing the effect of the adaptive ESO in the present invention on the speed and angular velocity estimation of the unmanned ship.

[0044] Figure 4 It is a diagram showing the effect of the adaptive ESO in the present invention on estimating unknown and changing model parameters.

[0045] Figure 5 This is a diagram showing the effect of the adaptive ESO in the present invention on lumped disturbance estimation.

[0046] Figure 6 It is the forward and bow control input performance diagram of the unmanned ship in the present invention. DETAILED DESCRIPTION

[0047] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0048] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0049] like Figure 1 As shown, the present invention provides an unmanned ship dynamics controller based on an adaptive extended state observer (ESO), comprising: a controller module, an estimator module, an ESO module, a guidance signal and an unmanned ship. The four input ports of the controller module are respectively connected to the guidance signal, the estimator module, the output port of the ESO module and the output port of the unmanned ship. The two input ports of the estimator module are respectively connected to the output ports of the controller module and the unmanned ship. The three input ports of the ESO module are respectively connected to the output ports of the controller, the estimator module and the unmanned ship. The input port of the unmanned ship is connected to the output port of the controller module. The estimator module includes a speed estimation unit and a parameter estimation unit; the ESO module includes a speed estimation unit and a disturbance estimation unit.

[0050] Specifically, the controller module includes four input terminals, the first one is connected to the guidance signal for receiving guidance speed related information, the second one is connected to the output terminal of the unmanned ship for collecting the actual speed information of the unmanned ship, the third one is connected to the output terminal of the parameter estimation unit for collecting model parameter estimation information, and the fourth one is connected to the disturbance estimation unit for collecting aggregate disturbance estimation information.

[0051] Furthermore, the speed estimation unit in the estimator includes three input terminals, the first input terminal is connected to the output terminal of the controller for collecting control input information, the second input terminal is connected to the output terminal of the unmanned ship for collecting actual speed information of the unmanned ship, and the third input terminal is connected to the parameter estimation unit for collecting model parameter estimation information.

[0052] The parameter estimation unit includes three input terminals, the first of which is connected to the output terminal of the controller for collecting control input information, the second of which is connected to the output terminal of the speed estimation unit in the estimator for collecting estimated speed information of the unmanned ship, and the third of which is connected to the output terminal of the unmanned ship for collecting actual speed information of the unmanned ship;

[0053] Furthermore, the speed estimation unit of the ESO includes four input terminals, the first input terminal is connected to the output terminal of the controller for collecting control input information, the second input terminal is connected to the output terminal of the unmanned ship for collecting actual speed information of the unmanned ship, the third input terminal is connected to the output terminal of the parameter estimation unit for collecting model parameter estimation information, and the fourth input terminal is connected to the output terminal of the disturbance estimation unit for collecting aggregate disturbance estimation value information.

[0054] The disturbance estimation unit includes two input terminals, one is connected to the output terminal of the unmanned ship for collecting the actual speed information of the unmanned ship, and the other is connected to the output terminal of the speed estimation unit in the ESO for collecting the estimated speed information of the unmanned ship.

[0055] As a preferred embodiment of the present invention, the dynamic model of the underactuated unmanned vessel is as follows:

[0056]

[0057] Among them, u is the forward speed of the unmanned ship, υ is the transverse drift speed of the unmanned ship, r is the bow angular velocity of the unmanned ship, and m 11 is the inertia parameter in the forward velocity direction, m 22 is the inertia parameter in the direction of drift velocity, m 33 is the inertial parameter in the direction of the yaw angular velocity, d 11 is the hydrodynamic damping parameter in the forward velocity direction, d 22 is the hydrodynamic damping parameter in the drift velocity direction, d 33is the hydrodynamic damping parameter in the direction of the bow angular velocity. The above parameters are determined according to different ship types and are also affected by load changes. w =[τ wu ,τ wv ,τ wr ] T represents external disturbance; τ u and τ r It is the forward and bow thrust of the under-actuated unmanned ship.

[0058] To rewrite the above model, first define:

[0059] Y u =[Y u1 ,τ u ],Y r =[Y r1 ,τ r ],θ u =[θ u1 ,θ u2 ], and θ r =[θ r1 ,θ r2 ], where Y u1 =[υr,u,1],Y r1 =[uυ,r,1], as well as

[0060] in, is the model parameter vector θ in the forward velocity direction u The estimated value of is the model parameter vector θ of the yaw angular velocity direction r The estimated value of u is the lumped disturbance in the forward velocity direction, σ r is the lumped disturbance in the direction of the yaw angular velocity, τ u is the forward thrust of the underactuated unmanned ship, τ r is the bow thrust of the underactuated unmanned ship, τ wu represents the external disturbance in the u direction, τ wr represents the external disturbance in the r direction.

[0061] Then the above unmanned ship dynamics model is rewritten as follows:

[0062]

[0063] in, and They are θ u and θ r The estimated value of

[0064] The estimator is expressed as follows:

[0065]

[0066] in, is the control gain of the predictor forward velocity estimation unit, is the control gain of the predictor yaw rate estimation unit, Γ u is the control gain of the predictor forward direction parameter speed estimation unit, Γ r is the control gain of the velocity estimation unit of the heading direction parameter of the predictor, and Γ u , and Γ r are all positive numbers; Proj(g) represents the projection operator.

[0067] In the embodiment of the present invention, the ESO module is expressed as follows:

[0068]

[0069] in, is the control gain of the ESO forward velocity estimation unit, is the control gain of the ESO yaw rate estimation unit, is the control gain of the disturbance estimation unit in the forward direction of the ESO, is the control gain of the ESO heading direction disturbance estimation unit, and and All are normal numbers; and Respectively represent σ u and σ r Observed value of .

[0070] The controller module in the present invention adopts a model-free anti-interference controller, which is expressed as follows:

[0071]

[0072] In the formula, μ u is the control gain of the forward direction dynamics controller, μ r is the control gain of the yaw direction dynamics controller, and μ u and μ r are all constants greater than 0, u r is the guidance signal in the forward direction, r r It is the guidance signal of the heading direction. is the derivative information of the guidance signal in the forward direction, It is the derivative information of the guidance signal in the heading direction.

[0073] The scheme and effect of the present invention are further described below through specific application examples. The control goal of this embodiment is to track a given kinematic guidance signal under unknown and changing model parameters and unknown disturbances. The control parameters are selected as follows: u =3.05×10 -11 , Γ r =1.55×10 -13 , μ u =1,μ r =1, the simulation results are as follows Figure 2-6 shown. Figure 2 The tracking error of the forward and heading speeds is shown. It can be seen that the tracking error can quickly converge to 0. Figure 3 The figure shows the estimated effect of the proposed adaptive ESO on the forward speed and bow speed of the unmanned ship, where the dotted line is the estimated speed and the half-dotted line is the actual speed. It can be seen that the estimated value can coincide with the actual value. Figure 4 The figure describes the effect of the proposed adaptive ESO on the estimation of the model parameters of the unknown and changing forward and bow directions of the unmanned ship. The dotted line is the estimated value, and the half-dotted line represents the actual value. It can be seen that the proposed adaptive ESO can accurately approximate the true value of the model parameters. Even if the model parameters change drastically, the proposed method can quickly converge to the vicinity of the true value. Figure 5 The estimated effect diagram of the proposed adaptive ESO on the lumped disturbance is drawn, where the dotted line represents the estimated lumped disturbance value, and the half-dotted line represents the actual lumped disturbance value of the unmanned ship. It can be seen that the proposed method can quickly estimate the lumped disturbance. Figure 6 The control input thrusts of the unmanned vessel in the forward and bow directions are depicted.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An unmanned ship dynamics controller based on an adaptive extended state observer, characterized in that: include: A controller module, an estimator module, and an ESO module; wherein: The controller module is used to track the kinematic guidance signal and output the control signal of the unmanned ship, wherein the control signal includes the forward thrust and bow thrust signals of the unmanned ship; The estimator module is used to output an unmanned ship heading angle estimation signal to the controller module according to the control signal output by the controller module and the actual speed signal of the unmanned ship; the estimator module includes a first speed estimation unit and a parameter estimation unit; The ESO module is used to output an unmanned ship disturbance estimation signal to the controller module according to the control signal, the actual speed signal of the unmanned ship and the unmanned ship heading angle observation signal output by the estimator module; the ESO module includes a second speed estimation unit and a disturbance estimation unit.

2. The unmanned ship dynamics controller based on adaptive extended state observer according to claim 1 is characterized in that: The first input end of the controller module is used to receive the guidance signal, the second input end is used to receive the actual speed signal of the unmanned ship, the third input end is used to receive the unmanned ship heading angle estimation signal output by the parameter estimation unit, and the fourth input end is used to receive the unmanned ship disturbance estimation signal output by the disturbance estimation unit. The output end of the controller module is connected to the control end of the unmanned ship; The first input end of the first speed estimation unit is connected to the output end of the controller, the second input end is connected to the output end of the unmanned ship, and the third input end is connected to the output end of the parameter estimation unit; The first input end of the parameter estimation unit is connected to the output end of the controller, the second input end is connected to the output end of the speed estimation unit, and the third input end is connected to the output end of the unmanned ship; The first input end of the second speed estimation unit is connected to the output end of the controller, the second input end is connected to the output end of the unmanned ship, the third input end is connected to the output end of the parameter estimation unit, and the fourth input end is connected to the output end of the disturbance estimation unit; The first input end of the disturbance estimation unit is connected to the output end of the unmanned ship, and the second input end is connected to the output end of the speed estimation unit.

3. The unmanned ship dynamics controller based on adaptive extended state observer according to claim 1 is characterized in that: The unmanned ship is an underactuated unmanned ship, and the dynamic model of the underactuated unmanned ship is: AND u =[And u1 ,τ u ],AND r =[And r1 ,τ r ] AND u1 =[υr,u,1],And r1 =[uυ,r,1] i u =[θ u1 ,i u2 ], i r =[θ r1 ,i r2 ], Among them, u is the forward speed of the unmanned ship, υ is the transverse drift speed of the unmanned ship, r is the bow angular velocity of the unmanned ship, and m 11 is the inertia parameter in the forward velocity direction, m 22 is the inertia parameter in the direction of drift velocity, m 33 is the inertial parameter in the direction of the yaw angular velocity, d 11 is the hydrodynamic damping parameter in the forward velocity direction, d 22 is the hydrodynamic damping parameter in the drift velocity direction, d 33 is the hydrodynamic damping parameter in the direction of the yaw angular velocity, is the model parameter vector θ in the forward velocity direction u The estimated value of is the model parameter vector θ of the yaw angular velocity direction r The estimated value of u is the lumped disturbance in the forward velocity direction, σ r is the lumped disturbance in the direction of the yaw angular velocity, τ u is the forward thrust of the underactuated unmanned ship, τ r is the bow thrust of the underactuated unmanned ship, τ wu represents the external disturbance in the u direction, τ wr represents the external disturbance in the r direction.

4. The unmanned ship dynamics controller based on adaptive extended state observer according to claim 3 is characterized in that: The estimator module model is as follows: in, is the control gain of the predictor forward velocity estimation unit, is the control gain of the predictor yaw rate estimation unit, Γ u is the control gain of the predictor forward direction parameter speed estimation unit, Γ r is the control gain of the velocity estimation unit of the heading direction parameter of the predictor, and Γ u , and Γ r are all positive numbers; Proj(g) represents the projection operator.

5. The unmanned ship dynamics controller based on adaptive extended state observer according to claim 3 is characterized in that: The ESO module model is as follows: in, is the control gain of the ESO forward velocity estimation unit, is the control gain of the ESO yaw rate estimation unit, is the control gain of the disturbance estimation unit in the forward direction of the ESO, is the control gain of the ESO heading direction disturbance estimation unit, and and All are normal numbers; and Respectively represent σ u and σ r Observed value of .

6. The unmanned ship dynamics controller based on adaptive extended state observer according to claim 3 is characterized in that: The model of the controller module is as follows: Among them, μ u is the control gain of the forward direction dynamics controller, μ r is the control gain of the yaw direction dynamics controller, and μ u and μ r are all constants greater than 0, u r is the guidance signal in the forward direction, r r It is the guidance signal of the heading direction. is the derivative information of the guidance signal in the forward direction, It is the derivative information of the guidance signal in the heading direction.

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