Development of a Resonant Generalized Predictive Controller for Sinusoidal Reference Tracking

Cordero, Raymundo; Estrabis, Thyago; Brito, Moacyr Aureliano Gomes de; Gentil, Gabriel

doi:10.1109/tcsii.2021.3102535

Cited by 6 publications

(9 citation statements)

References 29 publications

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“…where c and p are the size of the control horizon and the prediction horizon, respectively, such as c ≤ p. The value of Y can be obtained as follows [18]:…”

Section: Predictive Control Law Through Gpcmentioning

confidence: 99%

“…where A, B and C are defined in (20). The cost function in (25) is commonly applied in GPC to obtain the optimal U through an optimization procedure [18][19][20]:…”

Section: Predictive Control Law Through Gpcmentioning

confidence: 99%

“…Thus, the design of a control system that tracks/rejects biased sinusoidal reference/disturbance is not straightforward. The resonant controller based on generalized predictive control (GPC) described in [18] was implemented in a DSP and strictly satisfied IMP. GPC uses an augmented model to predict future plant responses.…”

Section: Introductionmentioning

confidence: 99%

“…GPC has a good transient response and constraints can be easily included [19][20][21]. However, the approach in [18] was not designed to track/reject biased-sinusoidal signals.…”

Section: Introductionmentioning

confidence: 99%

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Tracking and Rejection of Biased Sinusoidal Signals Using Generalized Predictive Controller

et al. 2022

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Some novel applications require the tracking/rejection of biased sinusoidal reference/distur-bances. According to the internal model principle (IMP), a controller must embed the model of a biased sinusoidal signal to track references and also reject perturbations modeled through the aforementioned signal. However, the design of that kind of controller is not straightforward, especially when they are implemented in digital processors. This paper presents a controller, based on generalized predictive control (GPC), designed for tracking/rejection of biased sinusoidal signals. In general, GPC is based on the prediction of the plant responses through an augmented prediction model. The proposed approach develops an augmented model that predicts the future errors. The prediction model and the control law used in the proposed approach embed the discrete-time model of a biased sinusoidal signal. Thus, the proposed controller can track/reject biased sinusoidal references/disturbances. The predicted errors and the future inputs of the proposed augmented model are used to define the cost function that measures the control performance. An optimization technique was applied to obtain the solution of the cost function, which is the optimal sequence of future model inputs that allows defining the control law. Experimental tests prove that the proposed controller can asymptotically track and reject biased sinusoidal signals.

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“…where c and p are the size of the control horizon and the prediction horizon, respectively, such as c ≤ p. The value of Y can be obtained as follows [18]:…”

Section: Predictive Control Law Through Gpcmentioning

confidence: 99%

“…where A, B and C are defined in (20). The cost function in (25) is commonly applied in GPC to obtain the optimal U through an optimization procedure [18][19][20]:…”

Section: Predictive Control Law Through Gpcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…GPC has a good transient response and constraints can be easily included [19][20][21]. However, the approach in [18] was not designed to track/reject biased-sinusoidal signals.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tracking and Rejection of Biased Sinusoidal Signals Using Generalized Predictive Controller

et al. 2022

Self Cite

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show abstract

“…Again, the method was only developed for SISO systems, without optimized usage of GPC parameters and none prescribed stability. Regarding [16], a new approach was developed to embed an internal model of sinusoidal reference through the application of the first and second-order difference operators in a SISO system. More applications of type-m controllers (m ≥ 2) can be found in [17] - [21].…”

Section: Introductionmentioning

confidence: 99%

A New Proposal Generalized Predictive Control Algorithm With Polynomial Reference Tracking Applied for Sodium Fast Reactors

et al. 2023

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This paper proposes a generalized predictive control (GPC) with constraints and orthonormal Laguerre functions using the simplified model of the primary system (reactor core and intermediate heat exchanger (IHX)) of a prototypical sodium fast reactor (SFR). This paper develops a multiple-input multiple-output (MIMO) GPC with input constraints able to track polynomial references of any degree applied in coolant temperature difference across the core and fractional power. The manipulated variables of the GPC-SFR are the reactivity and the sodium flow rate of the primary and secondary pipes. Moreover, orthonormal Laguerre functions and step down condition number techniques were also applied to avoid the numerical ill-conditioning issue in quadratic programming of large systems. Thus, a GPC type-2 was designed to control fractional power, coolant temperature difference across the core and sodium tank temperature of the SFR primary system when temperature references change according to a linear ramp after reaching their steady-state operation, sustaining 100% power operation on the reactor. In order to analyze the load tracking capability of the GPC-SFR type-2, the load following from 100% fractional power (FP) to 60% FP at 0.8% FP/min rate is simulated. Constraints on the rate of coolant temperature difference across the core and reactivity were applied for the design safety. For comparison criteria, this paper compares the GPC-SFR type-2 with the GPC-SFR type-1, i.e, standard model predictive control (MPC), to verify the viability and superior performance of the proposal regarding: (a) ramp-tracking capability of temperature and load; (b) the rejections of a reactivity disturbance of -1 cent and a secondary sodium inlet temperature disturbance of +10 o F ; and (c) a simulation with uncertainty in reactor design. The simulations show that the GPC-SFR type-2 overcome the GPC-SFR type-1 robustness and performance.

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Integration of Predictive Control and Interconnected Structure for Autotuning Velocity Controller

Chang

Lü

Zheng

et al. 2023

IEEE/ASME Trans. Mechatron.

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Development of a Resonant Generalized Predictive Controller for Sinusoidal Reference Tracking

Cited by 6 publications

References 29 publications

Tracking and Rejection of Biased Sinusoidal Signals Using Generalized Predictive Controller

Tracking and Rejection of Biased Sinusoidal Signals Using Generalized Predictive Controller

A New Proposal Generalized Predictive Control Algorithm With Polynomial Reference Tracking Applied for Sodium Fast Reactors

Integration of Predictive Control and Interconnected Structure for Autotuning Velocity Controller

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