This paper presents a novel multiloop and Multi-objective cooperative intelligent control system (MMCICS) used to improve the performance of position, velocity and acceleration integrated control on a complex multichannel plant. Based on regulation mechanism of the neuroendocrine system (NES), a bioinspired motion control approach has been used in the MMCICS which includes four cooperative units. The planning unit outputs the desired signals. The selection unit chooses the real-time dominant control mode. The coordination unit uses the velocity Jacobian matrix to regulate the cooperative control signals. The execution unit achieves the ultimate task based on sub-channel controllers with the proposed hormone regulation self-adaptive Modules (HRSMs). Parameter tuning is given to facilitate the MMCICS implementation. The MMCICS is applied to an actual 2-DOF redundant parallel manipulator where the feasibility of the new control system is demonstrated. The MMCICS keeps its subchannels interacting harmoniously and systematically. Therefore, the plant has fast response, smooth velocity, accurate position, strong self-adaptability, and high stability. The HRSM improves the control performance of the local controllers and the global system as well, especially for manipulators running at high velocities and accelerations.
With the development of the high-standard manufacturing requirement, plants become more complex while controlled by several of subchannels [
Neuroendocrine system (NES) is a major homeostatic system in human body and has some outstanding multiobjective cooperative modulation mechanisms. Being a multiloop feedback mechanism, NES can still regulate the functions of several organs and glands with high self-adaptability and stability, by means of regulating their hormone secretions synchronously [
To achieve multi-objective cooperative control, some recent work concentrates on how to use multi-loop and multi-objective regulation mechanism of the NES to design some novel control structures and systems. Stear [
In this paper, a novel multi-loop and multi-objective cooperative intelligent control system (MMCICS) based on regulation mechanism of NES is proposed. Inherited from NES, the MMCICS consists of four subunits: Planning unit regulates position, velocity and acceleration signals based on ultralong loop feedback. Selection unit is a soft switcher to smoothly select dominant motion control signal based on long loop feedback. As short loop feedback, coordination unit is responsible for processing and transmitting coordination signals to several sub-channels in execution unit. The execution unit is an integrity whose sub-channels interact harmoniously and systematically based on ultra-short loop feedback. Each channel has a proposed hormone regulation self-adaptive module (HRSM) which identifies control error and regulates control parameters in real time. The control performance of the proposed MMCICS is verified by an actual 2-DOF redundant parallel manipulator. The experimental results demonstrate that, through regulation mechanism of the MMCICS, the multiobjective integrated control task can be achieved easily while the stability, accuracy, adaptability, and response rate of the plant is improved by proposed HRSM.
The main contribution of this paper lies in that it generalizes the characteristics of the NES for regulation, and then reveals the similarity between the NES and a motion control system where the coordination of position, velocity, and acceleration are implemented by the cooperation of different subchannels of the plant. Furthermore, based on the regulation characteristics of the NES, a bioinspired motion control approach is provided, it has been used in MMCICS design. According to our knowledge, this is the first time that the MMCICS based on biological NES is proposed and especially applied to an actual manipulator. The proposed approach is practical and easy to implement, which provides a new efficient method for the intelligent control of complex systems.
The remainder of this paper is arranged as follows. In Section
The NES mainly includes nervous system and endocrine system [
A typical regulation mechanism of the neuroendocrine hormone can be generalized as follows [
Hormone regulation of the NES.
The regulation characteristics of NES can be summarized as below: (1) the NES has several feedback loops and glands. Each feedback mechanism has its own function and different messages can be transferred among them so that the whole system has a multiobjective regulation mechanism of integrity. (2) Central nervous system is the foremost command center. (3) Hypothalamus is the medium between the nervous system and the endocrine system. (4) Pituitary has the ability to achieve multi-hormone coordinative control. (5) The different glands always have different hormone secretion scopes and different hormone secretion standards. But they have the similar regulation mechanism that can enhance identification and secretion precision within a certain range of stimulus [
Therefore, corresponding to the motion control system, the central nervous system, the hypothalamus, the pituitary, and glands of NES can be regarded as the planning unit, the selection unit, the coordination unit, and the execution unit, respectively. In this scenario, the planning unit receives input signal and transmits the suitable motion planning signal to the selection unit. The selection unit processes the motion planning signal and chooses the dominant motion control signal. And then, the coordination unit converts dominant motion control signal to various coordination signals according to its performance characteristic. Various sub-channels in the execution unit receive their own coordination signal from the coordination unit and accomplish homologous task. Ultimately, the whole system could be controlled through the combined action of these sub-channels.
According to the bioinspired motion control approach, a novel multi-loop and multi-objective cooperative intelligent control system (MMCICS) is proposed to achieve intelligent coordination of position, velocity, and aceleration implemented by cooperation of several subchannels of plants, as shown in Figure
The structure of MMCICS.
The planning unit is primarily responsible for receiving and processing input signals of the position Automatic braking process. The position error is defined as
Cooperative planning process. Since acceleration is hardly to be controlled directly, the
The selection unit is designed as a switcher for the real-time dominant control mode. This unit receives the actual position feedback signal via long-loop feedback mechanism while the dominant motion control signal is transmitted to the coordination unit. Velocity-velocity control mode is on when the actual position is far from desired position while velocity control signal is sent to keep smooth movement. Position-velocity control mode takes over when the actual position is close to the desired position while position control signal is send to achieve accurate position. This rule for automatic switching is described as follows [
The coordination unit is a coordinator which sends cooperative control signals to each sub-channel of the plant. Many methods and mathematic models are suitable for this unit, the velocity Jacobian matrix is chosen in this paper due to the velocity control is our foremost object. In this scenario, all the input signals and output signals are regarded as the velocity signals whether the velocity-velocity control mode or the position-velocity control mode is selected. That output signals can be calculated by
The execution unit, which includes a number of sub-channels, is the core and key unit of the MMCICS. To keep sub-channels interact harmoniously and systematically, the same control method and control structure have been applied to each channel. As shown in Figure
The structure of sub-channel.
Some advanced controllers widely used in industry can be applied as primary controller. The controller can obey PID control algorithm, fuzzy control algorithm [
The HRSM is designed to improve primary controller self-adaptive performance. The regulation algorithm of HRSM is inspired from hormone regulation mechanism which includes identification and regulation processes. Identification. In NES, the gland can enhance identification and secretion precision within the working scope. However, when the stimulate signal beyond the control scope, hormone secretion rate is at its high limit. Similarly, the control error Regulation. The hormone secretion rate in NES is always nonnegative and monotone, and its secretion regulation mechanism usually follows the Hill functions, the growth curve, and so forth [
Then primary controller parameter can be regulated by its control characteristic. In the PID control algorithm, when the control error is too big, the proportion gain
Tune the primary controller parameter. First, only take the primary controller into action, and then tune the initial control parameters Determine the high and low limited hormone identification error. According to the response characteristics of the experimental results in step (1), determine the high limited error Tune the regulation coefficients of the hormone regulator. Take the execution unit into action, according to the response characteristic and overshoot of the experimental results, tune the critical regulation coefficient Determine the switching coefficient. Take the MMCICS into action and then determine the switching coefficient
Some typical experimental results are provided in this section to explore two main experiments of proposed MMCICS. Firstly, the control results with and without HRSM are compared to find out whether HRSM yields better in subchannel experiment. Next more comprehensive experiments are performed to verify multiobject cooperative control performance of the MMCICS, and whether HRSM has better global control effect.
As shown in Figure
The 2-DOF redundant parallel manipulator.
Firstly, to verify the effectiveness of the proposed HRSM in the execution unit, we only take active joint 1 (base
Parameter set.
Initial PID | Error factors | Hormone regulation factors |
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Motor in sub-channel has different dynamic characteristics at different velocities but has similar results in the same parameter sets. Multiple experiments have the similar results, and a typical result is as shown in Figure
Performance evaluation for subchannel experiment.
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1 | 5 | 10 | 20 | 40 | 50 | |||||||
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HRSM |
PID | HRSM |
PID | HRSM |
PID | HRSM |
PID | HRSM |
PID | ||||
Lower quartile | 0.0475 | 0.1575 | 0.18 | 0.2275 | 0.25 | 0.32 | 0.3 | 0.3575 | 0.32 | 0.4275 | 0.3575 | 0.48 | |
Median | 0.06 | 0.175 | 0.2 | 0.245 | 0.265 | 0.33 | 0.32 | 0.375 | 0.33 | 0.43 | 0.38 | 0.5 | |
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Upper quartile | 0.0725 | 0.185 | 0.22 | 0.2525 | 0.285 | 0.3525 | 0.34 | 0.385 | 0.3525 | 0.4525 | 0.4 | 0.54 |
Average | 0.061 | 0.175 | 0.201 | 0.246 | 0.272 | 0.335 | 0.324 | 0.37 | 0.336 | 0.439 | 0.379 | 0.506 | |
Variance | 0.000309 | 0.000445 | 0.000369 | 0.000484 | 0.000496 | 0.000445 | 0.000384 | 0.00052 | 0.000524 | 0.000589 | 0.000609 | 0.000964 | |
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Lower quartile | 0.06 | 0.095 | 0.0375 | 0.1675 | 0.04 | 0.16 | 0.02 | 0.0575 | 0.0175 | 0.04 | 0.01 | 0.03 | |
Median | 0.08 | 0.12 | 0.04 | 0.185 | 0.055 | 0.19 | 0.04 | 0.075 | 0.02 | 0.045 | 0.02 | 0.04 | |
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Upper quartile | 0.1 | 0.14 | 0.06 | 0.225 | 0.0775 | 0.22 | 0.05 | 0.1 | 0.04 | 0.06 | 0.025 | 0.0525 |
Average | 0.079 | 0.12 | 0.049 | 0.195 | 0.11 | 0.192 | 0.038 | 0.077 | 0.024 | 0.049 | 0.021 | 0.045 | |
Variance | 0.000449 | 0.00064 | 0.000449 | 0.001165 | 0.027 | 0.000736 | 0.000176 | 0.000501 | 0.000124 | 0.000249 | 0.000109 | 0.000225 | |
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Lower quartile | 0.05 | 0.08 | 0.08 | 0.215 | 0.06 | 0.195 | 0.04 | 0.08 | 0.03 | 0.06 | 0.02 | 0.0475 | |
Median | 0.06 | 0.105 | 0.1 | 0.245 | 0.08 | 0.22 | 0.05 | 0.1 | 0.04 | 0.065 | 0.035 | 0.06 | |
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Upper quartile | 0.085 | 0.12 | 0.1275 | 0.2525 | 0.085 | 0.2575 | 0.0625 | 0.12 | 0.045 | 0.08 | 0.04 | 0.08 |
Average | 0.068 | 0.102 | 0.103 | 0.237 | 0.078 | 0.225 | 0.052 | 0.099 | 0.04 | 0.066 | 0.033 | 0.063 | |
Variance | 0.000576 | 0.000656 | 0.000921 | 0.001021 | 0.000376 | 0.001705 | 0.000256 | 0.000609 | 0.00014 | 0.000144 | 0.000181 | 0.000321 |
Contrast effect of the velocity control. (a) Low velocity control, (b) high velocity control. (c) Output control signal.
To verify the multiobject cooperative control performance of the MMCICS, the end-effector of the redundant parallel manipulator is viewed as a controlled plant, and three active joints are viewed as three subchannels. The velocity Jacobian matrix between the end-effector and three active joints is
Due to the complex mechanism structure of the parallel manipulator with actuation redundancy, it is a typical nonlinear system and difficult to get the accurate dynamic and friction model [
Multichannel control experimental results. (a)
As shown in Figures
Figures
Some compare results of the 10 time’s average absolute values are shown in Table
Performance evaluation for comprehensive experiment.
Positon | Veloctiy | ||||
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Final error | Settling time | Error | Braking overshoot | ||
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MMCICS |
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0.82 s |
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CCS |
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1.07 s |
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This work presents a bioinspired cooperative intelligent control system for position, velocity, and acceleration multi-objective integrated control of a parallel plant. The similarity between the NES and motion control system revealed, and a bio-inspired motion control approach is proposed. Under the context of such approach, the MMCICS with system structure, algorithm, and steps in parameter tuning is proposed to achieve multiobjective control. The experiments are carried out with a 2-DOF redundant parallel manipulator where the feasibility of the new control system is demonstrated. The contrast effect shows that the stability, accuracy, adaptability, response rate of the proposed MMCICS is superior to those of the conventional controllers. According to our knowledge, this is the first time that NES-based MMCICS and HRSM are proposed and used for an actual parallel manipulator. The proposed MMCICS can be implemented easily and provides a new and efficient method for multiobjective integrated control of complex multichannel systems. In future works, force and torque control will be considered to establish a more complete multi-objective control system. More rigorous and advanced algorithm and proof are required instead of the PID controller. Besides, parameter optimization, dynamics, and stability analysis can be conducted on MMCICS.
This work was supported in part by the Key Project of the National Natural Science Foundation of China (No. 61134009), the National Natural Science Foundation of China (no. 60975059), Support Research Project of National ITER Program (no.2010GB108004), Specialized Research Fund for the Doctoral Program of Higher Education from Ministry of Education of China (no. 20090075110002), Project of the Shanghai Committee of Science and Technology (Nos. 11XD1400100, 11JC1400200, 10JC1400200, and 10DZ0506500).