Design of Continuous-time Controllers using Cartesian Genetic Programming

Kadlic, Branislav; Sekaj, Ivan; Pernecký, Daniel

doi:10.3182/20140824-6-za-1003.00915

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…Symbolic regression modeling has been used to construct serval nonlinear PID controllers, but these controllers are inconvenient for application because of their unrestricted structures. (Das et al, 2012a, 2012b; Kadlic et al, 2014; Sekaj and Perkacz, 2007).…”

Section: Introductionmentioning

confidence: 99%

An improved nonlinear proportional-integral-differential controller combined with fractional operator and symbolic adaptation algorithm

Shi

Miao

Wang

2019

Transactions of the Institute of Measurement and Control

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Parameter adjustment is usually applied for designing the proportional-integral-differential (PID) controllers. However, the ability to improve control performance by adjusting parameters is limited. Hence, with the goal to achieve ideal closed-loop response, this paper takes advantage of a structural optimization method for modifying the controller model. A symbolic adaptation algorithm for fractional order PID (FOPID) controller is employed to obtain precise nonlinear controller model. Firstly, a modeling comparison for nonlinear duffing system is carried out to highlight the efficiency of the symbolic adaptation algorithm. The case study indicates the proposed algorithm can establish compact dynamic models by amending the shortcomings of symbolic regression. Secondly, the proposed controller is restructured with the linear FOPID controller and its nonlinearity is increased by adjusting controllers’ components in symbolic form. The proposed controllers are simulated in an unstable second-order system, a time-delay system and a nonlinear VanderPol system. Compared with the IOPID and the FOPID controller, the symbolic adaptation algorithm improves the structural flexibility of these linear controllers. Meanwhile, the system response can better approximate the desired response and the structural integrity of the nonlinear controller model is guaranteed simultaneously. Finally, the nonlinear FOPD controllers for trajectory tracking experiments are carried out on a rotary inverted pendulum control system.

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Section: Introductionmentioning

confidence: 99%

An improved nonlinear proportional-integral-differential controller combined with fractional operator and symbolic adaptation algorithm

Shi

Miao

Wang

2019

Transactions of the Institute of Measurement and Control

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“…Among the methods used for parameter tuning, computational intelligence based methods (population-based algorithms in particular) take an important place in the literature [9,14,42,47,51]. This group of methods also includes genetic programming [15,16,25,35,46]. The GP is based on processing of the population of individuals (by using evolutionary operators) and its evaluation.…”

Section: Introductionmentioning

confidence: 99%

Genetic Programming Algorithm for Designing of Control Systems

Cpałka¹,

Łapa²,

Przybył³

2018

ITC

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,andrzej.przybyl}@iisi.pcz.pl Genetic programming algorithms and other population-based methods are a convenient tool for solving complex interdisciplinary problems. Their characteristic feature is that they flexibly adapt to the problem and expectations of a designer. In this paper, they are used for designing complex control systems. In particular, a new approach for automatic designing of PID-based controllers which are resistant to noises in the measuring path is proposed. It is based on the knowledge about an object model and capabilities of the genetic algorithm and genetic programming. Not only do these make it possible to tune parameters and select the structure of PID-based controllers, but they also tune parameters of some additional components of a complex controller structure, like parameters of finite impulse response (FIR) filters. The idea of the proposed approach relies on proper encoding of the controller and a dedicated way of evolutionary processing of encoded solutions. The approach proposed in this paper has been tested using a typical control problem, i.e. a DC motor control problem.

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“…Previous work also using GP-based Lyapunov function synthesis can be found in [40,41], and controller synthesis in [42][43][44][45][46][47]. However these pieces of work lack formal guarantees on the behaviour of the system, other than for specific tested samples or simulations.…”

Section: Introductionmentioning

confidence: 99%

Formal Controller Synthesis via Genetic Programming

Verdier

Mazo

2017

IFAC-PapersOnLine

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This paper proposes a framework for automatic formal controller synthesis for general hybrid systems with a subset of safety and reachability specifications. The framework uses genetic programming to automatically co-synthesize controllers and candidate Lyapunov-like functions. These candidate Lyapunov-like functions are used to formally verify the control specification, and their correctness is proven using a Satisfiability Modulo Theories solver. The advantages of this approach are: no restriction is made to polynomial systems, the synthesized controllers are expressed as compact expressions, and no explicit solution structure has to be specified beforehand. We demonstrate the effectiveness of the proposed framework in several case studies, including nonpolynomial systems, sampled-data systems, systems with bounded uncertainties, switched systems, and systems with jumps.

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Design of Continuous-time Controllers using Cartesian Genetic Programming

Cited by 8 publications

References 14 publications

An improved nonlinear proportional-integral-differential controller combined with fractional operator and symbolic adaptation algorithm

An improved nonlinear proportional-integral-differential controller combined with fractional operator and symbolic adaptation algorithm

Genetic Programming Algorithm for Designing of Control Systems

Formal Controller Synthesis via Genetic Programming

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