In modular multilevel converter battery energy storage systems (MMC-BESS), state-of-charge (SOC) balancing is essential for ensuring safe and reliable operation. Existing methods based on linear controllers or convent...
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In modular multilevel converter battery energy storage systems (MMC-BESS), state-of-charge (SOC) balancing is essential for ensuring safe and reliable operation. Existing methods based on linear controllers or conventional model-predictivecontrol (MPC) often suffer from slow balancing speed, difficult parameter tuning, and high computational burden. To address these challenges, this paper proposes a fast SOC balancing strategy based on nonlinear MPC. A nonlinear state-space model is first developed and then linearized to enable discrete single-step prediction of arm- and phase-level SOC values. A two-stage control scheme is introduced to coordinate inter-arm and inter-phase SOC balancing, significantly reducing the number of state variables involved in the MPC formulation. The proposed method eliminates the need for circulating current reference calculation and control parameter tuning. Simulation results demonstrate that the proposed method takes approximately 17.5 s and 39 s for inter-arm and inter-phase SOC balancing, respectively, while traditional three-level SOC balancing takes approximately 42 s and 88 s.
This paper presents a cooperative target defense strategy using nonlinearmodelpredictivecontrol (NMPC) for a three-agent target-attacker-defender (TAD) game. The TAD game consists of an attacker and a cooperative t...
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This paper presents a cooperative target defense strategy using nonlinearmodelpredictivecontrol (NMPC) for a three-agent target-attacker-defender (TAD) game. The TAD game consists of an attacker and a cooperative target-defender pair. The attacker's objective is to capture the target, whereas the target-defender team acts together such that the defender can intercept the attacker and ensure target survival. We assume that the cooperative target-defender pair do not have perfect knowledge of the attacker states;hence, they are estimated using an extended Kalman filter (EKF). NMPC is used to determine the optimal control commands for the target and the defender. A capture analysis based on the Apollonius circles was performed to identify the target survival regions. The findings from the analysis and the efficacy of the NMPC-based solution were evaluated through extensive numerical simulations. The results verified the escape and capture regions obtained through the analysis and showed that the NMPC-based solution offers robustness to the different unknown attacker models and performs better than the CLOS and A-CLOS based strategies.
In industrial process control, basic controllers such as linear PI(D) control and cascade controllers are widely accepted by the plant operators because they are easy to understand due to their clear cause-and-effect ...
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In industrial process control, basic controllers such as linear PI(D) control and cascade controllers are widely accepted by the plant operators because they are easy to understand due to their clear cause-and-effect relation. When the control and performance objectives cannot be met by simple controllers and manual interventions, advanced process control (APC) solutions, usually MPC controllers are employed. The design of APC solutions requires a considerable understanding of the process and more mathematical background-but also during the operational phase (e.g. error diagnosis). Then the issue of the trust of the operators into such complex control solutions and in their ability to monitor their behavior during plant operation arises. It may happen that APC solutions are not accepted or are fully or partly switched off by the operators after a while, which means a wasted effort and lost opportunities for better plant performance. By providing carefully chosen information about the behavior of the controller and well-designed operator interfaces, the trust of the operators and their ability to monitor advanced controllers can be increased. In this contribution we present an environment to investigate trust in automation experimentally and to explore new opportunities for the user-interaction with APC methods. The test environment consists of a chemical process simulator which is controlled online by a NMPC scheme, an interface editor for the fast development of interface concepts as well as a control room simulation in order to evaluate the synergetic cooperation between the operator and the algorithmic control schemes. (C) 2016, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.
For the widespread application of nonlinear model-predictive control (NMPC) in the chemical industry, the computational effort that is required for the solution of the underlying resulting nonlinear dynamic optimizati...
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ISBN:
(纸本)9781509007554
For the widespread application of nonlinear model-predictive control (NMPC) in the chemical industry, the computational effort that is required for the solution of the underlying resulting nonlinear dynamic optimization problems is a major obstacle. For complex process models and long prediction and control horizons, the computation times lead to large sampling times and feedback delays, which may cause performance deterioration and constraint violations. A promising approach to speed up the computations has been proposed in [1]. In this approach, two main ideas are put forward: On the one hand, the structure of the quadratic programming (QP) subproblems that result from using Sequential Quadratic Programming for the solution of the NLP problems is exploited, and different approximations of the exact solution are used in a parallel and hierarchical fashion (multi-level iterations). On the other hand, in each iteration of the algorithm the QP problems can be divided into a preparation phase based on the previous solution and a feedback phase based on the current state of the process (real-time iterations). In the present work we employ parallel computation to investigate the effects of computational delays and realize the parallel execution of two different approximations of the multi-level modes to reduce the computational burden. We use a benchmark semi-batch polymerization reactor model to investigate and to compare the control performances for an NMPC scheme with an economically motivated cost function using a standard implementation of an online optimizing control scheme, a scheme using real-time iterations and a scheme using multi-level iterations in a parallelized and interleaved fashion.
This contribution presents two real-time capable nonlinear model-predictive control (NMPC) approaches for an electro-pneumatic clutch for heavy trucks: a centralized control approach and a cascaded one. A clutch is ne...
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This contribution presents two real-time capable nonlinear model-predictive control (NMPC) approaches for an electro-pneumatic clutch for heavy trucks: a centralized control approach and a cascaded one. A clutch is necessary at start-up or during gear shifts to connect or disconnect the combustion engine and the gear box. This automated actuator disburdens the driver and provides the necessary actuation force according to the large torque typically transmitted through the drive train. The force characteristic of the clutch, however, is subject to hysteresis, which is described by a generalized Bouc-Wen model and used for a feedforward hysteresis compensation in the control algorithm. The proposed NMPC-algorithm involves (i) a minimization of the difference between the desired and predicted state vector at the end of the prediction horizon and (ii) flatness-based techniques to compute desired trajectories for the complete state vector as well as the control input. The optimal control is given by an additional, minimum-norm control input that minimizes the difference between the predicted state vector and the desired one at the end of the prediction horizon. Thereby, the computation effort of the NMPC approaches can be kept relatively small, and a real-time evaluation becomes possible. A reduced-order observer estimates an effective pressure in the clutch that also accounts for an uncertain disturbance force. Thereby, a disturbance compensation and a high tracking accuracy is achievable for the piston position as controlled variable. The efficiency of the two proposed control structures is emphasized by experimental results from a dedicated test rig.
A nonlinear model-predictive control (NMPC) is demonstrated for nonlinear systems using an improved fuzzy switching law. The proposed moving average filter fuzzy switching law (MAFFSL) is composed of a quasi-ARX radia...
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ISBN:
(纸本)9781479964871
A nonlinear model-predictive control (NMPC) is demonstrated for nonlinear systems using an improved fuzzy switching law. The proposed moving average filter fuzzy switching law (MAFFSL) is composed of a quasi-ARX radial basis function neural network (RBFNN) prediction model and a fuzzy switching law. An adaptive controller is designed based on a NMPC. a MAFFSL is constructed based on the system switching criterion function which is better than the (ON/OFF) switching law and a RBFNN is used to replace the neural network (NN) in the quasi-ARX black box model which is understood in terms of parameters and is not an absolute black box model, in comparison with NN. The proposed controller performance is verified through numerical simulations to demonstrate the effectiveness of the proposed method.
This paper proposes an optimal approach for state estimation based on the Takagi-Sugeno (TS) Kalman filter using measurement sensors and rough pose obtained from LIDAR scan end-points matching. To obtain stable and op...
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This paper proposes an optimal approach for state estimation based on the Takagi-Sugeno (TS) Kalman filter using measurement sensors and rough pose obtained from LIDAR scan end-points matching. To obtain stable and optimal TS Kalman gain for estimator design, a linear matrix inequality (LMI) is optimized which is constructed from Lyapunov stability criteria and dual linear quadratic regulator (LQR). The technique utilizes a Takagi-Sugeno (TS) representation of the system, which allows modeling the complex nonlinear dynamics in such a way that linearization is not required for the estimator or controller design. In addition, the TS fuzzy representation is exploited to obtain a real-time Kalman gain, avoiding the expensive optimization of LMIs at every step. The estimation schema is integrated with a nonlinear model-predictive control (NMPC) that is in charge of controlling the vehicle. For the demonstration, the approach is tested in the simulation, and for practical validity, a small-scale autonomous car is used.
In a previous work, multi-stage NMPC and tube-based NMPC schemes were combined into a single framework called tube-enhanced multi-stage NMPC with the goal of achieving an improved trade-off between simplicity and perf...
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In a previous work, multi-stage NMPC and tube-based NMPC schemes were combined into a single framework called tube-enhanced multi-stage NMPC with the goal of achieving an improved trade-off between simplicity and performance. In tube-enhanced multistage NMPC, the large uncertainties are handled using a multi-stage primary controller and the small uncertainties are handled using a multi-stage ancillary controller that tracks the predictions of the primary controller. In this work, we propose the replacement of the multistage ancillary controller by a single scenario NMPC that tracks the predicted trajectories of one of the scenarios of the multi-stage primary controller. The scenario that will be tracked by the ancillary controller as well as the ancillary controller model are time varying and are adapted to the current plant dynamics. The benefits of the new formulation are demonstrated on the benchmark Williams-Otto Continuous Stirred Tank Reactor (CSTR) example. Copyright (C) 2020 The Authors.
In a previous work, multi-stage NMPC and tube-based NMPC schemes were combined into a single framework called tube-enhanced multi-stage NMPC with the goal of achieving an improved trade-off between simplicity and perf...
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In a previous work, multi-stage NMPC and tube-based NMPC schemes were combined into a single framework called tube-enhanced multi-stage NMPC with the goal of achieving an improved trade-off between simplicity and performance. In tube-enhanced multistage NMPC, the large uncertainties are handled using a multi-stage primary controller and the small uncertainties are handled using a multi-stage ancillary controller that tracks the predictions of the primary controller. In this work, we propose the replacement of the multistage ancillary controller by a single scenario NMPC that tracks the predicted trajectories of one of the scenarios of the multi-stage primary controller. The scenario that will be tracked by the ancillary controller as well as the ancillary controller model are time varying and are adapted to the current plant dynamics. The benefits of the new formulation are demonstrated on the benchmark Williams-Otto Continuous Stirred Tank Reactor (CSTR) example.
In this paper, NMPC schemes based on fast update methods (fast NMPC schemes) are reviewed that strive to provide a fast but typically suboptimal update of the control variables at each sampling instant with negligible...
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In this paper, NMPC schemes based on fast update methods (fast NMPC schemes) are reviewed that strive to provide a fast but typically suboptimal update of the control variables at each sampling instant with negligible computational delay. The review focuses on schemes that employ one of two subclasses of fast update methods developed for direct solution approaches, the suboptimal update methods and the sensitivity-based update methods. The connections and similarities of the fast update methods, the elements of the fast NMPC, the control architecture as well as the fast NMPC schemes as a whole are highlighted to support the assessment of the benefits and limitations of each individual scheme. In this way, this review facilitates the choice of a suitable fast NMPC scheme within the vast amount of fast NMPC schemes available in literature. (C) 2016 Elsevier Ltd. All rights reserved.
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