A graph search and neural network approach to adaptive nonlinear model predictive control

Reese, Brandon M.; Collins, Emmanuel G.

doi:10.1016/j.engappai.2016.07.001

Cited by 19 publications

(9 citation statements)

References 40 publications

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“…Sampling-Based Model Predictive Control (SBMPC) is an MPC method that consists of the consolidation of an optimization algorithm called Sampling-Based Model Predictive Optimization (SBMPO) and a receding horizon control technique [27], [28]. The main idea behind using SBMPC to solve the energy management problem, formulated in the previous section, relies on the concept that this problem can be formulated as a shortest path problem.…”

Section: Sampling-based Model Predictive Control Energy Management So...mentioning

confidence: 99%

Sampling-Based Model Predictive Control of PV-Integrated Energy Storage System Considering Power Generation Forecast and Real-Time Price

Ospina

Gupta²,

Newaz

et al. 2019

IEEE Power Energy Technol. Syst. J.

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This paper proposes a novel control solution designed to solve the local and grid-connected distributed energy resources (DERs) management problem by developing a generalizable framework capable of controlling DERs based on forecasted values and real-time energy prices. The proposed model uses sampling-based model predictive control (SBMPC), together with the real-time price of energy and forecasts of PV and load power, to allocate the dispatch of the available distributed energy resources (DERs) while minimizing the overall cost. The strategy developed aims to find the ideal combination of solar, grid, and energy storage (ES) power with the objective of minimizing the total cost of energy of the entire system. Both offline and controller hardware-in-the-loop (CHIL) results are presented for a 7-day test case scenario and compared with two manual base test cases and four baseline optimization algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Quadratic Programming interior-point method (QP-IP), and Sequential Quadratic Programming (SQP)) designed to solve the optimization problem considering the current status of the system and also its future states. The proposed model uses a 24-hour prediction horizon with a 15-minute control horizon. The results demonstrate substantial cost and execution time savings when compared to the other baseline control algorithms. INDEX TERMSController Hardware-in-the-Loop (CHIL), distributed energy resources (DER), distributed generation (DG), distributed storage (DS), energy management, real-time pricing, model predictive control (MPC).

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Section: Sampling-based Model Predictive Control Energy Management So...mentioning

confidence: 99%

Sampling-Based Model Predictive Control of PV-Integrated Energy Storage System Considering Power Generation Forecast and Real-Time Price

Ospina

Gupta²,

Newaz

et al. 2019

IEEE Power Energy Technol. Syst. J.

Self Cite

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“…Various methods for estimating system parameters and meeting operating constraints are described in the adaptive MPC literature. Depending on the assumptions on model parameters, parameter identification methods such as recursive least squares (RLS) 7 , comparison sets, 8 set membership identification, 9,10 and neural networks 11,12 have been proposed. Heirung et al 13 propose an algorithm where the unknown parameters are estimated using RLS and system outputs are predicted using the resulting parameter estimates.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive model predictive control: Performance and parameter estimation

Liu

Cannon

Koksal-Rivet

2020

Intl J Robust & Nonlinear

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For systems with uncertain linear models, bounded additive disturbances and state and control constraints, a robust model predictive control (MPC) algorithm incorporating online model adaptation is proposed. Sets of model parameters are identified online and employed in a robust tube MPC strategy with a nominal cost. The algorithm is shown to be recursively feasible and input-to-state stable.Computational tractability is ensured by using polytopic sets of fixed complexity to bound parameter sets and predicted states. Convex conditions for persistence of excitation are derived and are related to probabilistic rates of convergence and asymptotic bounds on parameter set estimates. We discuss how to balance conflicting requirements on control signals for achieving good tracking performance and parameter set estimate accuracy. Conditions for convergence of the estimated parameter set are discussed for the case of fixed complexity parameter set estimates, inexact disturbance bounds, and noisy measurements.

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“…A comparison between SBMPO and other nonlinear MPC approaches is presented in [6]. It is seen that unlike approaches based on gradients and Hessians, SBMPO has the capacity to avoid local minima and if no implicit grid is used in the output space, it is proved that SBMPO is guaranteed to find a global optimum subject to the sampling.…”

Section: Introductionmentioning

confidence: 99%

“…The approach of [19] relies on Newton-type optimization, which requires an initial feasible trajectory and and is subject to convergence to a local minimum depending upon the initial conditions. In contrast, the current approach optimizes using SBMPO, which does not require an initial trajectory and as discussed in [6] converges to the global optimum of the discretized problem. Finally, unlike [6], the current approach is explicitly seeking to develop a methodology that is computationally fast.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, the current approach optimizes using SBMPO, which does not require an initial trajectory and as discussed in [6] converges to the global optimum of the discretized problem. Finally, unlike [6], the current approach is explicitly seeking to develop a methodology that is computationally fast. Using 1-DOF of the PUMA 762 robot, Appendix A provides a comparison between the heavy lifting planning given in [19] and planning using the current approach.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Dynamics to Consider Torque Constraints in Manipulator Planning With Heavy Loads

Chuy

Collins

Sharma

et al. 2017

Journal of Dynamic Systems, Measurement, and Control

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Input constraints are active in robot trajectory planning when a mobile robot traverses mobility challenges such as steep hills that limit the acceleration of the robot due to the torque constraints of the motor or engine or in manipulator lifting tasks when the load is sufficiently heavy that the torque constraints of the robot's motor prevent it from statically supporting the load in regions of the robot's workspace. This paper presents a general methodology for solving these planning tasks using a minimum-time cost function and applies it to the problem of a multiple degrees-of-freedom (DOF) manipulator lifting a heavy load. Planning for these types of problems requires use of the robot's dynamic model. Here, we plan using sampling-based model predictive optimization (SBMPO), which is only practical if the planning can be done quickly. Hence, substantial attention is given to efficient computations by: (1) using the dynamic model without integrating it, (2) using optimal control theory to develop an “optimistic A* estimate of cost-to-goal,” which is in this case a rigorous lower bound on the minimum time from a current state to a goal state, and (3) using prior experience to speed up the computation of a new trajectory. The methodology is experimentally verified for heavy lifting using a two-link manipulator.

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A graph search and neural network approach to adaptive nonlinear model predictive control

Cited by 19 publications

References 40 publications

Sampling-Based Model Predictive Control of PV-Integrated Energy Storage System Considering Power Generation Forecast and Real-Time Price

Sampling-Based Model Predictive Control of PV-Integrated Energy Storage System Considering Power Generation Forecast and Real-Time Price

Robust adaptive model predictive control: Performance and parameter estimation

Using Dynamics to Consider Torque Constraints in Manipulator Planning With Heavy Loads

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