On hardware-in-the-loop simulation

Bacic, Marko

doi:10.1109/cdc.2005.1582653

Cited by 137 publications

(67 citation statements)

References 12 publications

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“…However, a simplified recursive procedure for predicting the process free response is now described. To obtain this output predictor from Equation (22), first multiply throughout by ∆ to give A(z)∆Y(z) = B(z)∆U(z) + W(z). At discrete time step k, it can be observed that the expected future values of the random disturbance term w(k + i) = 0 for all i > 0, and since the free response is required, it can also be assumed that the future controls ∆u(k) = 0 and ∆u(k + 1) = 0.…”

Section: Optimal Predictionsmentioning

confidence: 99%

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A Microcontroller-Based Adaptive Model Predictive Control Platform for Process Control Applications

Short

Abugchem

2017

Electronics

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Model predictive control (MPC) schemes employ dynamic models of a process within a receding horizon framework to optimize the behavior of a process. Although MPC has many benefits, a significant drawback is the large computational burden, especially in adaptive and constrained situations. In this paper, a computationally efficient self-tuning/adaptive MPC scheme for a simple industrial process plant with rate and amplitude constraints on the plant input is developed. The scheme has been optimized for real-time implementation on small, low-cost embedded processors. It employs a short (2-step) control horizon with an adjustable prediction horizon, automatically tunes the move suppression (regularization) parameter to achieve well-conditioned control, and presents a new technique for generating the reference trajectory that is robust to changes in the process time delay and in the presence of any inverse response. In addition, the need for a full quadratic programming procedure to handle input constraints is avoided by employing a quasi-analytical solution that optimally fathoms the constraints. Preliminary hardware-in-the-loop (HIL) test results indicate that the resulting scheme performs well and has low implementation overhead.

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Section: Optimal Predictionsmentioning

confidence: 99%

“…HIL simulation provides an effective technique for designing and testing of real-time and embedded control systems [20][21][22][23][24][25]. HIL testing is used to lower total costs, shorten development cycles, and improve functional performance of real-time and embedded control systems.…”

Section: Hil Testing Facilitymentioning

confidence: 99%

A Microcontroller-Based Adaptive Model Predictive Control Platform for Process Control Applications

Short

Abugchem

2017

Electronics

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“…Uses can also be found in the control of electrical machines and in robotics applications (induction machine drives [28,29] and motion control [30,31]). More recently, reconfigurable devices have been increasingly finding applications as means for implementing hardware-in-the-loop platforms [32,33] for debugging purposes principally but increasingly as a means to emulate subcomponents of larger systems. …”

Section: Trends In the Use Of Fpgas For Implementing Iedsmentioning

confidence: 99%

Real-Time Control System for Improved Precision and Throughput in an Ultrafast Carbon Fiber Placement Robot Using a SoC FPGA Extended Processing Platform

Ochoa-Ruiz

Bevan

Lamotte

et al. 2017

International Journal of Reconfigurable Computing

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We present an architecture for accelerating the processing and execution of control commands in an ultrafast fiber placement robot. The system consists of a robotic arm designed by Coriolis Composites whose purpose is to move along a surface, on which composite fibers are deposed, via an independently controlled head. In first system implementation, the control commands were sent via Profibus by a PLC, limiting the reaction time and thus the precision of the fiber placement and the maximum throughput. Therefore, a custom real-time solution was imperative in order to ameliorate the performance and to meet the stringent requirements of the target industry (avionics, aeronautical systems). The solution presented in this paper is based on the use of a SoC FPGA processing platform running a real-time operating system (FreeRTOS), which has enabled an improved comamnd retrieval mechanism. The system's placement precision was improved by a factor of 20 (from 1 mm to 0.05 mm), while the maximum achievable throughput was 1 m/s, compared to the average 30 cm/s provided by the original solution, enabling fabricating more complex and larger pieces in a significant fraction of the time.

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“…In this paper, we seek to explore the impact of applying "Nolte C" and SBS on another realistic case study; that is the distributed control of a CarboNitriding heat treatment furnace system built using "hardware-in-the-loop" (HIL) testing method [29,30]. HIL has been extensively used in embedded systems development and refinement processes, particularly for automotive applications (see, for example, [31][32][33][34][35][36][37]).…”

Section: Introductionmentioning

confidence: 99%

Impact of Reducing Bit Stuffing Jitter on the Control Performance of a CAN-Based Distributed Furnace System

Nahas¹

2018

JES

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The Controller Area Network (CAN) protocol is widely used in distributed real-time, resource-constrained embedded systems. CAN uses "Non Return to Zero" (NRZ) coding and employs a bit-stuffing mechanism for clock synchronization. Such a mechanism causes a variation in the CAN frame length which may have a detrimental impact on the control behaviour of safety-critical systems employing this protocol. To address this issue, two techniques known as "byte-based XOR masking" and "software bit stuffing" were developed and achieved a jitter reduction of up to 20% and 40%, respectively, when employed in practical designs. This paper investigates the effectiveness of such techniques in a real-time control application; that is a simple furnace system case study based on a "hardware-in-the-loop" (HIL) testbed facility. The results show that reducing bit stuffing jitter has the potential to improve the control performance of distributed real-time systems employing CAN protocol.

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On hardware-in-the-loop simulation

Cited by 137 publications

References 12 publications

A Microcontroller-Based Adaptive Model Predictive Control Platform for Process Control Applications

A Microcontroller-Based Adaptive Model Predictive Control Platform for Process Control Applications

Real-Time Control System for Improved Precision and Throughput in an Ultrafast Carbon Fiber Placement Robot Using a SoC FPGA Extended Processing Platform

Impact of Reducing Bit Stuffing Jitter on the Control Performance of a CAN-Based Distributed Furnace System

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