CN110162039A - A kind of novel integrated ship path trace and rollstabilization optimal control method - Google Patents

Abstract

The present invention provides a kind of novel integrated ship path trace and rollstabilization optimal control method, comprising the following steps: being established according to ship motion feature with rudder angle be the ship motion controller model inputted；Choose prediction time domain and control time domain, the future state of forecasting system；It converts the optimization problem for considering that energy consumption is combined with control performance for ship path trace and rollstabilization problem to solve optimization problem in the case where constraining with actuator, and by first element interaction of solution in system；The Ship-Fin-Stabilizer Control model based on sliding-mode method is established, On-line Estimation is carried out to system unknown portions by RBF neural method.The course line that the present invention can make ship tracking set, mitigates the steering burden of crewman, while reducing ship rolling motion, reduces stormy waves to safety of ship, ship equipment, cargo security, the harm of personnel health, it is ensured that the normal operation of ship precision instrument.And the present invention can also reduce the energy consumption of ship, keep ship more economical, environmentally friendly.

Description

Novel comprehensive ship path tracking and rolling reduction optimal control method

Technical Field

The invention relates to the technical field of ship control, in particular to a novel comprehensive ship path tracking and rolling reduction optimization control method.

Background

When the ship sails on the sea, various shakes can be generated under the interference of external wind waves, and the shakes of the ship are most harmful mainly by rolling. The vessel rolling may cause the cargo to move laterally, adversely affecting the structure of the vessel, and even affecting the stability of the vessel, resulting in the vessel overturning. Meanwhile, the rolling of the ship can influence the body state of a driver, so that the driver cannot well fulfill obligations and the navigation safety of the ship is damaged.

At present, certain research is carried out on the problems of ship rolling reduction and path tracking, and relevant documents are as follows: in 2009, "Path following with roll constraints for marine surfaces in wave fields" published in LiZ utilized the advantages of MPC-the problem of processing constraints-the roll angle is regarded as the output for limitation, and straight-line Path tracking and ship roll reduction are realized. Liu C et al, in Journal of Ship Research, published "Integrated line of sight and model predictive control for path following and roll motion using rudders", realized arbitrary path tracking based on Li Z. But both use only rudders for vessel roll reduction and path tracking. As is well known, the fin stabilizer is a special ship stabilizer, and the stabilizing effect is very remarkable. Fang MC published "Applying the PD controller on thermal reduction and track feeding for the ship adding in waves" in Ocean Engineering, combining rudders and anti-roll fins for vessel roll reduction, but is limited to only heading control.

Disclosure of Invention

According to the technical problems, a novel comprehensive ship path tracking and rolling reduction optimization control method is provided. The invention mainly utilizes a novel comprehensive ship path tracking and rolling reduction optimization control method, which comprises the following steps:

s1: establishing a ship motion control model taking a rudder angle as input according to the ship motion characteristics;

s2: selecting a prediction time domain and a control time domain, and designing a model prediction controller; s3: the ship path tracking and rolling reduction problem is converted into an optimization problem which considers the combination of energy consumption and control performance, the optimization problem is solved under the constraint of an actuator, and the first element of the solution acts on the system;

s4: and establishing a fin stabilizer control model based on a sliding mode method, and carrying out online estimation on the unknown part of the system by using an RBF neural network method.

Further, according to the ship motion characteristics, a ship motion control model is established:

wherein,u_tδ, e denotes a path tracking error,indicates course error, phi indicates roll angle, v indicates roll speed, p indicates roll angular velocity, r indicates yaw angular velocity, delta indicates rudder angle,

wherein, a₁₁…a₃₄，b₁₁…b₁₃Can be obtained by calculating ship parameters;

converting the ship motion control model into a discrete model by giving sampling time:

x(k+1)＝Ax(k)+Bδ(k) (21)

y(k)＝Cx(k) (22)

wherein x ∈ R^6×1，y∈R^4×1，A∈R^6×6，B∈R^6×1，C∈R^4×6X (k) represents a state vector matrix of a system at the k-th sampling moment, x (k +1) represents a state vector matrix of a system at the k + 1-th sampling moment, delta (k) represents a control input matrix at the k-th sampling moment, y (k) represents an output matrix of the system at the k-th sampling moment, and the matrixes A, B and C are respectively A_t，B_t，C_tThe discretized matrix, k, represents the sampling instant.

Further, the rudder angle is limited, the control model is rewritten into the incremental model, and equations (2) and (3) are converted as follows:

when equation (2) is differentiated, it can be expressed as:

x(k+1)-x(k)＝A(x(k)-x(k-1))+B(δ(k)-δ(k-1)) (23)

to simplify the expression of formula (4), the following definitions are made:

Δx(k)＝x(k)-x(k-1)

Δδ(k)＝δ(k)-δ(k-1)

equation (4) can be expressed as:

Δx(k+1)＝AΔx(k)+BΔδ(k) (24)

similarly, when equation (3) is differentiated, then:

y(k+1)-y(k)＝C(x(k+1)-x(k))＝CAΔx(k)+CBΔδ(k) (6)

equations (5) and (6) are expressed as an amplification matrix:

wherein, O_6×4Represents a 6 × 4 all-zero matrix, I represents a 4 × 4 unit matrix; and is

Equation (7) can be expressed as:

selecting the prediction time domain N_pAnd the control time domain N_cThen at the kth sampling instant, the future inputs of the system in the control time domain can be expressed as: δ (k), δ (k +1), δ (k +2), …, δ (k + N)_c-1)；

The future state of the system in the prediction time domain can be represented as:

the future output of the system in the prediction time domain can be represented as: y (k +1| k), y (k +2| k), y (k +3| k), …, y (k + N | k)_P|k)。y(k+N_PI k) respectively representing the sampling times according to the k-thy (k), predicting the k + N_pSampling timey(k+N_p) A value of (d);

at the kth sampling instant, future inputs by the system: δ (k), δ (k +1), δ (k +2), …, δ (k + C)N_c-1), finding Δ δ (k), Δ δ (k +1), Δ δ (k +2), …, Δ δ (k + N)_c-1), bringing (8) the output of the system in the prediction time domain:

the variables in equation (9) are defined as follows:

ΔU＝[Δδ(k) Δδ(k+1) Δδ(k+2) … Δδ(k+N_c-1)]^T∈R^Nc；

equation (9) can be expressed as:

wherein F and Φ are:

further, the ship path tracking and rolling reduction problem is converted into an optimization problem which considers the combination of energy consumption and control performance; it defines a cost function as:

J＝Y^TQY+ΔU^TRΔU (11)

wherein, Y^TQY denotes the target adjustment to the deviation of the control error, Δ U^TR Δ U represents the adjustment to energy consumption, Q, R represent the weight matrix, respectively;

and (3) limiting the magnitude and the magnitude of the change rate of the input rudder angle delta:

δ_min≤δ(k+i)≤δ_max,i＝0,1,…N_p-1

Δδ_min≤Δδ(k+i)≤Δδ_max,i＝0,1,…N_p-1

solving a cost function, solving the control input delta by using the first term of the delta U and carrying in (2), solving the system state, outputting the system, sequentially carrying in the formulas (3) to (11), solving the cost function again to obtain a new delta U, solving the new control input delta by using the first term of the new delta U and carrying in (2), and repeating the circulation until the set circulation number is reached.

Further, establishing a fin stabilizer control model based on a sliding mode method:

S＝cφ+p (12)

wherein c represents a positive design parameter, phi represents a roll angle, and p represents a roll angular velocity;

derivation is performed on equation (12):

in the formula, b_fExpressing constants, approximating f by radial basis function neural network method_p，Wherein,is f_pIs determined by the estimated value of (c),representing the adaptation law of the neural network, H₁(z) represents a basis function vector.

The fin stabilizer control includes an equivalent control portion α_eqAnd a switching control section α_sw：

α＝α_eq+α_sw(14)

Wherein,

in the above formula, η₁，k₁All represent positive control design parameters;

defining the Lyapunov function:

wherein, gamma represents a positive design parameter,W₁ ^*representing an optimal neural network adaptation law;

derivation of the above Lyapunov function:

in the formula,

wherein H₁(Z) represents a basis function vector, and equations (14), (15), (16) and (19) are simplified by bringing them into equation (18):

a stable closed-loop control system is determined by the Lyapunov theorem according to the equations (17) and (20).

Compared with the prior art, the invention has the following advantages:

the invention provides a novel automatic control method of a ship, which can make the ship sail along a preset path in the stormy waves and reduce the rolling of the ship by combining a stabilizer fin and a rudder. The invention can be installed on ships with stabilizer fins, such as commercial ships, ferries, cruise ships and the like, and is used as a supplement of the existing autopilot control system on the ships. The invention can make the ship track the set course, reduce the steering burden of the shipman, reduce the rolling motion of the ship, reduce the harm of wind waves to the safety of the ship, the equipment and goods of the ship and the health of personnel, and ensure the normal operation of the precision instrument of the ship. The invention can also reduce the energy consumption of the ship, so that the ship is more economic and environment-friendly.

(1) The prior patent technology only has simple ship rolling reduction or path tracking control, and the ship rolling reduction and path tracking control can be simultaneously carried out in the application.

(2) It is found after only using stabilizer fin to subtract rolling control that current patent technology compares, and the stabilizer fin only need use less control angle just can reach very good effect that the rolling effect of this application under various stormy waves circumstances needs more obvious, simultaneously.

(3) The rudder and fin stabilizer combined control of the application type does not perform ship path tracking and roll reduction control on the rudder and the fin stabilizer respectively, but the rudder also cooperates the fin stabilizer to perform ship roll reduction control when performing ship path tracking, and therefore the roll reduction effect is more obvious than that of a single-use rudder or fin stabilizer.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

FIG. 1 is a schematic view of the overall process of the present invention.

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

As shown in fig. 1, the invention provides a novel comprehensive ship path tracking and roll reduction optimization control method, which comprises the following steps: step S1: and establishing a ship motion control model taking the rudder angle as input according to the ship motion characteristics. Step S2: selecting a prediction time domain and a control time domain, and designing a model prediction controller. Step S3: the method comprises the steps of converting ship path tracking and rolling reduction problems into an optimization problem which considers the combination of energy consumption and control performance, solving the optimization problem under the constraint of an actuator, and enabling a first element of the solution to act on a system. Step S4: and establishing a fin stabilizer control model based on a sliding mode method, and carrying out online estimation on the unknown part of the system by using an RBF neural network method.

As a preferred embodiment, according to the ship motion characteristics, a ship motion control model is established:

wherein,u_tδ, e denotes a path tracking error,indicating course error, phi indicating roll angle, v indicating roll speed, p indicating roll angular speed, r indicating yaw angular speed, and delta indicating rudder angle;

wherein, a₁₁…a₃₄，b₁₁…b₁₃Can be obtained by calculating ship parameters;

converting the ship motion control model into a discrete model by giving sampling time:

x(k+1)＝Ax(k)+Bδ(k) (2)

y(k)＝Cx(k) (3)

wherein x ∈ R^6×1，y∈R^4×1，A∈R^6×6，B∈R^6×1，C∈R^4×6X (k) represents a state vector matrix of a system at the k-th sampling moment, x (k +1) represents a state vector matrix of a system at the k + 1-th sampling moment, delta (k) represents a control input matrix at the k-th sampling moment, y (k) represents an output matrix of the system at the k-th sampling moment, and the matrixes A, B and C are respectively A_t，B_t，C_tThe discretized matrix, k, represents the sampling instant.

In the present embodiment, the control model is rewritten into the incremental model by limiting the steering angle, and equations (2) and (3) are converted as follows:

when equation (2) is differentiated, it can be expressed as:

x(k+1)-x(k)＝A(x(k)-x(k-1))+B(δ(k)-δ(k-1)) (4)

to simplify the expression of formula (4), the following definitions are made:

Δx(k)＝x(k)-x(k-1)

Δδ(k)＝δ(k)-δ(k-1)

equation (4) can be expressed as:

Δx(k+1)＝AΔx(k)+BΔδ(k) (5)

similarly, when equation (3) is differentiated, then:

y(k+1)-y(k)＝C(x(k+1)-x(k))＝CAΔx(k)+CBΔδ(k) (6)

equations (5) and (6) are expressed as an amplification matrix:

wherein, O_6×4Represents a 6 × 4 all-zero matrix, I represents a 4 × 4 unit matrix; and is

Equation (7) can be expressed as:

selecting the prediction time domain N_pAnd the control time domain N_cThen at the kth sampling instant, the future inputs of the system in the control time domain can be expressed as: δ (k), δ (k +1), δ (k +2), …, δ (k + N)_c-1); the future state of the system in the prediction time domain can be represented as: the future output of the system in the prediction time domain can be represented as: y (k +1| k), y (k +2| k), y (k +3| k), …, y (k + N | k)_P|k)。y(k+N_PI k) respectively representing the sampling times according to the k-thy (k), predicting the k + N_pSampling timey(k+N_p) A value of (d); at the kth sampling instant, future inputs by the system: : δ (k), δ (k +1), δ (k +2), …, δ (k + N)_c-1), finding Δ δ (k), Δ δ (k +1), Δ δ (k +2), …, Δ δ (k + N)_c-1), bringing (8) the output of the system in the prediction time domain:

the variables in equation (9) are defined as follows:

ΔU＝[Δδ(k) Δδ(k+1) Δδ(k+2) … Δδ(k+N_c-1)]^T∈R^Nc；

equation (9) can be expressed as:

wherein F and Φ are:

as a preferred embodiment, the ship path tracking and rolling reduction problem is converted into an optimization problem which considers the combination of energy consumption and control performance; it defines a cost function as:

J＝Y^TQY+ΔU^TRΔU (11)

wherein, Y^TQY denotes the target adjustment to the deviation of the control error, Δ U^TR Δ U represents the adjustment to energy consumption, Q, R represent the weight matrix, respectively;

and (3) limiting the magnitude and the magnitude of the change rate of the input rudder angle delta:

δ_min≤δ(k+i)≤δ_max,i＝0,1,…N_p-1

Δδ_min≤Δδ(k+i)≤Δδ_max,i＝0,1,…N_p-1

solving a cost function, solving the control input delta by using the first term of the delta U and carrying in (2), solving the system state, outputting the system, sequentially carrying in the formulas (3) to (11), solving the cost function again to obtain a new delta U, solving the new control input delta by using the first term of the new delta U and carrying in (2), and repeating the circulation until the set circulation number is reached.

Further, establishing a fin stabilizer control model based on a sliding mode method:

S＝cφ+p (12)

wherein c represents a positive design parameter, phi represents a roll angle, and p represents a roll angular velocity;

derivation is performed on equation (12):

in the formula, b_fExpressing constants, approximating f by radial basis function neural network method_p，Wherein,is f_pIs determined by the estimated value of (c),representing the adaptation law of the neural network, H₁(z) represents a basis function vector.

The fin stabilizer control includes an equivalent control portion α_eqAnd a switching control section α_sw：

α＝α_eq+α_sw(14)

Wherein,

in the above formula, η₁，k₁All represent positive control design parameters;

defining the Lyapunov function:

wherein, gamma represents a positive design parameter,W₁ ^*representing an optimal neural network adaptation law;

derivation of the above Lyapunov function:

in the formula,

wherein H₁(Z) represents a basis function vector, and equations (14), (15), (16) and (19) are simplified by bringing them into equation (18):

a stable closed-loop control system is determined by the Lyapunov theorem according to the equations (17) and (20).

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (5)

1. A novel comprehensive ship path tracking and rolling reduction optimal control method is characterized by comprising the following steps:

s1: establishing a ship motion control model taking a rudder angle as input according to the ship motion characteristics;

s2: selecting a prediction time domain and a control time domain, and designing a model prediction controller;

s3: the ship path tracking and rolling reduction problem is converted into an optimization problem which considers the combination of energy consumption and control performance, the optimization problem is solved under the constraint of an actuator, and the first element of the solution acts on the system;

s4: and establishing a fin stabilizer control model based on a sliding mode method, and carrying out online estimation on the unknown part of the system by using an RBF neural network method.

2. The novel integrated ship path tracking and roll reduction optimization control method according to claim 1, further characterized by comprising the following steps:

according to the ship motion characteristics, establishing a ship motion control model:

wherein,u_tδ, e denotes a path tracking error,indicates course error, phi indicates roll angle, v indicates roll speed, p indicates roll angular velocity, r indicates yaw angular velocity, delta indicates rudder angle,

wherein a is₁₁…a₃₄，b₁₁…b₁₃Can be obtained by calculating ship parameters;

converting the ship motion control model into a discrete model by giving sampling time:

x(k+1)＝Ax(k)+Bδ(k) (2)

y(k)＝Cx(k) (3)

wherein x ∈ R^6×1，y∈R^4×1，A∈R^6×6，B∈R^6×1，C∈R^4×6X (k) denotes the state vector matrix of the system at the k-th sampling instant, x (k +1) denotes the state vector matrix of the system at the k + 1-th sampling instant,delta (k) represents a control input matrix at the k-th sampling moment, y (k) represents an output matrix of a system at the k-th sampling moment, and the matrixes A, B and C are respectively A_t，B_t，C_tThe discretized matrix, k, represents the sampling instant.

3. The novel integrated ship path tracking and roll reduction optimization control method according to claim 1, further characterized by comprising the following steps:

the rudder angle is limited, the control model is rewritten into an incremental model, and the equations (2) and (3) are transformed as follows:

when equation (2) is differentiated, it can be expressed as:

x(k+1)-x(k)＝A(x(k)-x(k-1))+B(δ(k)-δ(k-1)) (4)

to simplify the expression of formula (4), the following definitions are made:

Δx(k)＝x(k)-x(k-1)

Δδ(k)＝δ(k)-δ(k-1)

equation (4) can be expressed as:

Δx(k+1)＝AΔx(k)+BΔδ(k) (5)

similarly, when equation (3) is differentiated, then:

y(k+1)-y(k)＝C(x(k+1)-x(k))＝CAΔx(k)+CBΔδ(k) (6)

equations (5) and (6) are expressed as an amplification matrix:

wherein, O_6×4Represents a 6 × 4 all-zero matrix, I represents a 4 × 4 unit matrix; and is

Equation (7) can be expressed as:

selecting the prediction time domain N_pAnd the control time domain N_cThen at the kth sampling instant, the future inputs of the system in the control time domain can be expressed as: δ (k), δ (k +1), δ (k +2), …, δ (k + N)_c-1); the future state of the system in the prediction time domain can be represented as:

the future output of the system in the prediction time domain can be represented as: y (k +1| k), y (k +2| k), y (k +3| k), …, y (k + N | k)_P|k)。y(k+N_PI k) respectively representing the sampling times according to the k-thy (k), predicting the k + N_pSampling timey(k+N_p) A value of (d);

at the kth sampling instant, future inputs by the system: δ (k), δ (k +1), δ (k +2), …, δ (k + N)_c-1), finding Δ δ (k), Δ δ (k +1), Δ δ (k +2), …, Δ δ (k + N)_c-1), bringing (8) the output of the system in the prediction time domain:

the variables in equation (9) are defined as follows:

equation (9) can be expressed as:

wherein F and Φ are:

4. the novel integrated ship path tracking and roll reduction optimization control method according to claim 1, further characterized by comprising the following steps:

the ship path tracking and rolling reduction problem is converted into an optimization problem which considers the combination of energy consumption and control performance; it defines a cost function as:

J＝Y^TQY+ΔU^TRΔU (11)

wherein, Y^TQY denotes the target adjustment to the deviation of the control error, Δ U^TR Δ U represents the adjustment to energy consumption, Q, R represent the weight matrix, respectively;

and (3) limiting the magnitude and the magnitude of the change rate of the input rudder angle delta:

δ_min≤δ(k+i)≤δ_max,i＝0,1,…N_p-1

Δδ_min≤Δδ(k+i)≤Δδ_max,i＝0,1,…N_p-1

solving a cost function, solving the control input delta by using the first term of the delta U and carrying in (2), solving the system state, outputting the system, sequentially carrying in the formulas (3) to (11), solving the cost function again to obtain a new delta U, solving the new control input delta by using the first term of the new delta U and carrying in (2), and repeating the circulation until the set circulation number is reached.

5. The novel integrated ship path tracking and roll reduction optimization control method according to claim 1, further characterized by comprising the following steps:

establishing a fin stabilizer control model based on a sliding mode method:

S＝cφ+p (12)

wherein c represents a positive design parameter, phi represents a roll angle, and p represents a roll angular velocity;

derivation is performed on equation (12):

in the formula, b_fExpressing constants, approximating f by radial basis function neural network method_p，Wherein,is f_pIs determined by the estimated value of (c),representing the adaptation law of the neural network, H₁(z) represents a basis function vector.

The fin stabilizer control includes an equivalent control portion α_eqAnd a switching control section α_sw：

α＝α_eq+α_sw(14)

Wherein,

in the above formula, η₁，k₁All represent positive control design parameters;

defining the Lyapunov function:

wherein, gamma represents a positive design parameter,W₁ ^*representing an optimal neural network adaptation law;

derivation of the above Lyapunov function:

in the formula,

wherein H₁(Z) represents a basis function vector, and equations (14), (15), (16) and (19) are simplified by bringing them into equation (18):

a stable closed-loop control system is determined by the Lyapunov theorem according to the equations (17) and (20).