| During the process control of modern industry, product quality is required higher and higher, which demands control performance with more efficiency. And, more intelligent and practical control algorithms are required by more and more complex control systems at the environment of the modern involuted information. Based on some bio-regulation mechanisms of the neuroendocrine-immune system, some intelligent control algorithms are studied in this thesis.First, we investigate the development of artificial bio-intelligent technologies, including artificial neural network, artificial immune system, artificial endocrine system, and the others intelligent control technologies. And their difficulties and further developments are summarized. Furthermore, some relative physiological theories and modulation mechanisms or models of neural system, endocrine system, and immune system, are introduced briefly. That provides the bio-base for the intelligent control algorithms studied in this thesis. Then, based on some bio-regulation mechanisms of the neuroendocrine-immune system, some intelligent control algorithms including intelligent control, learning control, decoupling control, optimized control and networked control, are studied respectively.For intelligent control, a two-level controller is first presented based on the hypothalamo-pituitary-adrenal model. The two-level controller includes the master control unit and the secondary one. The master control unit can adjust dynamically setpoint of the secondary one according to the real-time control error. Consequently, the controller can eliminate control error quickly and stably. Next, an ultrashort feedback intelligent controller is presented based on the ultrashort feedback mechanism of endocrine system. The output of the conventional control unit (CCU) is first fed back to the ultralshort-feedback unit (UFU), where the output of CCU is processed according to the hormone regulation law. Then the output of UFU is added to the output of CCU. Thus a nonlinear control algorithm is built. Consequently, the control performance is improved. Finally, the control performances of both controllers are examined via simulation experiments, whose results demonstrate the control performance and adaptation of both controllers are better than that of the conventional PID controller.For learning control, a novel reinforcement learning intelligent controller (RLIC) is presented based on the primary-secondary response mechanism. The RLIC has the abilities of learning, memory, and evolution, and can learn and produce the control antibodies (CABs) automatically during the period of eliminating the control error. When the control error appears again, the RLIC can eliminate it rapidly and stably, combined with the conventional control algorithm. After the control error is eliminated, a new CAB is produced and stored. Repeating the above process, the RLIC's learning ability and response rate become stronger and stronger. Consequently, the control performance of the RLIC can be improved. Simulation results demonstrate that response ability and stability of the RLIC are better than those of the conventional PID controller, and also better than the Q-reinforcement learning control.For decoupling control, a bio-imitated decoupling controller and an inverse decoupling controller are presented respectively, based on the bi-regulation mechanism of the growth hormone (GH) in endocrine system. And the corresponding decoupling control algorithms are also provided. Both decoupling controllers can eliminate the coupling influence between different control loops via adjusting actuators harmoniously, according to their related decoupling algorithms. Compared with the others decoupling control technologies, both the decoupling controller are more practical and implemented more easily. The results of simulation indicate that the schemes of both decoupling controller can completely eliminate the coupling influence and show better control performance.For optimized control, a novel adaptive genetic algorithm (HGA) is first presented based on the regulation law of hormone in endocrine system. The convergence rate and search precision of HGA are better than that of the standard genetic algorithm (GA). Then, two optimized controllers are presented respectively according to HGA and based on the different modulation mechanism of neuroendocrine-immune system. The first one is a novel nonlinear optimized intelligent controller (NOIC) based on the modulation mechanism of neuroendocrine-immune system. Also, a method to optimize and adjust the control parameters dynamically is provided, as thus the control performance is improved. According to the presentation mechanism of immune system, the presentation unit (PU) first pretreats real-time control error dynamically, and then the antibody control unit (ACU) can regulate the number of antibody control entities (ACEs) to eliminate control error. The main control unit can regulate the control action of PU and ACU. Furthermore the optimum unit (PU) and identification unit (IU) can optimized the real-time control parameters. Thus, the control performance of NOIC is improved. The second one is an intelligent optimized controller based on the regulation mechanism of adrenalin (ALIC) in endocrine system. The method to optimize and adjust the control parameters dynamically is also provided, and thus the control performance of ALIC is improved. The simulation results demonstrate that the control performances of the NOIC and ALIC are better than that of the conventional PID controller.For networked control, a novel architecture of distributed networked control system is first presented. And then the model identification and optimized control methods via remote network are presented respectively according to HGA. Finally, the network identification and optimized algorithms are applied in the micro-motion platform mechanism with 6 DOF.At last, a summary of the thesis is made, and the deficiency in the project and the further development are narrated respectively.
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