阅读说明：本技术 一种分布式泵控相邻偏差耦合神经网络pid液压爬模顶升系统 (Distributed pump-controlled adjacent deviation coupling neural network PID hydraulic climbing formwork jacking system ) 是由 张树忠 李苏 唐一文 于 2020-07-01 设计创作，主要内容包括：本发明液压爬模同步顶升系统采用容积调速(泵控直驱),为了实现在高负载、较大偏载情况下的液压爬模同步顶升,采用单控制器,已经满足不了大区域的爬模机构了,因此本发明提出分布式控制策略,运用相邻偏差耦合神经网络PID控制算法来实现多液压缸的同步运动。该液压爬模同步顶升系统由液压模块与控制模块组成。(The hydraulic climbing formwork synchronous jacking system adopts volume speed regulation (pump control direct drive), adopts a single controller to realize the hydraulic climbing formwork synchronous jacking under the conditions of high load and large unbalance load, and cannot meet the requirement of a climbing formwork mechanism in a large area, so that the hydraulic climbing formwork synchronous jacking system provides a distributed control strategy, and realizes the synchronous motion of a plurality of hydraulic cylinders by using an adjacent deviation coupling neural network PID control algorithm. The hydraulic climbing formwork synchronous jacking system consists of a hydraulic module and a control module.)

1. The utility model provides an adjacent deviation coupling neural network PID hydraulic pressure creeping formwork jacking system of distributing type pump accuse which characterized in that includes hydraulic module and control module:

the hydraulic module comprises a plurality of same volume speed regulation hydraulic circuits, and each volume speed regulation hydraulic circuit is supplied with oil by a unidirectional constant delivery pump driven by a respective independent servo motor; each servo motor is controlled by a control module;

the control module comprises a main controller and a plurality of sub-controllers, and each sub-controller controls a servo motor;

each sub-controller is provided with a neural network PID module, and the main controller sends the same expected displacement signal u to each sub-controller₀The sub-controllers are based on u₀Controlling the hydraulic circuit to move;

the displacement information of the hydraulic cylinder in each hydraulic loop is measured by a displacement sensor and is transmitted to the corresponding sub-controller and the front and the rear sub-controllers thereof; the hydraulic cylinder displacement of the ith hydraulic circuit is respectively differed from the (i-1) th displacement and the (i + 1) th displacement, and the deviations are added to be used as a compensation signal e_i：

e_i＝(x_i-x_i-1)+(x_i-x_i+1)

Wherein, each hydraulic circuit in front of the first hydraulic circuit transmits the signal u of the main controller₀As x₀Transmitting the signal to the 1 st sub-controller for processing; the signal u of the master controller is also applied to the last hydraulic circuit₀As x_i+1The signal is transmitted to the loop sub-controller for processing;

each controller obtains a compensation signal e according to the calculation_iThe movement of the hydraulic circuit is regulated.

2. The distributed pump-controlled adjacent bias coupled neural network PID hydraulic climbing formwork jacking system of claim 1, wherein the hydraulic module comprises 8 identical volume adjustable speed hydraulic circuits.

3. The distributed pump-controlled adjacent deviation coupled neural network PID hydraulic climbing formwork jacking system according to claim 1, wherein a main controller of the control module is connected with each sub-controller and each sub-controller in a wireless transmission mode.

Technical Field

The invention relates to the field of automatic control, in particular to a distributed pump-controlled adjacent deviation coupling neural network PID hydraulic climbing formwork jacking system.

Background

Nowadays, with the rapid increase of the population in the market, in order to meet the living needs of residents, a large number of high-rise and super high-rise buildings and large structures need to be built, so that how to apply a safe and efficient construction technology to the construction process becomes an urgent problem for many construction enterprises. The traditional template construction has the defects of difficult tower construction, complex procedures, poor safety protection, high cost, low efficiency and the like, and has the advantages of convenient operation, high safety, material saving, short construction period and the like in the construction of high-rise buildings for the hydraulic climbing formwork technology, so that the hydraulic climbing formwork is more and more widely applied to the high-rise buildings. At present, the study of the hydraulic creeping formwork by scholars at home and abroad is mainly focused on the aspects of engineering practical application and construction, which causes a plurality of defects in the aspect of a hydraulic creeping formwork synchronous system, and the conventional control strategies such as master-slave synchronization, parallel synchronization and the like are mostly adopted in the conventional hydraulic creeping formwork synchronous control system, so that the system has the defects of easy generation of time delay, poor anti-jamming capability and the like. The requirements of high precision, low pollution and automatic control on the hydraulic creeping formwork in high-rise buildings cannot be met. Therefore, the invention provides a distributed pump control adjacent deviation coupling neural network PID hydraulic climbing formwork jacking method, which is used for solving the defects in the traditional hydraulic climbing formwork system.

Disclosure of Invention

The invention is realized by adopting the following technical scheme:

the utility model provides an adjacent deviation coupling neural network PID hydraulic pressure creeping formwork jacking system of distributing type pump control, includes hydraulic module and control module:

the hydraulic module comprises a plurality of same volume speed regulation hydraulic circuits, and each volume speed regulation hydraulic circuit is supplied with oil by a unidirectional constant delivery pump driven by a respective independent servo motor; each servo motor is controlled by a control module;

the control module comprises a main controller and a plurality of sub-controllers, and each sub-controller controls a servo motor;

each sub-controller is provided with a nerveA network PID module, wherein the main controller sends the same expected displacement signal u to each sub-controller₀The sub-controllers are based on u₀Controlling the hydraulic circuit to move;

the displacement information of the hydraulic cylinder in each hydraulic loop is measured by a displacement sensor and is transmitted to the corresponding sub-controller and the front and the rear sub-controllers thereof; the hydraulic cylinder displacement of the ith hydraulic circuit is respectively differed from the (i-1) th displacement and the (i + 1) th displacement, and the deviations are added to be used as a compensation signal e_i：

e_i＝(x_i-x_i-1)+(x_i-x_i+1)

Wherein, each hydraulic circuit in front of the first hydraulic circuit transmits the signal u of the main controller₀As x₀Transmitting the signal to the 1 st sub-controller for processing; the signal u of the master controller is also applied to the last hydraulic circuit₀As x_i+1The signal is transmitted to the loop sub-controller for processing;

each controller obtains a compensation signal e according to the calculation_iThe movement of the hydraulic circuit is regulated.

Further, the hydraulic module comprises 8 same volume speed regulation hydraulic circuits.

Furthermore, compared with the prior art, the wireless transmission mode connection is adopted between the main controller of the control module and each sub-controller and between each sub-controller, the invention has the following advantages:

1. the volume speed regulation is adopted, the efficiency is high, and the pollution is less.

2. And the synchronous precision of the pump control system is improved by adopting an adjacent deviation coupling neural network PID control algorithm.

3. And a distributed control strategy and wireless transmission are adopted, so that the anti-interference capability of the system is improved, and the reliability is high.

Description of the drawings:

fig. 1 is a schematic diagram of a hydraulic climbing formwork synchronous jacking system.

FIG. 2 displacement compensator

FIG. 3 Adjacent bias coupling control strategy

FIG. 4 neural network PID control

Detailed description of the preferred embodiments

The hydraulic climbing formwork synchronous jacking system adopts volume speed regulation (pump control direct drive), adopts a single controller to realize the hydraulic climbing formwork synchronous jacking under the conditions of high load and large unbalance load, and cannot meet the requirement of a climbing formwork mechanism in a large area, so that the hydraulic climbing formwork synchronous jacking system provides a distributed control strategy, and realizes the synchronous motion of a plurality of hydraulic cylinders by using an adjacent deviation coupling neural network PID control algorithm. The hydraulic climbing formwork synchronous jacking system consists of a hydraulic module and a control module, and a schematic diagram of the system is shown in figure 1.

A hydraulic module: the hydraulic module of the hydraulic climbing formwork synchronous jacking system consists of eight same volume speed regulating hydraulic loops, wherein the volume speed regulating hydraulic loops are formed by driving a one-way constant delivery pump by a servo motor to supply oil to a hydraulic system, and the rotating speed of the pump is controlled by changing the rotating speed of the servo motor through a frequency converter, so that the discharge capacity of the constant delivery pump is controlled, and the displacement of a hydraulic cylinder is controlled; the overflow valve is used for overflow pressure stabilization, the three-position four-way valve is used for hydraulic cylinder reversing, a displacement sensor is installed at a port of the hydraulic cylinder, and the displacement of the hydraulic cylinder is accurately fed back to the sub-controllers of each hydraulic loop in real time.

A controller module: the controller module consists of a main controller and eight sub-controllers which are respectively used for controlling the single volume speed regulating loop. The information transmission between the main controller and the sub-controllers is carried by adopting a wireless network, and the wireless transmission technology has excellent anti-interference performance and high reliability, is beneficial to simplifying the structure, and can also reduce the defect that the external lines are damaged due to too many and too long connections. Feedback signals of displacement sensors in the hydraulic loops are also transmitted to the sub-controllers of the loops by adopting a wireless network. The electromagnetic directional valve adopted in the invention also adopts the electromagnetic directional valve capable of receiving wireless signals, and the position function of the directional valve is controlled by receiving the signals of the sub-controllers.

The main controller sends the same target displacement u to each sub-controller through a wireless network₀Frequency modulation signals are converted into frequency modulation signals through each sub-controller and transmitted to the frequency converter of each hydraulic loop, the rotating speed of the motor is controlled through the frequency converter,the fixed displacement pump and the motor rotate at the same rotating speed, and the output flow of the fixed displacement pump and the displacement of the hydraulic cylinder can be controlled by adjusting the rotating speed of the motor.

Each sub-controller processes the feedback and control signals by using an adjacent deviation coupling neural network PID algorithm, and a control signal flow chart is shown in FIG. 3. Each sub-controller receives the same expected displacement signal u from the main controller₀The displacement information of the hydraulic cylinder in each hydraulic circuit is transmitted to the corresponding controller through the displacement sensor, and is differed with the displacement feedback signals of the two adjacent front and back circuits of the circuit, and the obtained deviations are added to be used as compensation signals, as shown in fig. 2.

Taking the ith hydraulic circuits as examples, the hydraulic cylinder displacement of the ith hydraulic circuit is respectively differed between the (i-1) th displacement and the (i + 1) th displacement, and the differences are added to be used as compensation signals e_iIn which each hydraulic circuit precedes the first, so we will signal u from the master controller₀As x₀The signal is transmitted to a 1 st displacement compensator for processing; similarly, the signal u of the master controller is also transmitted in the last hydraulic circuit₀As x_i+1The signal is transmitted to the loop displacement compensator for operation. The final compensated signal obtained by each controller is:

e_i＝(x_i-x_i-1)+(x_i-x_i+1)

the neural network PID controller is added on each sub-controller and consists of a traditional PID controller and a neural network algorithm, the neural network PID controller is shown in figure 4, the controller can adjust the parameters of the PID controller according to the motion condition of the creeping formwork system to achieve the optimal synchronization performance, and the output state of the neuron of the output layer corresponds to three parameters k of the PID controller_P、k_I、k_DAnd the stable state transition of the PID controller is corresponding to the PID controller parameters under a certain optimal control rule through self learning and weighting coefficient adjustment of the neural network.

The method combines the adjacent deviation coupling control strategy with the neural network PID control algorithm, thereby eliminating the defects of poor anti-interference capability and obvious delay of adjacent coupling, solving the problem that the deviation coupling needs a complex algorithm under the condition of multi-hydraulic cylinder synchronization, and improving the automation degree of the system by applying the neural network PID control algorithm so as to optimize the state of the system.