Combined PID and LQR controller using optimized fuzzy rules
In this paper, a combination of PID controller and linear quadratic regulator is proposed. A fuzzy switching module is applied to optimally fuse both controllers. A new adaptive version of charged system search algorithm optimizes the membership functions of the fuzzy module. By the time, the algorithm changes itself to find a proper solution faster. To show the efficiency of the designed intelligent controller, the results of a simulated unicycle robot under disturbances are presented.
KeywordsArtificial intelligence Adaptive charged system search PID controller Linear quadratic regulator Fuzzy logic
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Conflict of interest
All authors declare that they have no conflict of interest.
Human participants or animals rights statement
This article does not contain any studies with human participants or animals performed by any of the authors.
- Guo W, Gao X, Jiang S, Zong C, Dai F (2014) LQR controller design for two-wheeled robot with a movable seat. In: Chinese control and decision conference (2014 CCDC), Changsha, pp 5248–5253Google Scholar
- Hu Z, Guo L, Wei S, Liao Q (2014) Design of LQR and PID controllers for the self balancing unicycle robot. In: IEEE international conference on information and automation (ICIA 2014) Hailar, pp 972–977Google Scholar
- Kankashvar MR, Kharrati H, Asl RM, Sadeghiani AB (2015) Designing PID controllers for a five-bar linkage robot manipulator using BBO algorithm. In: International conference on modeling, simulation, and applied optimization (ICMSAO 2015), Istanbul, pp 1–6Google Scholar
- Mohammadi Asl R, Hashemzadeh F, Badamchizadeh MA (2015) A new adaptive neural network based observer for robotic manipulators. In: 3rd RSI international conference on robotics and mechatronics (ICROM 2015), Tehran, pp 663–668Google Scholar
- Nobarian MS, Asl RM, Nemati M, Hashemzadeh F (2016) Optimal L1 control for linear time-delayed systems using GSA algorithm. In: 4th International conference on control, instrumentation, and automation (ICCIA 2016), pp 111–115Google Scholar
- Mohammadi Asl R, Pourabdollah E, Salmani M (2017a) Optimal fractional order PID for a robotic manipulator using colliding bodies design. Soft Comput. https://doi.org/10.1007/s00500-017-2649-9
- Pourabdollah E, Asl RM, Tsiligiridis T (2017) Performance optimization of a clustering adaptive gravitational search scheme for wireless sensor networks. In: Internet of things, smart spaces, and next generation networks and systems, Springer, pp 420–431Google Scholar
- Precup RE, Petriu E, Fedorovici LO, Radac MB, Dragan F (2014b) Multi-robot charged system search-based optimal path planning in static environments. In: IEEE international symposium on intelligent control (ISIC 2014), Juan Les Pins, pp 1912–1917Google Scholar
- Rizal Y, Ke CT, Ho MT (2015) Point-to-point motion control of a unicycle robot: design, implementation, and validation. In: 2015 IEEE international conference on robotics and automation (ICRA). Seattle, WA, pp 4379–4384Google Scholar
- Schoonwinkel A (1988) Design and test of a computer-stabilized unicycle. Dissertation, Stanford UniversityGoogle Scholar
- Taniguchi T, Eciolaza L, Sugeno M (2014) Model following control of a unicycle mobile robot via dynamic feedback linearization based on piecewise bilinear models. In: Laurent A, Strauss O, Bouchon-Meunier B, Yager R (eds) Information processing and management of uncertainty in knowledge-based systems, communications in computer and information science, vol 444. Springer, New York, pp 539–548Google Scholar