Efficient Predictive Control Research Articles

Efficient Predictive Control for Dual Three-Phase PMSM Drive With Vector Transformation

Efficient Predictive Control for Dual Three-Phase PMSM Drive With Vector Transformation

Efficient predictive control method for ORC waste heat recovery system based on recurrent neural network

The model predictive control performs well in regulating operating parameters and ensuring system safety when the organic Rankine cycles are operating with variable heat sources. However, traditional nonlinear predictive control based on the organic Rankine cycle mechanism model involves significant computational complexity, making it challenging to quickly find a control solution. This limitation hinders its application in the organic Rankine cycle for rapid response control. To address this issue, a fast model predictive control method is proposed in this work. A recurrent neural network model is well-trained using the input–output data of the organic Rankine cycle process, and it is used as a surrogate model in the design of the model predictive controller for the control of organic Rankine cycle operating parameters. The formulated optimal control problem is then transformed into a mixed integer linear programming problem, which can obtain high-quality and fast solutions during the control process. Through comparison with recurrent neural network-based nonlinear predictive control and pseudo-sequential method-based fast nonlinear predictive control, the results show that the designed controller can effectively accomplish the control of organic Rankine cycle operating parameters with smaller overshoot. Moreover, its average control solution time is shorter by 89.59% and 93.27% respectively while the total net output power of the system during the control process is 0.54% and 1.3% higher than that of the other two controllers. It exhibits superior control performance, even under variable waste heat conditions.

Reduced-order Koopman modeling and predictive control of nonlinear processes

In this paper, we propose an efficient data-driven predictive control approach for general nonlinear processes based on a reduced-order Koopman operator. A Kalman-based sparse identification of nonlinear dynamics method is employed to select lifting functions for Koopman identification. The selected lifting functions are used to project the original nonlinear state–space into a higher-dimensional linear function space, in which Koopman-based linear models can be constructed for the underlying nonlinear process. To curb the significant increase in the dimensionality of the resulting full-order Koopman models caused by the use of lifting functions, we propose a reduced-order Koopman modeling approach based on proper orthogonal decomposition. A computationally efficient linear robust predictive control scheme is established based on the reduced-order Koopman model. A case study on a benchmark chemical process is conducted to illustrate the effectiveness of the proposed method. Comprehensive comparisons are conducted to demonstrate the advantage of the proposed method.

Generalized efficient robust predictive control for networked interval type-2 T–S fuzzy system with adaptive event-triggered scheme

This article studies the generalized efficient robust predictive control (GERPC) for the nonlinear systems denoted by interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy model over networks with consideration of packet dropout, bounded disturbance and adaptive event-triggered scheme (AETS). First, the packet dropout is described by a Bernoulli process, and an AETS with thresholds that can be self-regulated according to different situations is employed to decide whether to release data into the communication network, so as to reduce the data transmission frequency and the network burden; Second, taking into consideration of packet dropout and AETS, a unified IT2 T–S fuzzy control model based on GERPC is established, and the stability of closed-loop system with bounded disturbance is guaranteed by quadratic boundedness (QB) technique; Third, generalized efficient robust predictive controller is designed to obtain a larger initial feasible region, smaller online computation and acceptable control performance. The unconstrained control law is designed offline, and a new variable will be optimized online to achieve good control performance, which is introduced to adjust the control quantity. Then, the convex LMI reformulation method is utilized to handle non-convex terms in optimization problem, and the effectiveness of the GERPC algorithm is proved by the matrix partition technique. At last, the effectiveness of the designed controller based on the GERPC strategy is verified by a given simulation experiment.

Simple Predictive Current Control of Asymmetrical Six-Phase Induction Motor With Improved Performance

Despite the intensive research work on the application of model predictive control (MPC) for multi-phase machines, there are many challenges still to be handled such as high circulating currents, variable switching frequency, and high computation burden. This paper proposes a simple, yet efficient predictive current control (PCC) for six-phase induction motor (IM) that reduces considerably circulating current, computation cost, and switching frequency. In the proposed method, a group of four candidate voltage vectors (VVs) is formed in each control sample. Unlike similar methods reported in literature, these candidate vectors are generated based on a simple lookup table and the previous optimal VV. The lookup table is designed such that it allows only one commutation in each control sample. The performance of the proposed method is assessed experimentally using a one kW asymmetrical six-phase induction motor (A6PIM). Different performance indices are investigated to compare the proposed method against two different PCC methods in literature at different operating conditions. The experimental results confirm that about 50% reduction in average switching frequency and 15% in current total harmonic distortion at different speed ranges are observed with the proposed method compared to the conventional one.

Computationally Efficient Predictive Current Control With Finite Set Extension Using Derivative Projection for IM Drives

This article proposes a computationally efficient predictive current control (PCC) combined with an extension of a finite set using current derivative projection, to solve the challenges experienced by finite control set PCC (FCS-PCC), that is, unsatisfied steady-state performance and high computational burden. Since only one switching sequence is utilized in the entire sampling period, FCS-PCC is inevitably penalized by the high torque and current deviations. More specifically, FCS-PCC suffers from the high computational burden caused by the exhaustive search in the optimization stage. To tackle the aforementioned issues, a reformulated objective function using current derivative projection with least-squares (LSs) optimization in PCC is presented in this work. First, PCC is geometrically described as a quadratic programming (QP) problem. To minimize the deviation between the selected and desired current derivative, the objective function is rearranged as the quadratic Euclidean norm of the derivative deviation. The exhaustive search in the optimization stage is avoided by a preselection principle. Based on the above, the optimal stator current derivatives in the consecutive sampling intervals combined with their duty cycles are optimized by the LS method. The effectiveness of the proposed method is verified by the experimental results based on a 2.2-kW IM drive platform.

Computationally Efficient Model-Free Predictive Control of Zero-Sequence Current in Dual Inverter Fed Induction Motor

A model-free predictive zero-sequence current (ZSC) control is presented in this article for the dual-inverter-fed open-end winding induction motor (OEWIM). The proposed method is performed in a cascaded finite set predictive control approach to decrease the computational burden. In the proposed method, a state observer estimates the ZSC and its disturbances. The estimated ZSC is suppressed by a disturbance rejecting control loop that produces the reference of common-mode voltage (CMV). As a benefit, the motor parameters have not appeared in this process, which improves the robustness of the whole control system. To reduce the computational burden, the model-free predictive control (MFPC) is implemented in two cascaded stages. First, the predictive algorithm is performed for the voltage vectors (VVs) with zero CMV to detect the optimum zone of the space vector locations. In the second stage, the proposed MFPC is implemented with the VVs of the selected zone, and the optimum VV is achieved. In this way, the predictive algorithm is iterated 13 times. On the contrary, the conventional predictive algorithm is iterated 27 times for the VVs of the dual inverter. So, the calculation of the proposed method is reduced almost 50%. This feature is achieved without neglecting any of the VVs. Therefore, the performance of the proposed method is not degraded by reducing the calculations. A comparative analysis is carried out through simulations and experimental tests. The results confirm that while the proposed MFPC method is computationally efficient, its performance and robustness are also improved.

Computationally efficient predictive torque control strategies without weighting factors

Predictive torque control (PTC) is a promising control method for electric machines due to its simplicity, fast dynamics, ability to handle nonlinearities, and easy inclusion of additional control objectives. The main challenge in conventional PTC design is to determine the weighting factors in the cost function. These weighting factors are generally chosen by the trial-and-error method or metaheuristic optimization algorithms, but these methods may not apply the optimum voltage vectors according to changing operating conditions. There are also several studies on the elimination of the weighting factors. This paper proposes two weighting factorless PTC strategies with lower computational complexities than the current literature. To demonstrate the superiority of the proposed methods, their performances are experimentally compared to those of the existing methods through a test bed equipped with an induction motor. Finally, two PTC strategies with a simple design and improved performance are introduced to the literature.

A novel approach to PFC for nonlinear systems

This paper proposes a computationally efficient predictive control law for non-linear systems, that is one that can easily be coded and implemented on low cost hardware. Moreover, it has a secondary core benefit that the core tuning parameter reduces to a single choice which is: how much faster than open-loop would you like the closed-loop to converge? Conceptually the approach builds on the PFC approach but proposes a very different type of coincidence condition which removes the lag associated to the conventional approach. Simulations demonstrate that for some non-linear systems this is a cheap and simple way of ensuring effective feedback, with constraint handling.

Computationally efficient predictive torque control for induction motor drives based on flux positional errors and extended Kalman filter

This paper proposes an improved model based predictive torque control (MPTC) method based on positional error between reference and estimated stator flux vectors. The main advantages of the proposed method are: the improved computational efficiency which requires minimum hardware resources and weighting-factor-free cost function. Weighting factor is removed by using modified reference transformation, which converts torque reference into equivalent stator flux reference. Improvement in computational performance is achieved by using decreased number of voltage vectors for prediction. An admissibility criterion based on flux positional errors is introduced to reduce the number of voltage vectors. The computational time saved is utilised to incorporate extended Kalman filter for better estimation of flux and torque. The validity of the proposed method is tested on a two-level three-phase inverter fed induction motor drive with dSpace DS1104 as controller board. The dynamic response and computational cost of the proposed method is compared to other established MPTC methods. The superiority of the proposed technique is confirmed by experimental results, which show an average of 32% reduction in computational time when compared to conventional MPTC while comparable dynamic response in terms of torque ripple, flux ripple and load current harmonics, is also maintained.

A predictive approach to adaptive fuzzy sliding-mode control of under-actuated nonlinear systems with input saturation

In this paper, a computationally efficient robust predictive control method is proposed for continuous-time under-actuated SISO systems in the presence of actuator saturation and state-dependent uncertainties. The proposition of this research is to employ the idea of model prediction together with the Adaptive Fuzzy Sliding-Mode Control (AFSMC) for tuning the sliding surface parameters by predicting the anticipated effects of uncertainties. In the proposed scheme, only after the trigger conditions are met, the coefficients of the sliding surface are updated and the AFSMC is applied. Hence, computational complexity can be controlled by adjusting the switching rule. In the AFSMC, a fuzzy system is used to approximate a nonlinear function, and a robust term to compensate for any possible mismatches. An adaptively tuned gain is also applied to the control signal to prevent instability caused by the actuator saturation. Based on the updating sliding surface, fuzzy singletons, the upper bound of the fuzzy approximation error, and the saturation gain are adaptively tuned. Closed-loop stability is shown to be guaranteed using the multiple Lyapunov functions theorem and the Barbalat’s lemma. Finally, the method is applied for the depth control of an Autonomous Underwater Vehicle (AUV), depicting the excellent performance of the proposed method.

Predictor-Based Neural Network Finite-Set Predictive Control for Modular Multilevel Converter

This letter investigates the possibility of deploying a novel finite control-set model predictive control solution for solving the ongoing research challenges in predictive control regulated modular multilevel converter, i.e., model parameter sensitiveness and excessive computational burden as well as weighting factors selection. Specifically, it is realized by cascading a predictor-based neural network design, which enables a smooth and fast identification of system dynamics, and a computationally efficient finite-set predictive control, which is responsible for simplifying the rolling optimization and reducing the computational complexity. The main contribution of the proposed methodology relies on the fact that no knowledge of any model parameters and weighting factors in whole control process are required, which leads to a significant enhancement in the robustness and reliability of the control system in the presence of parametric uncertainties, while remaining computationally feasible. Finally, the stability analysis is given, and the proposed methodology is experimentally assessed for modular multilevel converter, where steady-state and transient-state performance tests confirm the interest of the proposal.

An Efficient Iterative Learning Predictive Functional Control for Nonlinear Batch Processes.

Iterative learning model-predictive control (ILMPC) is very popular in controlling the batch process since it possesses not only the learning ability along batches but also the strong time-domain tracking properties. However, for a fast batch process with strong nonlinear dynamics, the application of the ILMPC is challenging due to the difficulty in balancing the computational efficiency and tracking accuracy. In this article, an efficient iterative learning predictive functional control (ILPFC) is proposed. The original nonlinear system is linearized along the reference trajectory to derive a 2-D tracking-error predictive model. The linearization error is compensated by utilizing the Lipschitz condition so that the objective function can be formulated with the upper bound of the actual tracking error. For enhancing control efficiency, predictive functional control (PFC) is applied in the time domain, which reduces the dimension of the decision variable in order to effectively cut down the computational burden. The stability and convergence of this ILPFC with terminal constraint are analyzed theoretically. Simulations on an unmanned ground vehicle and a typical fast batch reactor verify the effectiveness of the proposed control algorithm.

An Efficient Robust Predictive Control of Main Steam Temperature of Coal-Fired Power Plant

Regulating performance of the main steam temperature (MST) system concerns the economy and safety of the coal-fired power plant (CFPP). This paper develops an offset-free offline robust model predictive control (RMPC) strategy for the MST system of CFPP. Zonotope-type uncertain model is utilized as the prediction model in the proposed RMPC design owing to its features of higher accuracy, compactness of representation and less complexity. An offline RMPC aiming at the system robustness and computational efficiency is then developed to maintain the desired steam temperature in case of wide operating condition change. The proposed RMPC is realized by two stages: in the first stage, the RMPC law set, which is the piecewise affine (PWA) of the MST system state is designed offline; then in the second stage, the explicit control law is selected online according to the current state. To achieve an offset-free tracking performance, a manipulated variable target observer is employed to update the chosen RMPC law. The control simulations using on-site operating data of a 1000 MW ultra-supercritical power plant show that the proposed approach can achieve satisfactory control performance and online computation efficiency even under complicated operating conditions.

Prefrontal engagement during sequential manual actions in children at early adolescence compared with adults

In everyday behavior, we perform numerous goal-directed manual tasks that contain a sequence of actions. However, knowledge is limited regarding developmental aspects of predictive control mechanisms in such tasks, particularly with regard to brain activations supporting sequential manual actions in children. We investigated these issues in typically developing children at early adolescence (11–14 years) compared with previously collected data from adults. While lying in a magnetic resonance imaging (MRI) scanner, the participants steered a cursor on a computer screen towards sequentially presented targets using a hand-held manipulandum. The next target was either revealed after completion of the ongoing target (one-target condition), in which case forthcoming movements could not be planned ahead, or displayed in advance (two-target condition), which allowed the use of a predictive control strategy. The adults completed more targets in the two- than one-target condition, displaying an efficient predictive control strategy. The children, in contrast, completed fewer targets in the two- than one-target condition, and difficulties implementing a predictive strategy were found due to a limited capacity to inhibit premature movements. Brain areas with increased activation in children, compared with the adults, included prefrontal and posterior parietal regions, suggesting an increased demand for higher-level cognitive processing in the children due to inhibitory challenges. Thus, regarding predictive mechanisms during sequential manual tasks, crucial development likely occurs beyond early adolescence. This is at a later age than what has previously been reported from other manual tasks, suggesting that predictive phase transitions are difficult to master.

Efficient predictive cruise control of autonomous vehicles with improving ride comfort and safety

In this paper, the adaptive cruise control problem of autonomous vehicles is considered and we propose a novel predictive cruise control approach to improve driving safety and comfort of the host vehicle. The main idea of the approach is that the predicted acceleration commands of the host vehicle are stair-likely pre-planned to satisfy their changes along the same direction within the prediction horizon. The predictive cruise controller is then computed by online solving a finite horizon constrained optimal control problem with a decision variable. Besides explicitly handling safety constraints of vehicles, the obtained controller has abilities to efficiently attenuate peaks of the cruise commands while reducing computational load of online solving the optimization problem. Hence, the ride comfort and safety performances of vehicles are improved in terms of softening acceleration response and constraint satisfaction. Moreover, the ride comfort, following and safety performances of vehicles are summed with varying weights to cope with various traffic scenarios. Some classical cases are adopted to evaluate the proposed adaptive cruise control algorithm in terms of ride comfort, car-following ability and computational demand.

A Computationally Efficient Predictive Cruise Control for Automated Electric Vehicles

This paper proposes an energy-efficient predictive cruise control (PCC) system to cope with range anxiety of automated electric vehicles. The proposed approach is formulated as an optimal control problem to realize better energy efficiency and ensure safe inter-vehicle distance. To improve computational efficiency, a fast algorithm combining Gauss pseudospectral method (GPM) and moving horizon control (MHC) is introduced to solve this nonlinear optimal problem. The comparative simulation results reveal that the energy economy of the PCC system is improved about 4.1%, and its computation time is reduced compared with the Euler method while ensuing the same accuracy.

Constrained computationally efficient nonlinear predictive control of Solid Oxide Fuel Cell: Tuning, feasibility and performance

Control of Solid Oxide Fuel Cells (SOFCs) is a challenging task since they are nonlinear dynamic systems and it is essential to precisely satisfy the existing technological constraints which must be imposed on the manipulated variable (the fuel flow) and on fuel utilisation. This paper details a constrained computationally efficient nonlinear Model Predictive Control (MPC) algorithm for the SOFC process. The predicted voltage and fuel utilisation trajectories are successively linearised on-line which leads to a simple to solve quadratic optimisation MPC problem. The emphasis is put on three aspects: (a) selection of tuning parameters, (b) feasibility of the constrained MPC optimisation problem, (c) good control quality and low computational burden. At first, tuning is thoroughly described. It is demonstrated that soft fuel utilisation constraints lead to feasible MPC optimisation. It is shown that control accuracy and constraints’ satisfaction ability of the algorithm are very similar to those of the “ideal” MPC strategy with nonlinear on-line optimisation, but its computational burden is much lower. Finally, it is shown that the algorithm is much more precise than the simple MPC algorithm with successive on-line model linearisation and the classical MPC algorithm based on a parameter-constant linear model.

A synthesis approach of fast robust MPC with RBF-ARX model to nonlinear system with uncertain steady status information

The mechanical model of a plant in real industry is usually difficult to obtain. This paper integrates the data-driven RBF-ARX modeling method and a fast Robust Model Predictive Control (RMPC) approach to achieving output-tracking control of a nonlinear system with unknown steady status information. Considering the large online computational burden of online RMPC, this paper proposes a RBF-ARX model-based efficient robust predictive control (RBF-ARX-ERPC) approach. First, based on the RBF-ARX model, a polytopic uncertain linear parameter varying (LPV) state-space model is built to represent the dynamic behavior of the system; next, two convex polytopic sets are constructed to wrap the globally nonlinear behavior of the system. Then, an optimization problem including several linear matrix inequalities (LMIs) is formulated, which is solved offline to synthesize a sequence of explicit control laws corresponding to a sequence of asymptotically stable invariant ellipsoids in the state space, of which all the optimization results are stored in a look-up table. For the real-time control online, it only involves simple state-vector computation and bisection search. Two simulation examples, i.e. the modeling and control of a widely used continuously stirred tank reactor (CSTR) and a linear one-stage inverted pendulum (LOSIP) system, and the real-time control experiments on an actual LOSIP plant are provided to demonstrate the effectiveness of the proposed RBF-ARX model-based efficient RPC approach.

Efficient Predictive Torque Control for Induction Motor Drive

Finite control set model predictive control is an emerging technique for the control of power electronic converters. This paper introduces a simple and efficient predictive torque control (PTC) algorithm for induction motor drive. The proposed technique eliminates the need for flux-weighting factor for the conventional PTC. As a result, tedious offline tuning is no longer required. At the same time, the proposed technique ensures that the relative importance between torque and flux ripples is determined in an online fashion. Moreover, unlike the conventional method that needs to evaluate the cost function seven times (for two-level three-phase inverter case), the proposed method needs only to test four voltage vectors at each control sample which leads to a significant reduction in the computation time and switching frequency without sacrificing the performance. The effectiveness of the proposed method is demonstrated by both simulation and experimental results with comprehensive comparisons with the reported literature.