Controllability and observability analysis of basal ganglia model and feedback linearisation control

被引:13
|
作者
Rouhollahi, Korosh [1 ]
Andani, Mehran Emadi [2 ]
Izadi, Iman [3 ]
Karbassi, Seyed Mahdi [1 ]
机构
[1] Yazd Univ, Dept Appl Math, Yazd, Iran
[2] Univ Isfahan, Dept Biomed Engn, Esfahan, Iran
[3] Isfahan Univ Technol, Dept Elect & Comp Engn, Esfahan 8415683111, Iran
关键词
feedback; brain; neurophysiology; diseases; medical control systems; closed loop systems; controllers; linearisation techniques; bioelectric phenomena; controllability analysis; observability analysis; basal ganglia model; feedback linearisation control; deep brain stimulation; clinical remedy; tremor control; Parkinson's disease; feedback signal; closed-loop controller; nonlinear BG model; feedback linearisation controller; two-part controller; subthalamic nucleus stimulation; partial state feedback controller; globus pallidus internal stimulation; disease condition; delivered stimulation signal; DEEP BRAIN-STIMULATION; PARKINSONS-DISEASE; TREMOR; SIMULATION; DYNAMICS; MECHANISM; NETWORK; LOOP;
D O I
10.1049/iet-syb.2016.0054
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
Deep brain stimulation (DBS) is a clinical remedy to control tremor in Parkinson's disease. In DBS, one of the two main areas of basal ganglia (BG) is stimulated. This stimulation is produced with no feedback of the tremor and often causes a wide range of unpleasant side effects. Using a feedback signal from tremor, the stimulatory signal can be reduced or terminated to avoid extra stimulation and as a result decrease the side effects. To design a closed-loop controller for the non-linear BG model, a complete study of controllability and observability of this system is presented in this study. This study shows that the BG model is controllable and observable. The authors also propose the idea of stimulating the two BG areas simultaneously. A two-part controller is then designed: a feedback linearisation controller for subthalamic nucleus stimulation and a partial state feedback controller for globus pallidus internal stimulation. The controllers are designed to decrease three indicators: the hand tremor, the level of delivered stimulation signal in disease condition, and the ratio of the level of delivered stimulation signal in health condition to disease condition. Considering these three indicators, the simulation results show satisfactory performance.
引用
收藏
页码:144 / 154
页数:11
相关论文
共 50 条
  • [21] Observability and Controllability Analysis of Pipeline Systems
    Lei, Cheng
    Li, Xiangshun
    Wei, Di
    2016 2ND INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS - COMPUTING TECHNOLOGY, INTELLIGENT TECHNOLOGY, INDUSTRIAL INFORMATION INTEGRATION (ICIICII), 2016, : 290 - 293
  • [23] Controllability and observability of linear linguistic control systems
    Zhao, Liang
    ICIC Express Letters, 2011, 5 (9 B): : 3341 - 3346
  • [24] Controllability and Observability of Singular Boolean Control Networks
    Meng, Min
    Li, Beiyou
    Feng, Jun-e
    CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2015, 34 (04) : 1233 - 1248
  • [25] Controllability and observability of switched Boolean control networks
    Zhang, L.
    Feng, J.
    Yao, J.
    IET CONTROL THEORY AND APPLICATIONS, 2012, 6 (16): : 2477 - 2484
  • [26] Robust Controllability and Observability of Boolean Control Networks
    Wang, Yuanhua
    Zhang, Xiao
    Hao, Yaqi
    Cheng, Daizhan
    2018 37TH CHINESE CONTROL CONFERENCE (CCC), 2018, : 811 - 816
  • [28] Synthesis for controllability and observability of logical control networks
    Zhang, Kuize
    Johansson, Karl Henrik
    2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC), 2019, : 108 - 113
  • [29] Controllability and Observability of Singular Boolean Control Networks
    Min Meng
    Beiyou Li
    Jun-e Feng
    Circuits, Systems, and Signal Processing, 2015, 34 : 1233 - 1248
  • [30] Model-Based Spatiotemporal Analysis and Control of a Network of Spiking Basal Ganglia Neurons
    Liu, Jianbo
    Khalil, Hassan K.
    Oweiss, Karim G.
    2011 5TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER), 2011, : 273 - 277