Multivariable Predictive Control Based on Neural Network Model and Simplex-Evolutionary Hybrid Optimization

Batch to batch temperature control of a semi-batch chemical reactor with heating/cooling system was discussed in this study. Without extensive modeling investigations, a two-dimensional (2D) general predictive iterative learning control (2D-MGPILC) strategy based on the multi-model with time-varying weights was introduced for optimizing the tracking performance of desired temperature profile. This strategy was modeled based on an iterative learning control (ILC) algorithm for a 2D system and designed in the generalized predictive control (GPC) framework. Firstly, a multi-model structure with time-varying weights was developed to describe the complex operation of a general semi-batch reactor. Secondly, the 2D-MGPILC algorithm was proposed to optimize simultaneously the dynamic performance along the time and batch axes. Finally, simulation for the controller design of a semi-batch reactor with multiple reactions was involved to demonstrate that the satisfactory performance could be achieved despite of the repetitive or non-repetitive disturbances.

The hydroelectric energy is one of the most important renewable energy in the world. It does not encounter the problem of population displacement and is not as expensive as solar or wind energy. However, the hydro electrical generating units are usually isolated from the grid network; thus, they require control to maintain of constant the power for any working conditions. The simulation model of hydropower plant was constructed based on mathematical equations that summarize the behavior of the hydropower plant. The simulation model of power plant is useful in stability studies. This paper, presents the approach of Generalized Predictive Control (GPC) is applied to a multivariable model of the part turbine/generator of hydropower plant. In this study, the standard multivariable (GPC) algorithm is presented. It is then applied to achieve sets points tracking of the outputs of the plant. A Multi Input Multi Output (MIMO) model is used for control purposes. A comparative study is carried out using the named controller’s multivariable Linear Quadratic Gaussian (LQG) and multivariable (GPC) Controls. The performance of the proposed controller is illustrated by a simulation example of hydropower plant. Encouraging results are obtained that motivate for further investigations.

This paper details a voltage control strategy based on Generalized Predictive Control (GPC) applied to a single-phase T-type neutral point clamped voltage source inverter in stand-alone operation mode. A performance comparison between the GPC and a Proportional-Integral (PI) controller is presented to highlight the improved performance of GPC under different load scenarios. The main results show that the GPC can achieve longer prediction horizons than other controllers based on model predictive control. Furthermore, in contrast to the PI controller, the GPC responds to load changes as disturbances. Finally, GPC can operate efficiently without an online optimization method nor recursive system identification method.

This study presents a hybrid method based on generalised predictive control (GPC) and a proposed new hybrid shuffled frog leaping (NHSFL) algorithm to design stabilising signals to damp the multi-machine power system low-frequency oscillations. A linearised model predictive controller based on GPC is designed in which the proposed NHSFL algorithm is employed for optimising the cost function of the GPC. The numerical results are presented on a two-area four-machine and a five-area 16-machine power system. The effectiveness of the designed controllers is shown by considering various operating conditions. The proposed approach, which is called as GPC-NHSFL, is compared with a classical-based method, GPC algorithm and GPC-based standard SFL algorithm (GPC-SFL). The simulation results show the superiority and capability of the proposed approach to enhance power systems damping.

In the complex heating systems, higher order, large inertia, large delay and uncertainty typically exist. This work introduces generalized predictive PID control algorithm to adjust the system. The purpose of the control algorithm is to achieve a steady state in a shorter time. On the basis of analyzing PID control algorithm and Generalized Predictive Control (GPC), configuring a given performance function index control-weighted sequences to GPC control. Derived form of generalized predictive control algorithm with PID. The Matlab simulation shows that using generalized predictive PID control algorithm can produce better results in shorter time than simple using PID or GPC in the heating system.

The paper considers the discrete time-invariant linear systems affected by input disturbances and construct the explicit description of the constrained generalized predictive control (GPC) law taking in account the constraints existence from the design stage. The explicit formulation of the predictive controllers gives a useful insight on the closed loop capabilities. The GPC is a special case of model predictive control (MPC) whose explicit formulation is known to be a piecewise affine function of state. As novelty the present work shows that this piecewise linear dependence on the context parameters can be found by exploiting the fact that the optimum of a min-max multiparametric program is placed on the vertices of a parameterized polyhedron. As these parameterized vertices have specific validity domains, the control law is expressed as a piecewise linear function of the current system parameters. The resulting GPC law is formulated in terms of a look-up table with two-degree of freedom polynomials in the backward shift operator (also known as the RST formulation)

S0ingle-input/single-output generalised predictive control (GPC) has been applied successfully to muscle-relaxant anaesthesia in both simulations and clinical trials. This work was extended later to the multivariable case involving simultaneous control of muscle relaxation through EMG measurements, and unconciousness using blood-pressure monitoring in a series of simulation studies. To achieve even higher degrees of automation, a fault detection isolation and accommodation layer has been superimposed on top of the multivariable GPC strategy. The performance of the overall supervisory control scheme has been evaluated in a series of simulation runs on a work station. The results are encouraging in the sense that the algorithm has proved very effective in detecting, diagnosing and eventually compensating for various simulated faults normally encountered in the operating theatre. The experiments used two versions of GPC: the multivariable GPC (using a P-canonical form for the process model) and GPC with feedforward, and were conducted in noise-free and noise-corrupted situations since operating theatres can be electrically dirty environments. The study forms the basis for an experiment protocol which will be followed in clinical trials.

The multivariable generalized predictive control (GPC) law is derived in a stochastic setting which closely follows the usual polynomial solution of the LQG problem. The assumptions which must be made to ensure a GPC type of control law emerges are clear from the analysis which provides insights into the relationship between LQG and GPC control. The form of the single degree-of-freedom control law obtained closely parallels the GPC solution but ensures closed loop stability. The solution of the multi-step cost-function problem is shown to include an LQG controller which must be implemented for the actual feedback system and a set of controllers which are needed to calculate the predicted future controls and outputs. This is of importance when constraints are to be applied based on the future predicted signals. The alternative method outlined, referred to as the vector-matrix solution, does not require this sequence of controllers to be found and it is very similar to the GPC form of solution. However, the LQG stability properties for the feedback loop are still retained.

Robust design of multiloop GPC and its application for a cement mixing plant

A robustness approach for prefiltering the GPC

Generalized predictive control (GPC) is nowadays, widely ,spread in control theory as well as in the industrial world. Generalized predictive control (GPC) belong to this family has been demonstrated as a powerful controlling process plants, In this work a polynomial approach of generalized predictive control is proposed whose aim to find optimal values for the tuning control and application of predictive control with polynomial form RST and application of induction motor and developed prediction optimal equation with cost function and resolved of the Diophantine equations this application is reserved in our work for the induction motor with example of a dynamic system and non linear multivariable with load torque.

In the servo system, the autotuning controller based on a generalized predictive control (GPC) usually requires a specified reference instruction and the accurately linear system model. The former seriously limits the servo applications, while the latter's lack easily reduces the control performance. This article proposes a variable-structure proportional–integral (VSPI) velocity controller based on a GPC and an interconnected structure to address the above limitations. First, the VSPI controller is designed to track the arbitrary-order instruction without a static difference. Second, based on the Taylor series and the higher order difference operators, an improved prediction model, whose prediction output only includes the velocity error, is constructed to liberate the GPC from the reference instruction. Then, the control parameters of the VSPI controller can be adjusted online by the rolling optimization. Furthermore, the interconnected structure using three modified sliding-mode observers (MSMOs) is designed to efficiently provide the system model for the GPC, where an adaptive gain and an equivalent derivative designed in each MSMO make it possible for the identification parameters to converge exponentially. Finally, the third MSMO is used to identify the load disturbance for promoting the interconnected structure and the disturbance compensation, which can ensure the identification accuracy and the linearization of the system model. Simulation and experimental results demonstrate the effectiveness of the proposed method.

In this paper, a new GPC (Generalized Predictive Control) algorithm which always guarantees the stability of a closed loop system is proposed. The feedback part of the presented predictive control is shown to be equivalent to that of RHTC (Receding Horizon Tracking Control) with a terminal state constraint. The stability of the presented control law is proved and the simplest form of the stabilizing GPC is presented, which can be widely applied to various areas. The proposed control is different from Clarke's GPC in the sense that the latter guarantees stability for an indefinitely large horizon, while the former guarantees the stability for any horizon greater than the system order.

A model predictive control approach with relevant identification in dynamic PLS framework

The paper discusses state-space generalized predictive control and the preview control algorithms. The optimization procedure used in the derivation of predictive control algorithms is considered. The performance index associated with the generalized predictive controller (GPC) is examined and compared with the linear quadratic (LQ) optimal control formulation used in preview control. A new performance index and consequently a new algorithm is proposed dynamic performance predictive controller (DPPC) that combines the features of both GPC and preview controller. This algorithm minimizes the performance index through a dynamic optimization. A simple example illustrates the features of the three algorithms and prompts a discussion on what is actually minimized in predictive control. The DPPC algorithm, derived in this paper, provides for a minimum of the predictive performance index. The differences and similarities between the preview control and the predictive control have been discussed and optimization approach of predictive control has been explained.