WEC controller, method, and system

Abstract

WEC controller, method and system, comprising: a controller (10) for a gyroscopic structure (2) associated with a floating float (3) and comprising an electric converter (9) suitable for converting a rotational energy of said floating float (3) into electric energy, the controller (10) receives as input a perturbed output state (x) comprising an operating variable of said gyroscopic structure (2), determines a drive signal (u) for said electric converter (9), and determines a first signal portion (v) determined using a predictive control model of said gyroscopic structure (2) calculated based on said perturbed output state (x) and a second signal portion (v) determined using a tube convergence calculated on a parametric deviation (r) of said operating variable of said perturbed output state (x). * ), and the parametric deviation (r) is determined by the unperturbed output nominal state (z NP ) of the second signal part (v * ) and

Description

[Technical Field]

[0001] The present invention relates to a controller for a wave energy conversion system, including an electric generator or a wave energy converter (WEC) capable of generating electrical energy from ocean waves.

[0002] The present invention also relates to a method for controlling an electrical converter of a gyroscopic structure associated with a floating hull, and an associated WEC system. [Background technology]

[0003] As is well known, wave power is one of the main sources of renewable energy, and in recent years, many large-scale plants have been developed and constructed for the conversion of wave power into electrical energy. Some plants, such as inertial wave energy concentrators (WEC) or inertial wave energy concentrators (ISWEC) plants, use reactants or PTOs that utilize the inertia of large masses to generate a reaction and extract its force.

[0004] Inertial conversion systems including floating bodies are known. The floating bodies are fixed to the seabed and equipped with directional gyroconverters, each connected to a power generator. The generators can convert rotational energy, caused by the vibration of the floating bodies and induced by wave forces, into electrical energy through the movement of a flywheel.

[0005] In this case, the gyroscope structure includes a gyroscope, a flywheel associated with a float by a suspension device, and an electric converter coupled to a rotation axis substantially orthogonal to the main inertia axis of the flywheel. The converter includes an electric motor controlled by a driver / inverter coupled to the rotation axis by appropriate joints and gears. Thus, power can be generated through the electric motor rather than supplied to it by operating in the even quadrant of the VI diagram of the driver / inverter by applying a reaction resistance driving torque that primarily acts as a damper.

[0006] To maximize the extracted power and improve the efficiency of the structure, the opposing resistive drive torque should be appropriately modulated.

[0007] Classical control systems such as PID (Proportional, Integrative, and Derivative) controllers are well-known and widely used in industrial settings, typically operating as controllers for SISO (Single-Input Single-Output) systems. While satisfactory in various aspects, systems using PID controllers have drawbacks. In fact, waves are described in the literature as random processes with statistical distribution characteristics known as the "JONSWAP distribution," and their parameters depend on the sea area in question. In the case of PID controllers, the control parameters are updated by a pre-set gain scheduling according to sea condition forecasts. Therefore, the table values ​​used as control parameters may differ from the values ​​required by the actual waves, and losses as a result of the extracted energy are inherent.

[0008] The use of dynamic systems employing a controller using a state evolution model is known. Such dynamic systems employ a Model Predictive Control (MPC) controller. The MPC controller is described in the paper by D. Wilson et al., "A comparison of WEC control strategies", Sandia National Labs, Albuquerque, New Mexico, Tech. Rep. SAND2016 - 4293, April 2016.

[0009] In its most general form, the MPC controller is based on feedback from the state and a control law dynamically calculated by minimizing an appropriate cost function for optimizing the system state.

[0010] The MPC controller differs from a PID controller in several essential aspects. a) The control law or control function is based on the solution of the Euler - Lagrange equation that generates, in classical optimization theory, a function of time that results as the stationary part (as the superior extremity) of the cost function. b) The cost function internally includes various terms related to the state, the input signal, and the kinetic energy associated with the state and the control signal, and is usually a convex function. c) The cost function usually includes a term that becomes null when the required state is achieved and a term that becomes null when the energy of the control action is minimized.

[0011] Furthermore, the control law of the MPC controller can define the dynamic constraints used to minimize the cost function. The control law of the MPC controller is based on the solution of the Euler - Lagrange equation and is essentially a function of time that simultaneously minimizes the error with respect to the required state and the error with respect to the energy involved in achieving this result.

[0012] The use of MPC controllers to control WEC systems by employing state-deployment models of gyroscopic structures is known. This allows for a single set of control parameters for all ocean wave conditions, relative to the location where the plant is installed. The accuracy of the state-deployment model and the estimation of the associated parameter set affect the performance of the control system.

[0013] In other words, these MPC controllers are optimized for systems with fixed, predetermined parameters, and are therefore less efficient for systems affected by variations in these parameters or the presence of random disturbances such as ocean wave conditions. Similarly, ocean conditions that are not considered during the calibration of MPC controller parameters can lead to suboptimal energy extraction conditions, i.e., low performance.

[0014] A known solution is described in the paper BRACOO G et al.: "Optimizing energy production of an Inertial Sea Wave Energy Converter via Model Predictive Control," Control Engineering Practice, Pergamon Press, Oxford, GB-vol.96, January 17, 2020 - XP086048062.

[0015] The fundamental technical problem of this invention is to devise a control method for a gyroscope structure having functional and structural features that enables error reduction due to the modeling of wave and gyroscope structures, thereby maximizing the extracted energy and thus overcoming the shortcomings mentioned with reference to the prior art. [Overview of the project]

[0016] The fundamental solution of this invention is to drive the future development of the operating variables that define the state of the gyroscope structure in a constrained manner, thereby improving the robustness and efficiency of the control.

[0017] Based on this solution, the technical problems are solved by the controller defined in claim 1 and the specific embodiments described in claims 2-5.

[0018] The present invention also relates to the control method defined by claim 6, the specific embodiments described by claims 7-10, and the WEC system defined by claim 11. [Brief explanation of the drawing]

[0019] Further features and advantages of the present invention will become apparent from the following description of preferred embodiments and variations thereof of the system provided for the examples with reference to the accompanying drawings. [Figure 1] Figure 1 schematically shows the inertial WEC system (ISWEC) and gyroscope structure. [Figure 2] Figure 2 schematically shows the inertial WEC system (ISWEC) and gyroscope structure. [Figure 3] Figure 3 is a block diagram of a controller manufactured according to the present invention. [Figure 4] Figure 4 schematically shows, in an orthogonal diagram, the ideal and actual output state development of a pipe convergence model applied to a system with two state variables. [Figure 5] Figure 5 shows a block diagram of a second embodiment of the controller manufactured according to the present invention. [Figure 6] Figure 6 schematically shows a simulation of the time-dependent trend of extracted power from an inertial WEC conversion system using an implemented controller according to the present invention. [Figure 7] Figure 7 shows some details of the graph in Figure 6. [Figure 8] Figure 8 shows some details of the graph in Figure 6. [Figure 9] Figure 9 shows some details of the graph in Figure 6. [Modes for carrying out the invention]

[0020] Referring to these figures, 1 outlines an inertial WEC system, i.e., an ISWEC, which includes a floating body 3 and a pair of identical and independent gyroscope structures 2 arranged symmetrically to balance the forces with respect to the floating body 3. Each gyroscope structure 2 includes an electrical converter 9 suitable for converting the rotational energy of the floating body 3 into electrical energy.

[0021] In the schematic configuration shown in Figure 1, the floating buoy 3 is substantially symmetrical with respect to the roll axis X and has a first gyroscope structure 2 in which the gyroscope 6 is highlighted, and a second gyroscope structure 2'. The second gyroscope structure 2' is shown with a cover protection 7 suitable for covering the underlying flywheel. In the following description, reference will be made to the first gyroscope structure 2, which includes the gyroscope 6.

[0022] The floating body 3 has a pitch angle δ and a rotational moment forced by the wave. In JPEG0007882888000001.jpg939, the image is formed to allow rotation along the pitch axis Y. In Figures 1 and 2, the wave is indicated by the arrow in direction A.

[0023] Although not shown in the diagram, in conventional methods, the floating body 3 is fixed to the seabed, and its roll axis X is substantially parallel to the wave direction A. The roll axis X is perpendicular to the pitch axis Y. The floating body 3 also has a yaw axis Z substantially perpendicular to the plane P defined by the roll axis X and the pitch axis Y.

[0024] JPEG0007882888000002.jpg47166

[0025] The electric converter 9, schematically shown in the figure, includes an electric motor associated with a driver / inverter and is driven by the controller 10 via a drive signal u that cancels out precession torque to maximize the extracted power.

[0026] Figure 3 shows a portion of the inertia conversion system WEC1 of Figure 1 via a block diagram, in relation to the gyroscope structure 2 and controller 10 designed according to the present invention.

[0027] The gyroscope structure 2 is represented by a real plant block 20, i.e., a nonlinear system, which includes a structure block 21 suitable for representing the actual state z of the gyroscope structure 2. The structure block 21 receives a drive signal u and generates a vector containing the unperturbed output state z of the gyroscope structure 2.

[0028] The perturbation and / or disturbance (w) is added to the unperturbed output state (z) to define the perturbed output state x, which includes the operating variables of the gyroscope structure 2. The perturbation w is filtered by the transfer function and includes a set of external and internal perturbations to the gyroscope structure 2, such as mooring effects and external driving forces of waves that may be applied to other perturbing elements / forces.

[0029] The controller 10 receives a perturbation output state x as input to generate a drive signal u suitable for driving or activating the electrical converter 9. In its most common form, the controller 10 is a TRMPC, an acronym for Tube-Based Robust Model Predictive Control.

[0030] In the first embodiment, the operating variables defining the perturbed output state x can be represented in vector matrix form as follows:

number

[0031] Controller 10 controls the first signal portion v and the second signal portion v * The drive signal u is determined by adding the two values.

[0032] The first signal portion v is determined by a predictive control block 13 which includes a predictive control model for a gyroscope structure 2 having a perturbed output state x received as input. In one embodiment, the predictive control model has a cost function, for example, employed by a conventional MPC controller. In this case, the predictive control block 13 uses a control law, the solution of which can be based on a solution to the Euler-Lagrange equation by providing a time series that minimizes errors with respect to the desired state and minimizes the energy used to realize that state.

[0033] Second signal portion v * This is determined by a nominal convergence module 18 having nominal tube convergence. Specifically, the nominal convergence module 18, together with block 13, enables the convergence of the expansion of perturbed output state x to the nominal expansion z of the output state according to TRMPC control.

[0034] The nominal convergence module 18 includes a defined gain matrix K, which takes into account the uncertainty of the system and conforms to the pipe convergence requirement for convergence to a predetermined value, such as zero for the parametric deviation r of the gyroscope structure 2. The parametric deviation r is obtained as the difference of the operating variables of the perturbed output state x with respect to the unperturbed output state z of the gyroscope structure 2.

[0035] Next, the second signal portion v * This is obtained as the product of the gain matrix K and the parametric deviation or error r.

[0036] Figure 4 schematically shows the pipe convergence of a linear system with two state variables x1 and x2 under TRMPC control. In the plane of variables x1 and x2, the solid line represents the nominal state z0-z that converges to zero. NIdentify the required trajectory in the time interval T that includes N subsequent steps of the expansion length Δt. The dotted line represents the N perturbed actual states X0-X of the system N of the actual trajectory generated by the expansion. Along with the triangular shape, in fact, the section of the tube is highlighted, and for each nominal state z0-z N corresponds to the appropriate boundary space X’ for identifying the allowable actual perturbed states X0-X N and takes into account the time evolution in N subsequent steps from the initial instant t0 to the final instant t N i.e., up to (t0+T). Naturally, in this case, the boundary space X’ which is triangular, is assumed to include all possible perturbations w and all errors due to the uncertainties related to the model parameters with respect to each nominal state z0-z N .

[0037] The boundary space X’ defines the maximum distance of the perturbed output state x with respect to the nominal states z0-z N and is located at the center of the boundary space X’. Of course, the boundary space X’ can have a shape different from the perimeter of the triangle, and its size depends on the strength required for the tube convergence.

[0038] The system of Figure 4 with two state variables, x1 and x2, has a two-dimensional representation in space. Clearly, in the case of the gyroscope structure 2, in the first embodiment, as shown in the vector matrix 1, there are four state variables, and thus the boundary space X’ is four-dimensional. The time interval T is predefined at the design stage considering the dynamic characteristics of the system being analyzed, such as the dynamic characteristics of the wave referring to the wave period. Of course, the characteristics of the equipment and / or hardware used are also relevant.

[0039] The nominal states z0-z N are ideal non-perturbed states and are determined based on the non-perturbed model of the gyroscope structure 2.

[0040] In one embodiment, the gain matrix K is determined by using a procedure following linear matrix inequalities or LMI theory, as described in the paper "Tube-Based Robust Model Predictive Control for Spacecraft Proximity Operations in the Presence of Persistent Disturbance" by M. Mammerella, Capello, Park, Guglieri, Romano, 2018, published June 1, 2018, in "Aerospace Science and Technology - Volume 77, pp. 585-594".

[0041] Therefore, the definition of this region ("pipe") and its width are obtained as uncertainty in both the model used in the control and the amount of any unmodeled disturbance (e.g., mooring effect).

[0042] According to the embodiment shown in Figure 3, the prediction block 13 is implemented by cascaded nominal units 14 and 15. The nominal unit 14 includes a nominal non-perturbed model of the gyroscope structure 2. The nominal unit 14 receives a perturbed output state x as input and also receives a first portion of the signal v generated by the prediction unit 15 as feedback, and the non-perturbed output nominal state z of the gyroscope structure 2. NP Obtain it.

[0043] The prediction unit 15 uses a cost function J that includes a quadratic term for non-quadratic terms related to the state of the gyroscope structure 2, its driving operation, and the instantaneous force absorbed by the gyroscope structure 2. T Includes a predictive dynamic control model having the following characteristics.

[0044] JPEG0007882888000004.jpg32166

[0045] Furthermore, according to this embodiment, the nominal convergence module 18 is a non-perturbed nominal state z generated by the nominal unit 14. NP The signal v is taken as input, and the parametric deviation r is calculated. * The second part is obtained by multiplying the parametric deviation r by the gain matrix K.

[0046] signal v * The second part is the actual state x0-x of the gyroscope structure 2. N This allows for more precise maintenance of the actual trajectory of the unfolding within the boundary space X' or the optimal state, while also enabling modification of the drive signal u. This allows the gyroscope system 2 to be optimally controlled even in the presence of external random perturbations w generated by the wave.

[0047] A second embodiment is shown in Figure 5, in which the controller 10 determines the drive signal u as an extended drive signal. Below, only the differences from the previous solution will be specifically described.

[0048] The extended drive signal u is the first part of the extended signal v and signal v * This includes the second part of the same thing.

[0049] The prediction unit 15 is the extended nominal state z a It takes as input and the non-perturbed nominal state z NP , and the operating variable of the perturbed output state x and the non-perturbed nominal state z of the gyroscope structure 2 NP This includes a parametric deviation r calculated as the difference between the operating variable and the operating variable.

[0050] Extended state z a Block 15 is a vector that allows for the modeling and prediction of the disturbance w trend, while enhancing the prediction of the gyro unit state.

[0051] In this way, the cost function J T The drive signal generated by the prediction unit 15 is minimized by minimizing the drive signal JPEG0007882888000005.jpg864 allows for more accurate determination of the extended drive signal u, thus enabling more efficient control of the electrical converter 9.

[0052] Therefore, the acquired controller is very robust with respect to the unfolding control of perturbed states. Such control is achieved by both dual feedback of the perturbed output state x and a dynamic tube convergence model.

[0053] Furthermore, the applicant was able to verify that the controller 10, designed in this manner, enables the gyroscope system 2 to be maintained or returned to the desired state, even in the presence of uncertainty in the predictive control model parameters of the predictive unit 15 and the nominal unit 14.

[0054] Figure 6 schematically shows a simulation of the trend of extracted power as a function of time of the inertial WEC conversion system using controller 10 implemented according to the present invention. Power is negative and represents extracted power. Figures 7-9 show some detailed elements related to the graph in Figure 6. The maximum error has been found to be approximately 2% with the following parameter variations. - Gyroscope mass fluctuation (VMG curve): ±10% - Floating mass fluctuation (VMS curve): ±15% - Gyroscope inertia variation (VIG curve): ±15% - Flywheel inertia variation (VIV curve): ±2%

[0055] The charts in Figures 7-9, identified by Nom, show the trends in extracted power, taking into account the nominal parameters of the model subjected to forced waves.

[0056] Figure 7 shows the nominal curve Nom, along with two VMG curves and two VMS curves. Figure 8 shows the nominal curve Nom, along with two VIV curves and two VIG curves. Figure 9 shows the nominal curve Nom, along with the extracted lower limit of power L.I and upper limit L S The curve representing this is emphasized by collectively changing the mass and inertia of the floating body 3, the gyroscope, and the flywheel.

[0057] The present invention also refers to a method for controlling the gyroscope structure 2 of the WEC system 1 described above, and details thereof and cooperating components having the same structure and function as described above will be shown with the same numbers and reference numerals.

[0058] Specifically, the gyroscope structure 2 is associated with a floating body 3 and includes an electrical converter 9 that converts the rotational energy of the floating body 3 into electrical energy. In its most common form, the method includes a TRMPC (Tube-Based Robust Model Predictive Control) for the electrical converter 9.

[0059] The controller 10 receives a perturbation output state x that includes the operating variables of the gyroscope structure 2.

[0060] According to the present invention, the method involves the first part of signal (v) and signal v * The electric converter 9 is driven by a drive signal u obtained by adding the second part of the signal.

[0061] The method is designed to determine the first part of the signal v by using a predictive control model of the gyroscope structure 2 calculated with respect to the perturbed output state x.

[0062] Furthermore, the method uses a nominal convergence module 18 having dynamic tube convergence to obtain the second signal portion v * We devise a way to determine this. Dynamic tube convergence is calculated using the parametric deviation r of the operating variable of the perturbed output state x. These parametric deviations r are calculated using the perturbed output state x and the non-perturbed output nominal state z of the gyroscope structure 2. NP It is defined as the difference in the operating variables between and . Non-perturbed output nominal state z NPThis is obtained by the non-perturbed nominal model of gyroscope structure 2.

[0063] In the first embodiment, the method is designed to determine the first signal portion v using a predictive control model implemented by a conventional MPC controller.

[0064] Furthermore, the method uses "dynamic tube convergence," that is, a gain matrix K defined as the convergence of the parametric deviation r of the gyroscope structure 2 to a predetermined value, preferably zero. Generally, dynamic tube convergence deals with linear systems, and in Figure 4, a more general implementation of a system having two state variables, x1 and x2, is shown. In this way, in state space, the gain matrix K is defined as the required non-perturbed output states z0-z of the gyroscope structure 2. n Perturbation output state X0-X n This enables the development of [the product / service].

[0065] Required non-perturbative state z0-z n This is determined a priori based on the nominal model of the gyroscope structure 2, that is, considering a non-disturbed and linear system.

[0066] In the design phase, the method involves a time interval T, the number of subsequent steps N, and each required non-disturbant state z0-z. n We will devise a way to define the size and shape of the boundary space X'.

[0067] In this way, in the state space, for each perturbed output state x, the parametric deviation r multiplied by the parameters of the gain matrix K maintains the actual trajectory of the gyroscope structure 2 within the boundary space X' defined for each required state, and the non-perturbed required states z0-z n The development of this process leads to a predetermined final required state z n It converges to the required non-perturbative states z0-z. n It is assumed that the boundary space X' surrounding contains all possible causes of the disturbance or perturbation w.

[0068] According to one embodiment, the tube gain matrix K is determined offline by using linear matrix inequality theory. In one embodiment, matrices A and B used in the classical representation of the linear system in state space discretization are the cost function J, as detailed in the following chapter. T It is considered together with the weight matrices Q, R, and P used within it.

[0069] In this way, the second part v of the drive signal u * This is determined in order to maintain the expansion of the perturbed state within the tube defined by the boundary space X', with respect to convergence to a non-perturbed state.

[0070] According to one embodiment shown in Figure 3, the method determines the first signal portion v through an implementation configuration of connected prediction blocks 13 in which nominal units 14 and prediction units 15 are arranged in a cascade. In the nominal unit 14, the non-perturbed nominal model of the gyroscope structure 2 uses feedback between the perturbed output state x and the first signal portion v to determine the non-perturbed nominal state z NP Used to generate [something].

[0071] In the prediction unit 15, the predictive dynamic control model of the gyroscope structure 2 takes the unperturbed nominal state z as input. NP Driven by and generating the first portion of signal v,

[0072] The predictive dynamic control model includes a cost function J that contains quadratic terms relating to the state and driving operation of the gyroscope structure 2. T By devising this, and also considering the non-quadratic term J relating to the instantaneous force absorbed by the gyroscope structure 2, T Includes.

[0073] The method is drive signal JPEG0007882888000006.jpg864 is a cost function J TThe computation is designed to be used according to the optimization problem determined to minimize the value.

[0074] JPEG0007882888000007.jpg21166

[0075] Furthermore, the method involves the non-perturbed nominal state z generated by the nominal unit 14. NP Using this, we calculate the parametric deviation r and the signal v * We will devise a way to define the second part.

[0076] In one alternative embodiment shown in the more general embodiment of Figure 5, the method is extended state z a By providing this as input to the prediction unit 15, the drive signal u is designed to be generated as an extended drive signal. Extended state z a is a non-perturbative nominal state z NP , and the operating variable of output state x and the non-perturbed output nominal state z of gyroscope structure 2 NP This includes a parametric deviation r calculated as the difference between the operating variable and the operating variable.

[0077] Extended state z a Block 15 is a vector that allows for the modeling and prediction of the disturbance w trend, while enhancing the prediction of the gyro unit's state.

[0078] The drive signal obtained in this way JPEG0007882888000008.jpg864 is more accurate, and the control of the electrical converter 9 is more efficient.

[0079] The method designed in this way achieved pre-defined goals and objectives by enabling the generation of so-called robust drive signals u with respect to internal and external perturbations of the gyroscope structure of the WEC system.

[0080] Furthermore, the corrections generated by the controller and obtained by tube convergence, as implemented according to the present invention, enable the gyroscope structure to be returned to an operating state close to the required state, even in the presence of uncertainties in the parameters of the nominal models of the nominal units and prediction blocks, or in the presence of disturbances that have not been previously considered and / or modeled.

[0081] (Cost function) Considering the state of the simplified model shown above, the cost function J related to the predictive dynamic control model and the predictive unit 15 is as follows: T This is detailed according to the formula.

number

[0082] Cost function J T This includes a quadratic term relating to the state and control or driving operation of the gyroscope structure 2, and a non-quadratic term relating to instantaneous absorption force. Due to the instantaneous force term, which is a mixed term by definition, the cost function J T It is not convex, and its minimization is determined by the determination of the drive signal v.

[0083] The power extracted in the kth step and the energy z of the state in the kth step.K And the control variable v Ek The energy is calculated by summing all of them together over a time interval T that includes N steps.

[0084] The contribution of each term is adjusted in the calculation of the total cost function by the matrix Q associated with the state and the matrix R associated with the control variables. As the weighting coefficient increases, the energy of the associated term is reduced.

[0085] According to one embodiment, the matrix P is calculated together with the gain matrix K according to linear matrix inequalities or LMI theory.

Claims

1. A controller (10) for a gyroscope structure (2) associated with a floating body (3) and comprising an electrical converter (9) suitable for converting the rotational energy of the floating body (3) into electrical energy, wherein the controller (10) receives a perturbation output state (x) including the operating variables of the gyroscope structure (2) as input, The determination of the drive signal (u) for driving the electrical converter (9), wherein the drive signal (u) is A first signal portion (v) is determined using a predictive control model of the gyroscope structure (2) calculated based on the perturbation output state (x), A second signal portion (v) is determined using the tube convergence calculated on the parametric deviation (r) of the operating variable of the gyroscope structure (2). ＊ ) and the parametric deviation (r) is the non-perturbed output nominal state (z ＮＰ The second signal portion (v) is calculated as the nominal difference of the operating variables of the perturbation output state (x) with respect to the above perturbation output state (x). ＊ )and The characteristic of including the above determination, Controller (10).

2. The required non-perturbative state (z) of the gyroscope structure (2) ０ -z Ｎ A nominal convergence module (18) comprising a gain matrix (K) suitable for defining the convergence of the parametric deviation (r) to a predetermined value, taking into account the time evolution of the parametric deviation (r), wherein the gain matrix (K) comprises each required non-perturbed state (z ０ -z Ｎ The controller according to claim 1, characterized in that it includes the nominal convergence module (18) defined with respect to the boundary space (X') of ).

3. A prediction block (13) comprising cascaded nominal units (14) and prediction units (15), wherein the nominal unit (14) includes a non-perturbed nominal model of the gyroscope structure (2), The nominal unit (14) receives the perturbed output state (x) as input, and receives the first signal portion (v) generated by the prediction unit (15) as feedback, and the non-perturbed output nominal state (z ＮＰ ) generates, The prediction unit (15) includes a predictive control model that receives the non-perturbed output nominal state (z ＮＰ ) as an input, or the prediction unit (15) includes the prediction block (13) that receives the non-perturbed output nominal state (z ＮＰ ) and further the parametric deviation (r) as the input to generate the first signal portion (v). The controller according to claim 1, characterized in that.

4. The nominal convergence module (18) generates the non-perturbed output nominal state (z) generated by the nominal unit (14). ＮＰ The controller according to claim 3, characterized in that it receives ) as input.

5. The predictive control model of the prediction unit (15) has a cost function (J) which includes a quadratic term relating to the state of the gyroscope structure (2) and the drive signal, and further includes a non-quadratic term relating to the instantaneous force absorbed by the gyroscope structure (2). Ｔ ) including, The aforementioned cost function (J Ｔ The calculation to minimize the drive signal sequence The determination is made that the first signal portion (v) is the drive signal sequence at least one element (v ｉ The determination is made by i = 0. . T) and The controller according to claim 1, characterized by the following:

6. A method for controlling an electrical converter (9) of a gyroscope structure (2) associated with a floating buoyancy body (3), wherein the electrical converter (9) is configured to convert the rotational energy of the floating buoyancy body (3) into electrical energy, and the method provides receiving a perturbation output state (x) including the operating variables of the gyroscope structure (2), in the method, - First signal portion (v) and second signal portion (v) ＊ The electric converter (9) is driven by a drive signal (u) including the following: - Determining the first signal portion (v) using a predictive control model of the gyroscope structure (2) calculated based on the perturbation output state (x), - The second signal portion (v ＊ The determination of the state using tube convergence calculated on the parametric deviation (r) of the operating variable of the perturbed output state (x), wherein the parametric deviation (r) is the non-perturbed output nominal state (z) of the gyroscope structure (2). ＮＰ The determination is calculated based on the operation of ) A method characterized by the following.

7. - The predetermined final required state (z ｎ In order to converge to ), the nominal model of the gyroscope structure (2) determines the required non-perturbed state (z) of the gyroscope structure (2). ０ -z ｎ The definition of the development of the state, wherein the development of the state is determined in a time interval (T) having N subsequent steps, - Each perturbation output state (x ０ -x Ｎ Regarding ), the corresponding required non-perturbative state (z ０ -z ｎ This defines a parametric deviation (r) compared to ) and - The perturbation output state (x ０ -x ｎ Multiplying the parametric deviation (r) by the parameters of the gain matrix (K) so as to maintain the actual trajectory defined by the expansion of ) within a single boundary space (X'), wherein the boundary space is the required non-perturbed state (z ０ -z ｎ The multiplication that is predetermined by the surroundings of ) The method according to claim 6, characterized by the above.

8. - The non-perturbed output nominal state (z) starting from the perturbed output state (x) of the gyroscope structure (2) and the feedback of the first signal portion (v) ＮＰ To generate the above, a non-perturbative nominal model of the gyroscope structure (2) is used, - In order to generate the first signal portion (v), the non-perturbed output nominal state (z) received as input ＮＰ Based on the above, the predictive control model of the gyroscope structure (2) is used, or the non-perturbed output nominal state (z) received as input is used to generate the first signal portion (v). ＮＰ ) and further, based on the parametric deviation (r), a predictive control model of the gyroscope structure (2) is used. The method according to claim 6, characterized by the above.

9. The predictive control model has a cost function (J) which includes a quadratic term related to the state of the gyroscope structure (2) and the drive signal (u), and a non-quadratic term related to the instantaneous force absorbed by the gyroscope structure (2). Ｔ ) by devising a method - Drive signal sequence The cost function (J) that determines this Ｔ The computation is performed according to an optimization problem that minimizes ) and - The drive signal sequence at least one of the elements (v ｉ i = 0 Based on , . . . ,T) the first signal portion (v) is determined and The method according to claim 6, characterized by the above.

10. - By receiving the perturbed output state (x) as input and the first signal portion (v) as feedback, an ideal non-perturbed output state (z) is obtained. ＮＰ To provide a nominal unit (14) suitable for defining ) and - The ideal non-perturbed output state (z) generated by the nominal unit (14) ＮＰ By using ), the parametric deviation (r) is calculated and The method according to claim 6, characterized by the above.

11. - Floating float (3), - A gyroscope structure (2) associated with the floating body (3) and equipped with an electrical converter (9) suitable for converting the rotational energy of the floating body (3) into electrical energy, - A controller (10) that receives a perturbation output state (x) including the operating variables of at least one gyroscope structure (2) as input, characterized in that it is configured according to any one of claims 1 to 5, and The WEC system, including the WEC system.

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