Robust PID Design Based on QFT and Convex–Concave Optimization

Mercader, Patricia; Åström, Karl Johan; Baños, Alfonso; Hägglund, Tore

doi:10.1109/tcst.2016.2562581

Cited by 79 publications

(52 citation statements)

References 29 publications

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“…This means that, despite the exhaustive tuning of those parameters, it is impossible to find a PID configuration able to reduce the setling time to 0, the over-oscillation to 0, the error to 0, etc. However, the objective is to adjust the PID parameters following a compromise among them, making adjustments depending on the requirements of the application [25,26]. Finally, there is an anti-windup element to guarantee the maintenance of the control signal within the actuation limits.…”

Section: Definition Of a Pid Controllermentioning

confidence: 99%

“…However, in the case of this paper, a combined optimization using four parameters and a sequence of steps, instead of a single step response, is considered. In consequence, in this case, is not assured that the PID optimization can be considered as a convex optimization problem [24][25][26]. This fact supports the use of meta-heuristics because they can scale possible local minima in the search space.…”

Section: Optimization Of the Controllermentioning

confidence: 99%

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Speed Control Optimization for Autonomous Vehicles with Metaheuristics

2020

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The development of speed controllers under execution in autonomous vehicles within their dynamic driving task (DDT) is a traditional research area from the point of view of control techniques. In this regard, Proportional – Integral – Derivative (PID) controllers are the most widely used in order to meet the requirements of cruise control. However, fine tuning of the parameters associated with this type of controller can be complex, especially if it is intended to optimize them and reduce their characteristic errors. The objective of the work described in this paper is to evaluate the capacity of several metaheuristics for the adjustment of the parameters Kp, 1/Ti, and 1/Td of a PID controller to regulate the speed of a vehicle. To do this, an adjustment error function has been established from a linear combination of classic estimators of the goodness of the controller, such as overshoot, settling time (ts), steady-state error (ess), and the number of changes of sign of the signal (d). The error obtained when applying the controller has also been compared to a computational model of the vehicle after estimating the parameters Kp, Ki, and Kd, both for a setpoint sequence used in the adjustment of the system parameters and for a sequence not used during the adjustment, and therefore unknown by the system. The main novelty of the paper is to propose a new global error function, a function that enables the use of heuristic optimization methods for PID tuning. This optimization has been carried out by using three methods: genetic algorithms (GA), memetics algorithms (MA), and mesh adaptive direct search (MADS). The results of the application of the optimization methods using the proposed metric show that the accuracy of the PID controller is improved, compared with the classical optimization based on classical methods like the integral absolute error (IAE) or similar metrics, reducing oscillatory behaviours as well as minimizing the analysed performance indexes.

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Section: Definition Of a Pid Controllermentioning

confidence: 99%

Section: Optimization Of the Controllermentioning

confidence: 99%

Speed Control Optimization for Autonomous Vehicles with Metaheuristics

2020

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“…In these approaches the controller parameters are tuned manually using graphical methods. New optimization-based algorithms have also been proposed recently (Mercader et al, 2016). The set of all stabilizing PID controllers with H ∞ performance is obtained using only the frequency-domain data in Keel and Bhattacharyya (2008).…”

Section: Introductionmentioning

confidence: 99%

A data-driven approach to robust control of multivariable systems by convex optimization

Karimi

Kammer

2017

Automatica

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The frequency-domain data of a multivariable system in different operating points is used to design a robust controller with respect to the measurement noise and multimodel uncertainty. The controller is fully parametrized in terms of matrix polynomial functions and can be formulated as a centralized, decentralized or distributed controller. All standard performance specifications like H2, H∞ and loop shaping are considered in a unified framework for continuous-and discrete-time systems. The control problem is formulated as a convex-concave optimization problem and then convexified by linearization of the concave part around an initial controller. The performance criterion converges monotonically to a local optimal solution in an iterative algorithm. The effectiveness of the method is compared with fixed-structure controller design methods based on non-smooth optimization via multiple simulation examples.

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“…The Quantitative Feedback Theory (QFT) also uses the frequency response of a system to compute robust controllers. New optimization based algorithms have been proposed recently Mercader et al (2016) to compute QFT controllers. The set of all stabilizing PID controllers with H ∞ performance is obtained using only the frequency-domain data in Keel and Bhattacharyya (2008).…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Controller Design for Atomic-Force Microscopy

Kammer¹,

Nievergelt

Fantner

et al. 2017

IFAC-PapersOnLine

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A novel method to design data-driven, fixed-structure controllers with H 2 and H ∞ performance objectives is presented. The control design problem is transformed into a convex optimization problem with linear matrix inequality constraints, which can be solved efficiently with standard solvers. The method is used to design a data-driven controller for an atomic-force microscope. The closed-loop performance of the calculated controller is validated on a real setup.

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Robust PID Design Based on QFT and Convex–Concave Optimization

Cited by 79 publications

References 29 publications

Speed Control Optimization for Autonomous Vehicles with Metaheuristics

Speed Control Optimization for Autonomous Vehicles with Metaheuristics

A data-driven approach to robust control of multivariable systems by convex optimization

Data-Driven Controller Design for Atomic-Force Microscopy

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