Adaptive horizon model predictive control based sensor management for multi-target tracking

Page, Scott F.; Dolia, Alexander N.; Harris, C.J.; White, Neil

doi:10.1109/acc.2006.1656527

Cited by 5 publications

(6 citation statements)

References 6 publications

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“…The time dependency along with the associated computational burden of MPC poses a serious limitation in-comparison to existing motor control techniques, the proposed technique eases the computational burden by skipping model update (as discussed above), partial precomputing [31] and varying horizons [32]. The idea of pre-computation of system matrices was originally used in FCS-MPC [31], but as the MPC computational burden is relatively significant in-comparison to FCS-MPC, the partial pre-computation in proposed technique is more effective.…”

Section: Current Control Loopmentioning

confidence: 99%

Brain Emotional Learning and Adaptive Model Predictive Controller for Induction Motor Drive: A New Cascaded Vector Control Topology

Affan

Uddin

2021

Int. J. Control Autom. Syst.

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With the development of high-speed microprocessors, it is now possible to implement mathematically complex vector control algorithms without compromising on the performance of motor drive. Among vector control techniques space vector proportional-integral (PI), direct-torque control (DTC), field-oriented control (FOC), model-predictive control (MPC) are being widely used in industries. But their limitations have urged researchers to develop more advance techniques. In this paper, a new technique learning and adaptive model — based predictive control (termed as LAMPC) is proposed for the vector control of three phase induction motor. In the proposed method, the dynamic model of induction motor is updated adaptively based on prediction (receding horizon principle) for the inner control loop (current control) while the brain emotional learning-based intelligent controller (BELIC) is used for the outer control loop (speed control). The proposed methodology offers desired dynamic response, precise tracking, good disturbance handling capability along with satisfactory steady-state performance. To show the effectiveness of the proposed approach, benchmark simulation results for various inputs are presented using MATLAB/Simulink. Finally, the detailed qualitative and quantitative comparison of the proposed LAMPC is made with the most relevant vector techniques to show its significance.

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Section: Current Control Loopmentioning

confidence: 99%

Brain Emotional Learning and Adaptive Model Predictive Controller for Induction Motor Drive: A New Cascaded Vector Control Topology

Affan

Uddin

2021

Int. J. Control Autom. Syst.

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“…AHMPC mitigates this difficulty by checking that stabilization is occuring. If (11)(12)(13)(14)(15) don't hold even after the horizon N has been increased substantially then this is a strong indication that the solver is returning locally rather than globally minimizing solutions and these local solutions are not stabilizing. To change this behaviour one needs to start the solver at a substantially different initial guess.…”

Section: One Way Of Obtaining a Terminal Pairmentioning

confidence: 99%

“…For more on Class K functions we refer the reader to [9]. If (11)(12)(13)(14)(15) hold for all for k = N, . .…”

Section: Adaptive Horizon Model Predictive Controlmentioning

confidence: 99%

“…, N + M − 1. then we presume that x * N (N ) ∈ X f , a set where κ f (x) is stabilizing and we use the control u * N (0) to move one time step forward to x + = f (x, u * N (0)). At this next state x + we solve the horizon N − 1 optimal control problem and check that the extension of the new optimal trajectory satisfies (11)(12)(13)(14)(15).…”

Section: Adaptive Horizon Model Predictive Controlmentioning

confidence: 99%

“…Adaptive horizon tracking is discussed in [12] and [4]. In [10] an adaptive parameter estimation algorithm suitable for MPC was proposed, which uses the available input and output signals to estimate the unknown system parameters.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Horizon Model Predictive Control and Al’brekht’s Method

Krener

2020

Encyclopedia of Systems and Control

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A standard way of finding a feedback law that stabilizes a control system to an operating point is to recast the problem as an infinite horizon optimal control problem. If the optimal cost and the optmal feedback can be found on a large domain around the operating point then a Lyapunov argument can be used to verify the asymptotic stability of the closed loop dynamics. The problem with this approach is that is usually very difficult to find the optimal cost and the optmal feedback on a large domain for nonlinear problems with or without constraints. Hence the increasing interest in Model Predictive Control (MPC). In standard MPC a finite horizon optimal control problem is solved in real time but just at the current state, the first control action is implimented, the system evolves one time step and the process is repeated. A terminal cost and terminal feedback found by Al'brekht's methoddefined in a neighborhood of the operating point is used to shorten the horizon and thereby make the nonlinear programs easier to solve because they have less decision variables.Adaptive Horizon Model Predictive Control (AHMPC) is a scheme for varying the horizon length of Model Predictive Control (MPC) as needed. Its goal is to achieve stabilization with horizons as small as possible so that MPC methods can be used on faster and/or more complicated dynamic processes.

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Adaptive Horizon Model Predictive Control and Al’brekht’s Method

Krener

2021

Encyclopedia of Systems and Control

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Adaptive horizon model predictive control based sensor management for multi-target tracking

Cited by 5 publications

References 6 publications

Brain Emotional Learning and Adaptive Model Predictive Controller for Induction Motor Drive: A New Cascaded Vector Control Topology

Brain Emotional Learning and Adaptive Model Predictive Controller for Induction Motor Drive: A New Cascaded Vector Control Topology

Adaptive Horizon Model Predictive Control and Al’brekht’s Method

Adaptive Horizon Model Predictive Control and Al’brekht’s Method

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