A controller (15) which receives a target value (g*) related to a piston (3) of the hydraulic cylinder unit (1) and an actual value (g) related to the piston (3) of the hydraulic cylinder unit (1). On the basis of the difference (?g) of the values, the controller determines a provisional manipulated variable (u). A linearization unit (17) downstream of the controller (15) multiplies the provisional manipulated variable (u) by a linearization factor (F) and outputs the product to a valve control unit (7) as a final manipulated variable (u) such that the actual value (g) is brought toward the target (g*) at an adjustment speed. On the basis of working pressures (pA, pB) on both sides of the piston (3) and/or working pressures (pP, pT) on the feed side and on the outflow side of the valve control unit (7) and a target piston force (FKL) to be applied by the piston (3), the linearization unit (17) determines target values (pA*, pB*) for the working pressures (pA, pB). The linearization unit determines the linearization factor (F) dynamically as a function of an actual position(s) of the piston (3), the target values (pA*, pB*), and the working pressures (pP, pT) on the feed side and on the outflow side of the valve control unit (7).

A controller (15) which receives a target value (g*) related to a piston (3) of the hydraulic cylinder unit (1) and an actual value (g) related to the piston (3) of the hydraulic cylinder unit (1). On the basis of the difference (?g) of the values, the controller determines a provisional manipulated variable (u). A linearization unit (17) downstream of the controller (15) multiplies the provisional manipulated variable (u) by a linearization factor (F) and outputs the product to a valve control unit (7) as a final manipulated variable (u) such that the actual value (g) is brought toward the target (g*) at an adjustment speed. On the basis of working pressures (pA, pB) on both sides of the piston (3) and/or working pressures (pP, pT) on the feed side and on the outflow side of the valve control unit (7) and a target piston force (FKL) to be applied by the piston (3), the linearization unit (17) determines target values (pA*, pB*) for the working pressures (pA, pB). The linearization unit determines the linearization factor (F) dynamically as a function of an actual position(s) of the piston (3), the target values (pA*, pB*), and the working pressures (pP, pT) on the feed side and on the outflow side of the valve control unit (7).

In order to control motions of a first movable part and a second movable part in which the first movable part is mounted, the control device includes: a first measuring unit configured to measure the motion of the first movable part; a first compensation unit configured to generate a first amount of operation based on an output of the first measuring unit to control the motion of the first movable part; a second compensation unit configured to generate a second amount of operation based on the output of the first measuring unit to control the motion of the first movable part; a first computing unit configured to generate an amount of operation for driving the first movable part based on an output of the first compensation unit and an output of the second compensation unit; a second measuring unit configured to measure the motion of the second movable part; a third compensation unit configured to generate a third amount of operation based on an output of the second measuring unit to control the motion of the second movable part; and a control unit configured to determine parameter values for generating the second and third amounts of operation in the second and third compensation units using machine learning by starting the machine learning at different timings.

In order to control motions of a first movable part and a second movable part in which the first movable part is mounted, the control device includes: a first measuring unit configured to measure the motion of the first movable part; a first compensation unit configured to generate a first amount of operation based on an output of the first measuring unit to control the motion of the first movable part; a second compensation unit configured to generate a second amount of operation based on the output of the first measuring unit to control the motion of the first movable part; a first computing unit configured to generate an amount of operation for driving the first movable part based on an output of the first compensation unit and an output of the second compensation unit; a second measuring unit configured to measure the motion of the second movable part; a third compensation unit configured to generate a third amount of operation based on an output of the second measuring unit to control the motion of the second movable part; and a control unit configured to determine parameter values for generating the second and third amounts of operation in the second and third compensation units using machine learning by starting the machine learning at different timings.

Multi-resonant control of a 3 degree-of-freedom (heave-pitch-surge) wave energy converter enables energy capture that can be in the order of three times the energy capture of a heave-only wave energy converter. The invention uses a time domain feedback control strategy that is optimal based on the criteria of complex conjugate control. The multi-resonant control can also be used to shift the harvested energy from one of the coupled modes to another, enabling the elimination of one of the actuators otherwise required in a 3 degree-of-freedom wave energy converter. This feedback control strategy does not require wave prediction; it only requires the measurement of the buoy position and velocity.

Multi-resonant control of a 3 degree-of-freedom (heave-pitch-surge) wave energy converter enables energy capture that can be in the order of three times the energy capture of a heave-only wave energy converter. The invention uses a time domain feedback control strategy that is optimal based on the criteria of complex conjugate control. The multi-resonant control can also be used to shift the harvested energy from one of the coupled modes to another, enabling the elimination of one of the actuators otherwise required in a 3 degree-of-freedom wave energy converter. This feedback control strategy does not require wave prediction; it only requires the measurement of the buoy position and velocity.

A multi-resonant wide band controller decomposes the wave energy converter control problem into sub-problems; an independent single-frequency controller is used for each sub-problem. Thus, each sub-problem controller can be optimized independently. The feedback control enables actual time-domain realization of multi-frequency complex conjugate control. The feedback strategy requires only measurements of the buoy position and velocity. No knowledge of excitation force, wave measurements, nor wave prediction is needed. As an example, the feedback signal processing can be carried out using Fast Fourier Transform with Hanning windows and optimization of amplitudes and phases. Given that the output signal is decomposed into individual frequencies, the implementation of the control is very simple, yet generates energy similar to the complex conjugate control.

A multi-resonant wide band controller decomposes the wave energy converter control problem into sub-problems; an independent single-frequency controller is used for each sub-problem. Thus, each sub-problem controller can be optimized independently. The feedback control enables actual time-domain realization of multi-frequency complex conjugate control. The feedback strategy requires only measurements of the buoy position and velocity. No knowledge of excitation force, wave measurements, nor wave prediction is needed. As an example, the feedback signal processing can be carried out using Fast Fourier Transform with Hanning windows and optimization of amplitudes and phases. Given that the output signal is decomposed into individual frequencies, the implementation of the control is very simple, yet generates energy similar to the complex conjugate control.

A parameter tuning method of an unknown proportional-integral-derivative (PID) controller is provided. The unknown PID controller is replaced with the control algorithm of the generic controller to perform the optimal parameter tuning to obtain the target parameter of the generic controller. The unknown PID controller is set with a first parameter group so that the corresponding input signal, the control signal and the output signal are measured for performing the parameter identification procedure of the generic controller to obtain a second parameter group of the generic controller. When the second parameter group is not within a specification range of the target parameter, the first parameter is re-calculated and modified by a direct search method in accordance with the difference between the second parameter group and the target parameter for setting the unknown PID controller again, and then the input signal, the control signal and the output signal are measured again.

A control device controls a hydraulic cylinder unit having a piston. The device receives a setpoint variable and an actual variable and determines, based on a difference between the setpoint variable and the actual variable, a preliminary manipulated variable for valves of the hydraulic cylinder unit. The setpoint variable and the actual variable relate to a position of the piston or a force applied by the piston. Linearization factors are determined dynamically as a function of the actual position of the piston and working pressures on both sides of the piston and a hydraulic fluid tank/pump. Definitive manipulated variables to control the valves are determined from the preliminary manipulated variable and the linearization factors. With the linearization factors, a ratio of the piston adjustment speed to the difference between the setpoint variable and the actual variable is independent of the actual position of the piston and the working pressures.