Nonlinear model predictive control hardware implementation with custom-precision floating point operations

Ayala, Helon Vicente Hultmann; Sampaio, Renato Coral; Muñoz, Daniel M.; Llanos, Carlos H.; Coelho, Leandro dos Santos; Jacobi, Ricardo Pezzuol

doi:10.1109/med.2016.7535908

Cited by 11 publications

(12 citation statements)

References 23 publications

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“…Using a data width of 27 bits is suggested in Ayala et al 17 as a good starting point for testing the floating-point precision required for the application, because it presents a good tradeoff between performance and resource usage regarding Xilinx digital signal processor (DSP) inputs of 18 bits. However, data width values of 32 bits are commonly found in the literature, such as in the works of Lau et al 27 and Ingole and Kvasnica, 28 helping to understand how the increase of the number of bits used for representing data might improve the performance of the controller for our application.…”

Section: Experimental Testing and Resultsmentioning

confidence: 99%

“…These floating-point arithmetic units were developed using VHDL and parameterized data, which means the designer is capable of choosing the appropriate amount of bits to represent numeric values within the desired architecture. 17…”

Section: Hardware Implementation Of Mpcmentioning

confidence: 99%

“…Implementations of MPC using FPGAs can be vastly found in the literature, ranging from linear to nonlinear approaches: in the automotive industry, 10,11 followed by power electronics and energy systems, 12,13 aerospace, 14,15 and among many others. Some of these implementations are capable of achieving very fast calculation times, as it is the case of the embedded MPC solutions presented by Jerez et al 16 and by Ayala et al, 17 whose applications correspond to a set of four oscillating masses attached to walls and a single-link robotic manipulator, respectively. Both works obtained response times in the order of milliseconds.…”

Section: Introductionmentioning

confidence: 99%

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Embedded linear model predictive control in field-programmable gate array using register-transfer level implementation and floating-point representation applied to a quadrotor system model

Farias¹,

Murilo²,

Lopes³

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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Model predictive control is increasingly becoming a popular control strategy for a wide range of applications in both industry and academia, mainly motivated by its ability to systematically handle constraints imposed on a system, regardless of its nature. However, this generates high computational demands, limiting the applicability of model predictive control. Field-programmable gate arrays are reconfigurable hardware platforms that allow the parallel implementation of model predictive control, accelerating such algorithms, but most works found in the literature opt to use high-level synthesis tools and fixed-point numeric representation to generate embedded controllers, resulting in faster-designed solutions but not exactly efficient and flexible ones, that can be applied to different scenarios. Regarding such matter, this work proposes the manual implementation (register-transfer level implementation) of linear model predictive control and the usage of floating-point numeric representation applied to a quadrotor system. The initial results obtained using the proposed controller are presented in this article, achieving 29.34 ms of calculation time at 50 MHz for the attitude control of a quadrotor model containing twelve states and four control outputs.

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Section: Experimental Testing and Resultsmentioning

confidence: 99%

Section: Hardware Implementation Of Mpcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Embedded linear model predictive control in field-programmable gate array using register-transfer level implementation and floating-point representation applied to a quadrotor system model

Farias¹,

Murilo²,

Lopes³

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

“…If less processing time is required and the hardware resource is not restricted, two equal neural networks can be used. The implementation and synthesis of the neural network can be done with the methodology and tools proposed in [ 50 , 51 ]. Ayala et al, show a floating-point implementation in hardware for a neural network architecture that guarantees low latency.…”

Section: Methodsmentioning

confidence: 99%

Sensor Fusion to Estimate the Depth and Width of the Weld Bead in Real Time in GMAW Processes

Bestard

Sampaio

Vargas

et al. 2018

Sensors

Self Cite

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The arc welding process is widely used in industry but its automatic control is limited by the difficulty in measuring the weld bead geometry and closing the control loop on the arc, which has adverse environmental conditions. To address this problem, this work proposes a system to capture the welding variables and send stimuli to the Gas Metal Arc Welding (GMAW) conventional process with a constant voltage power source, which allows weld bead geometry estimation with an open-loop control. Dynamic models of depth and width estimators of the weld bead are implemented based on the fusion of thermographic data, welding current and welding voltage in a multilayer perceptron neural network. The estimators were trained and validated off-line with data from a novel algorithm developed to extract the features of the infrared image, a laser profilometer was implemented to measure the bead dimensions and an image processing algorithm that measures depth by making a longitudinal cut in the weld bead. These estimators are optimized for embedded devices and real-time processing and were implemented on a Field-Programmable Gate Array (FPGA) device. Experiments to collect data, train and validate the estimators are presented and discussed. The results show that the proposed method is useful in industrial and research environments.

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“…Authors in [12] used MPC for position and force control of a human arm like a seven DOF robotics manipulator. In [13], a real-time computation method for MPC has been presented, which introduced a mapping of offline approximation approach in neural networks (NN). The proposed technique has been implemented on a low cost field programmable gate array (FPGA) to show its less hardware and computational time requirements.…”

Section: Introductionmentioning

confidence: 99%

Modern Control Laws for an Articulated Robotic Arm: Modeling and Simulation

Iqbal

2019

Eng. Technol. Appl. Sci. Res.

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The robotic manipulator has become an integral component of modern industrial automation. The current paper deals with the mathematical modeling and non-linear control of this manipulator. DH-parameters are used to derive kinematic model while the dynamics is based on Euler-Lagrange equation. Two modern control strategies, H∞ and model predictive control (MPC), are investigated to develop the control laws. For an optimal performance, the controllers have been fine-tuned through a simulation conducted in MATLAB/Simulink environment. The designed control laws are subjected to various inputs and tested for effectiveness in transient parameters like settling time and overshoot as well as steady state error. Simulation results confirm the effectiveness of the developed controllers by precisely tracking the reference motion trajectories.

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Nonlinear model predictive control hardware implementation with custom-precision floating point operations

Cited by 11 publications

References 23 publications

Embedded linear model predictive control in field-programmable gate array using register-transfer level implementation and floating-point representation applied to a quadrotor system model

Embedded linear model predictive control in field-programmable gate array using register-transfer level implementation and floating-point representation applied to a quadrotor system model

Sensor Fusion to Estimate the Depth and Width of the Weld Bead in Real Time in GMAW Processes

Modern Control Laws for an Articulated Robotic Arm: Modeling and Simulation

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