33 results about "Nonlinear adaptive control" patented technology

Nonlinear adaptive welders and power source controls are provided along with welding methods method for regulating a welding process, in which one or more welding waveform parameters are adapted in nonlinear fashion based on measured welding process feedback information.

The invention discloses a nonlinear self-adaption control method of near-space hypersonic vehicle (NHV), which belongs to a flight control method in the technical field of aerospace. The control method mainly comprises three control law parts: a nominal nonlinear generalized predictive control law (NGPC), a B-spline recursive functional linkage network (BRFLN) self-adaption control law, and a robust control law of gain self-adaption adjustment. The invention integrates the simplicity of the NGPC method and the effectiveness of dynamic uncertainty of the BRFLN learning, targets the immeasurable dynamic uncertainty and fast interference of an attitude system in the flight of the NHV, brings favorable learning effect, and realizes the nonlinear accurate control to the attitude angle.

Nonlinear adaptive resource management systems and methods are provided. According to one embodiment, a controller identifies and prevents resource starvation in resource-limited systems. To function correctly, system processes require resources that can be exhausted when under high load conditions. If the load conditions continue a complete system failure may occur. Controllers functioning in accordance with embodiments of the present invention avoid these failures by distribution shaping that completely avoids undesirable states. According to one embodiment, a Markov Birth / Death Chain model of the resource usage is built based on the structure of the system, with the number of states determined by the amount of resources, and the transition probabilities by the instantaneous rates of observed consumption and release. A control stage is used to guide a controller that denies some resource requests in real systems in a principled manner, thereby reducing the demand rate and the resulting distribution of resource states.

The invention provides a steering engine saturation resistant self-adaptive control method for ship courses. The method comprises performing a mathematical description on ship course motion, and constructing a dynamic saturation-resistant compensator, a first state vector z1 of a self-adaptive backstepping controller and a Lyapunov function of the z1 to obtain virtual control; calculating a second state vector z2 of the self-adaptive backstepping controller through subtraction through combining ship course angle information, the course angular speed and the compensation state which is output by a dynamic saturation-resistant compensator module, constructing an overall Lyapunov function in the control method, and obtaining a ship course non-linear self-adaptive controller provided with the saturation-resistant compensator through combining ship course stability conditions to complete the ship course non-linear self-adaptive control method provided with the saturation-resistant compensator. The self-adaptive control method is explicit in thinking, clear and reasonable in structure and prone to engineering implementation.

The invention relates to a flight control method of an unmanned helicopter and provides a posture nonlinear self-adaptive control method of a quad-rotor unmanned helicopter. Progressive tracking control over the flight posture of the helicopter can be effectively achieved under the condition that system parameters like the moment of inertia of the helicopter, the air damping coefficient and mass center offset are unknown. According to the adopted technical scheme, the method comprises the steps that a small quad-rotor unmanned helicopter control design model serves as a controlled object, online estimation is carried out on unknown system parameters through an immersion-invariant set theory, a controller is constructed by utilization of an estimated value, and progressive tracking control over the flight posture of the quad-rotor unmanned helicopter is achieved. The posture nonlinear self-adaptive control method of the quad-rotor unmanned helicopter is mainly applied to flight control of the unmanned helicopter.

The invention relates to a small unmanned helicopter nonlinear control method. A small unmanned helicopter self-adaptive control method based on an immersion and invariant set method is provided, and realizes the effect that a small unmanned helicopter can maintain a control effect with stable posture under the situation of parameter uncertainty. Thus, the technical scheme adopted by the invention provides a posture nonlinear self-adaptive control method for the small unmanned helicopter, which applies the immersion and invariant set method to the control of a small unmanned helicopter posture system while the small unmanned helicopter is under the situation of parameter uncertainty. The posture nonlinear self-adaptive control method is mainly applied to small unmanned helicopter nonlinear control.

The invention discloses a variable error non-linear adaptive control method; a proper approximation error is introduced to approach a performance index function and a strategy control function, so the index function can finally realize uniform convergence. The method combines with a nerve network; because of excellent approach performance of the nerve network, and evaluate network and strategy network approximation errors can be simultaneously adjusted, so finally the performance index function can be converged into a neighborhood of an optimal evaluate function.

The invention discloses an identifying method for an inertia shaft of a full-automatic magnetic bearing system based on nonlinear self-adaption control. The method comprises the following steps that firstly, a full-automatic magnetic bearing rotor dynamics model with rotor imbalance and displacement sensor harmonic noise is established; secondarily, a nonlinear self-adaption control rule and estimation rule are provided, the system stability and astringency of estimation parameters are proved, it is guaranteed that a magnetic suspension rotor inertia shaft displacement estimation value is convergent to zero, and meanwhile the high-level harmonic component fourier coefficient of displacement sensor harmonic noise is estimated; then, a variable rotation speed strategy is adopted, the same frequency component of displacement sensor harmonic noise and recognizable degree of rotor imbalance are improved, the fourier coefficient values of two same frequency components are solved, and finallyidentifying for the inertia shaft of the full-automatic magnetic bearing system is achieved. According to the identifying method for the inertia shaft of the full-automatic magnetic bearing system based on nonlinear self-adaption control, speed raising or speed reduction for one time is only needed, and identifying of the magnetic bearing inertia shaft can be achieved online, so that a magnetic suspension rotor rotates around the inertia shaft.

The invention provides a tracing optimization control method for a wind turbine. According to the method, through calculating an estimated value of the air power torque of a wind wheel and a measuring value of the rotating speed of the wind wheel or a power generator, a non-linear self-adaptive control algorithm is designed; the controller linear part is subjected to optimization design; and the feasibility of the controller optimization is theoretically demonstrated.

The invention relates to a magnetic bearing system inertia axis identification method based on a nonlinear self-adaptive algorithm. Firstly, a magnetic bearing system rotor dynamics model including unbalance and Sensor Runout is established; secondly, a nonlinear self-adaptive control law and an estimation law are provided, and Fourier coefficients of Sensor Runout higher harmonic components can be also estimated while the estimated value of the inertia center displacement of a magnetic suspension rotor tends to zero; then, identification of Sensor Runout synchronous components and the unbalance amount of the rotor is achieved by changing the rotation speed strategy of the rotor and increasing the observability degree of a system, namely the identification of inertia axis is achieved; finally, the Fourier coefficients of the synchronous components in a self-adaptive algorithm are corrected, multi-harmonic current is accurately inhibited, displacement stiffness is compensated, and multi-harmonic vibration control of a magnetic-suspension inertia executing mechanism is achieved.

The invention relates to a non-linear self-adaptive controller used for a two-freedom-degree planar parallel robot. A control Lyapunov method is used for achieving task space self-adaptive control of the parallel robot. Due to the fact that the characteristics of parameters in an inertial matrix are fully considered, changes of undetermined parameter linearity of a model are not adopted any more in the design, and designing of a control law and a self-adaptive law becomes simpler because part of the parameters do not need to be estimated. According to the method, global asymptotic stability of the terminal position and speed of a system can be guaranteed.

The invention discloses a nonlinear adaptive control method of a variable error, which approximates a performance index function and a strategy control function by introducing an appropriate approximate error, and enables the index function to finally converge consistently. According to the method, a neural network is combined, and due to the good approximation performance of the neural network, the final performance index function can be converged to one neighborhood of the optimal evaluation function by adjusting the approximation errors of am evaluation network and a strategy network at thesame time.

The invention relates to a multi-model adaptive control method and system of a nonlinear system with growth rate boundedness. The multi-model adaptive control method includes identifying the system by at least one linear model and at least one nonlinear model based on the neural network, wherein the linear models and the nonlinear models correspond to a linear adaptive controller and a nonlinear adaptive controller based on the neural network respectively; switching the optimal controllers to control at each sampling moment on the basis of a performance index switching unit. Compared with a conventional nonlinear multi-model adaptive control method, the limit of nonlinear terms of the nonlinear system is relaxed to the growth rate boundedness, and adaptability of the multi-model adaptive controller is effectively improved.

Nonlinear adaptive welders and power source controls are provided along with welding methods method for regulating a welding process, in which one or more welding waveform parameters are adapted in nonlinear fashion based on measured welding process feedback information.

The invention discloses a nonlinear adaptive alternating current servo motor angular position control method and system. The method comprises the following steps: acquiring original motor rotor angular position data; inputting the original motor rotor angular position data to a motor rotor angular position control module; performing second-order time differential on the motor rotor position data to generate motor rotor angular acceleration data; inputting the motor rotor angular acceleration data and the output of a motor rotor angular position control module into an incremental inverse dynamics control module at the same time; and inputting the output of the incremental inverse dynamics control module to the motor current control module. According to the method, a motor model and a motor load model required by controller parameter setting are not needed, and the technical problems of difficulty in motor model and load model identification and controller time-varying parameter setting in the existing servo motor control technology are solved. Under the condition that the problems cannot be effectively solved in the prior art, the nonlinear adaptive control method which is simple, effective and high in performance is provided.

The invention discloses a nonlinear self-adaption control method of near-space hypersonic vehicle (NHV), which belongs to a flight control method in the technical field of aerospace. The control method mainly comprises three control law parts: a nominal nonlinear generalized predictive control law (NGPC), a B-spline recursive functional linkage network (BRFLN) self-adaption control law, and a robust control law of gain self-adaption adjustment. The invention integrates the simplicity of the NGPC method and the effectiveness of dynamic uncertainty of the BRFLN learning, targets the immeasurable dynamic uncertainty and fast interference of an attitude system in the flight of the NHV, brings favorable learning effect, and realizes the nonlinear accurate control to the attitude angle.

The invention relates to a multi-model adaptive control method and a multi-model adaptive control system of a spherical tank system. The method comprises the steps as follows: identifying a spherical tank liquid level control system through at least one linear model and at least one nonlinear model based on a neural network, wherein the two models correspond to a linear adaptive controller and a nonlinear adaptive controller based on the neural network respectively; and switching to the optimal controller to implement control at each sampling moment on the basis of a performance index switching unit. Compared with a conventional nonlinear multi-model adaptive control method, the limit of nonlinear terms of the spherical tank liquid level control system is relaxed to bounded growth rate, and the adaptability of the multi-model adaptive controller can be effectively improved.

The invention provides a method for controlling the angular velocity of a permanent magnet synchronous motor using self-adaptation and direct-feed compensation, and relates to the technical field of motor design and control. The method includes the following steps: performing Clarke transformation on the stator current to obtain the stator current in the two-phase stationary coordinate system and Parker transform the stator current in the two-phase stationary coordinate system and the rotor position to obtain the stator current in the two-phase rotating coordinate system; design the second one according to the first current error signal and the first nonlinear adaptive control law Stator voltage; design the expected value of the second stator current according to the angular velocity error signal; design the first stator voltage according to the second current error signal and the preset second nonlinear adaptive control law; use the third stator voltage and the fourth stator voltage The voltage is output to the permanent magnet synchronous motor through the three-phase inverter to complete the control of the angular velocity of the permanent magnet synchronous motor. The invention improves the accuracy of controlling the angular velocity of the permanent magnet synchronous motor.

The invention discloses a nonlinear adaptive control method for a dual UAV hoisting system based on load generalized displacement, which belongs to the field of electromechanical system control. The method includes: according to the two-dimensional plane model of the system, a novel nonlinear adaptive control method is proposed without linearization and other simplified operations. In this method, while considering the air resistance of the aircraft, by introducing the state coupling of the load swing angle, on the basis of ensuring the completion of the positioning task of the aircraft, the suppression of the load swing is accelerated. The experimental results show that the proposed scheme has good performance in suppressing the load swing and quickly completing the positioning task of the aircraft.

A non-linear adaptive control method with variable error, which approximates the performance index function and the policy control function by introducing a suitable approximation error, and makes the index function converge eventually. The present invention combines the neural network. Due to the good approximation performance of the neural network, the final performance index function can converge to a neighborhood of the optimal evaluation function by simultaneously adjusting the approximation errors of the evaluation network and the strategy network.

The invention relates to a magnetic bearing system inertia axis identification method based on a nonlinear self-adaptive algorithm. Firstly, a magnetic bearing system rotor dynamics model including unbalance and Sensor Runout is established; secondly, a nonlinear self-adaptive control law and an estimation law are provided, and Fourier coefficients of Sensor Runout higher harmonic components can be also estimated while the estimated value of the inertia center displacement of a magnetic suspension rotor tends to zero; then, identification of Sensor Runout synchronous components and the unbalance amount of the rotor is achieved by changing the rotation speed strategy of the rotor and increasing the observability degree of a system, namely the identification of inertia axis is achieved; finally, the Fourier coefficients of the synchronous components in a self-adaptive algorithm are corrected, multi-harmonic current is accurately inhibited, displacement stiffness is compensated, and multi-harmonic vibration control of a magnetic-suspension inertia executing mechanism is achieved.

The invention discloses a load generalized displacement-based nonlinear self-adaptive control method for a double-unmanned aerial vehicle hoisting system, and belongs to the field of electromechanical system control. The method comprises the step of providing a novel nonlinear adaptive control method without linearization and other simplified operations according to a two-dimensional plane model of a system. According to the method, the air resistance borne by the aircraft is considered, and meanwhile, load swing angle state coupling is introduced, so that the suppression of load swing is accelerated on the basis of ensuring that the aircraft positioning task is completed. Experimental results show that the scheme has good performance in the aspects of inhibiting load swing and rapidly completing an aircraft positioning task.

The invention discloses a nonlinear self-adaptive control method and system for motion control of a mechanical arm. The method comprises the steps that the output of an incremental nonlinear inverse dynamics controller is input to a servo motor driver for driving the mechanical arm, and the like. The system is used for implementing the method. According to the method, a mechanical arm motion load model and an external disturbance model required by controller parameter setting are not needed, so that the technical problems of difficulty in motion load model identification and controller time-varying parameter setting in an existing mechanical arm motion control technology are solved. Under the actual condition that the difficulties and problems cannot be effectively solved in the prior art, the mechanical arm motion control method and system are quite simple, effective and high in performance.

The invention provides a tracing optimization control method for a wind turbine. According to the method, through calculating an estimated value of the air power torque of a wind wheel and a measuring value of the rotating speed of the wind wheel or a power generator, a non-linear self-adaptive control algorithm is designed; the controller linear part is subjected to optimization design; and the feasibility of the controller optimization is theoretically demonstrated.

The invention relates to a self-adaptive electric control vacuum chuck manipulator for label-removed waste plastic bottles and a control method. A pneumatic loop and an electric control element of the manipulator are integrated in a manipulator shell; the control method comprises the steps that negative pressure of the long corrugated pipe vacuum suction cup is transmitted back to the single-chip microcomputer, whether the voltage value of the negative pressure is larger than F or not is judged, and adsorption succeeds if the voltage value is smaller than F; if so, entering a switching mechanism for operation; the switching mechanism calculates the voltage value returned by the negative pressure sensor when the nonlinear functional is gt; during linear functional, a linear adaptive controller control method is adopted to obtain u1 (k); when the nonlinear functional is gt; the linear functional adopts a nonlinear adaptive controller control method; u2 (k) is obtained; and the u1 (k) or u2 (k) is transferred into a pressure reducing valve until the adsorption is successful. The problems that in the existing sorting process, the air tightness of a suction cup cannot be guaranteed, the sorting success rate is reduced, the sorting efficiency of an industrial robot is affected, and the service life of a mechanical arm is shortened are solved.

The invention relates to a control method of a power electronic power conversion device, in particular to an adaptive nonlinear control method suitable for a megawatt-level medium-voltage medium-frequency three-level DC converter. The control scheme mentioned in the present invention mainly completes the voltage stabilizing output function and the short-circuit current limiting control function, so as to realize the ability to adapt to the change and disturbance of the subsequent stage load and the short-circuit fault protection ability. The controller mentioned in the present invention divides the system state into CCM steady state, DCM steady state and dynamic state, and the switching between the three states is accompanied by parameter adaptive adjustment, so as to obtain relatively good dynamic and static characteristics of voltage stabilized output. The controller mentioned in the present invention can overcome the shortcomings of the traditional control method that it is difficult to take into account the dynamic performance and static performance of the voltage stabilized output, as well as the shortcomings of different working modes, and can obtain the smaller ripple in the steady state and the smaller voltage fluctuation in the disturbance to the maximum extent. Advantages of small size and faster recovery time.