Engineering Commons^™

Robust Predictive Control Of Nonplanar Fully-Actuated Uavs, Yun Ma, Yuan Wang, Meng Li, Peng Wang, Yanling Tang

Journal of System Simulation

Abstract: Targeting the problem that nonplanar fully-actuated unmanned aerial vehicles (UAVs) are susceptible to external winds and unmodeled dynamics, the predictive control system with good robustness is designed. A nonlinear motion model with six degrees of freedom is established through the Newton-Euler approach. A linear extended state observer is designed to estimate the state variables by transforming the system affected by matched and unmatched disturbances into an equivalent system only affected by the matched disturbances. A predictive controller is designed for the equivalent system to reduce the output oscillation and input surging and a disturbance compensator is also designed to …

Reinforcement-Learning-Based Adaptive Tracking Control For A Space Continuum Robot Based On Reinforcement Learning, Da Jiang, Zhiqin Cai, Zhongzhen Liu, Haijun Peng, Zhigang Wu

Journal of System Simulation

Abstract: Aiming at the tracking control for three-arm space continuum robot in space active debris removal manipulation, an adaptive sliding mode control algorithm based on deep reinforcement learning is proposed. Through BP network, a data-driven dynamic model is developed as the predictive model to guide the reinforcement learning to adjust the sliding mode controller's parameters online, and finally realize a real-time tracking control. Simulation results show that the proposed data-driven predictive model can accurately predict the robot's dynamic characteristics with the relative error within ±1% to random trajectories. Compared with the fixed-parameter sliding mode controller, the proposed adaptive controller …

Performance Improvement Of Induction Motor Drives With Model-Based Predictive Torque Control, Fati̇h Korkmaz

Turkish Journal of Electrical Engineering and Computer Sciences

One of the most important advantages of using modeling and simulation software in design and control engineering is the ability to predict system behavior within specified conditions. This paper presents a novel error vector-based control algorithm that aims to reduce torque ripples predicting flux and torque errors in a conventional vector-controlled induction motor. For this purpose, a new control model has been developed that envisages flux change by applying probabilistic space vectors' torque and flux control. In the proposed predictive control algorithm, flux and torque errors are calculated for each candidate voltage vector. Thus, the optimal output voltage vector that …

Simplified Model Predictive Current Control Of Non-Sinusoidal Low Power Brushlessdc Machines, Alireza Lahooti Eshkevari, Hossein Torkaman

Turkish Journal of Electrical Engineering and Computer Sciences

Several strategies have been proposed to control nonsinusoidal brushless DC machines (BLDCMs). However, high electromagnetic torque ripple and current overshoots occur in commutation times, which are significant problems of those strategies such as for hysteresis current controllers. This paper proposes a model predictive strategy to solve the above issues. It is simple and straightforward. Moreover, it reduces the motor torque ripple significantly and improves the response rate of the control system to the load torque variation in comparison with the conventional technique. The torque varies smoothly, and the performance of the system at commutation time is improved by eliminating the …

Data Driven Pre Tuning Adaptive Subspace Model Predictive Control, Han Pu, Liu Miao, Jia Hao

Journal of System Simulation

Abstract: The problem of predictive control is investigated for power plant superheated steam temperature system with the characteristics of large delay, large inertia and time-varying. The data driven pre tuning adaptive subspace model predictive control (PTA-MPC) method, which combines the advantages of subspace identification and state space predictive control, is proposed. The state space models of multiple conditions are obtained by subspace identification with the input signal in persistent excitation. The predictive control law is derived with the state space models, and the controller parameters are optimized by using particle swarm optimization (PSO) algorithm. Based on the least square parameter …

Sensorless Second-Order Switching Surface For A Three-Level Boost Converter, Tarek Messikh, Nasrudin Abd Rahim, Saad Mekhilef

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposes a sensorless second-order switching surface to control a three-level boost converter (TLBC). A predictive current method is proposed to reduce the number of sensors in the normal second-order switching surface method. Based on a developed model of the TLBC, the current is estimated and a switching surface is formulated in the state-energy plane. Simulation and hardware tests are carried out to verify the viability and the effectiveness of the proposed control technique. Results obtained show a good performance of the converter in term of DC-bus balancing and fast dynamic response under sudden load change.

Supervised Learning-Based Explicit Nonlinear Model Predictive Control And Unknown Input Estimation In Biomedical Systems, Ankush Chakrabarty

Open Access Dissertations

Application of nonlinear control theory to biomedical systems involves tackling some unique and challenging problems. The mathematical models that describe biomedical systems are typically large and nonlinear. In addition, biological systems exhibit dynamics which are not reflected in the model (so-called 'un-modeled dynamics') and hard constraints on the states and control actions, which exacerbate the difficulties in designing model-based controllers or observers.

This thesis investigates the design of scalable fast explicit nonlinear model predictive controllers (ENMPCs). The design involves (i) the estimation of a feasible region using Lyapunov stability methods and support vector machines; and (ii) within the estimated feasible …

Predictive Control Of A Constrained Pressure And Level System, Erkan Kaplanoğlu, Taner Arsan, Hüseyi̇n Selçuk Varol

Turkish Journal of Electrical Engineering and Computer Sciences

The focus of this paper is the implementation of a constrained predictive control algorithm implemented in Multi-Parametric Toolbox (MPT), which is a free MATLAB toolbox for design, analysis, and implementation of controllers for constrained linear, nonlinear, and hybrid systems. In general, MPT is used for modeling systems offline. The novelty of this study is that real-time mode MPT is used in process control. We also combined the Model Predictive Control Toolbox with MPT. This novel controller is considered a real-time controller of level-pressure systems. In this study, a special type of model predictive control algorithm, the constrained continuous-time generalized control, …

Predictive Congestion Control Mac Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Available congestion control schemes, for example transport control protocol (TCP), when applied to wireless networks results in a large number of packet drops, unfairness with a significant amount of wasted energy due to retransmissions. To fully utilize the hop by hop feedback information, a suite of novel, decentralized, predictive congestion control schemes are proposed for wireless sensor networks in concert with distributed power control (DPC). Besides providing energy efficient solution, embedded channel estimator in DPC predicts the channel quality. By using the channel quality and node queue utilizations, the onset of network congestion is predicted and congestion control is initiated. …