Unfalsified Adaptive Control (UAC) is a recently proposed robust adaptive Control strategy. In this paper, the UAC principles and algorithms are reviewed and Multi-Model UAC is followed as an intermediate between UAC and multiple Model Control. Different approaches in UAC and MMUAC are studied. Also, Multi-Model Unfalsified Generalized predictive Control (MMUGPC) is proposed, which is a new Control design strategy in the UAC framework. For an uncertain system, by utilizing several generalized predictive Controllers and discrete switching between them with unfalsified Control, a new structure is proposed and appropriate equations are derived. Simulation results show the effectiveness of proposed Multi-Model Unfalsified Generalized predictive Control.

Model predictive Controller is widely used in industrial plants. Uncertainty is one of the critical issues in real systems. In this paper, the direct adaptive Simplified Model predictive Control (SMPC) is proposed for unknown or time varying plants with uncertainties. By estimating the plant step response in each sample, the Controller is designed and the Controller coefficients are directly calculated. The proposed method is validated via simulations for both slow and fast time varying systems. Simulation results indicate the Controller ability for tracking references in the presence of plant’s time varying parameters. In addition, an analytical tuning method for adjusting prediction horizon is proposed based on optimization of the objective function. It leads to a simple formula including the Model parameters, and an indirect adaptive Controller can be designed based on the analytical formula. Simulation results indicate a better performance for the tuned Controller. Finally, experimental tests are performed to show the effectiveness of the proposed methodologies.

In this paper autonomous modification of satellite attitude in its orbit is implemented for low earth orbit satellites using Model predictive Control. MPC minimizes force which applying on the thrusters subject to hard constraints on the Control input and output of the system. Here Clohessy-Wilshire equations are utilized by consideration of main disturbance of low orbits such as Atmospheric Drag and Non-Spherical earth disturbance for the design of MPC. The result of simulation proves that this MPC can amend the difference between the linear and non-linear mathematical Models of the satellite. In addition, not only orbital disturbances are compensated but also the satellite maintained in orbit with high accuracy. For performance evaluation of this MPC method, A Linear Quadratic Regulator is realized for Orbit's autonomous Control; the result of these two methods demonstrated that fuel consummation and Control effort in MPC manner is considerably less than LQR method.

Flight simulators as an integral component of today aviation industry, play an important role in training the pilots and development of the new equipment. Optimal motion cueing beside the positive characteristics of easy computation and implementation, due to limited performance in keeping the motion system within the workspace in complex maneuvers, is faced with serious obstacles. predictive Control method featured with inherent capabilities of dealing with constraints on inputs and state variables, while maintaining the high quality of the output, is faced with progressive development. The task of Model predictive Control is solving the optimal problem over the Control horizon to accommodate the feasible movement of the flight simulator by decreasing as could as the difference of the perception of motion between the pilots in real vehicle and the simulator. This approach is based on minimizing the quadratic cost function incorporating the sensation of motion, the motion system configuration related state variables as well as input Control signal. Although in this method, the design of washout filters are not needed. In this article, the systematic design of motion cueing algorithm based on Model predictive Control is described and its performance in comparison with optimal washout filter cueing method is illustrated. The proposed motion cueing method posing with much limited and smoother movements in surge-pitch maneuver tends to efficiently maintaining the motion system with in its workspace while preserving the same sense of motion. This results in increasing the capabilities of the motion system to be employed in much complex maneuvers.

In this article closed-loop behavior of non-linear unstable systems using Model predictive Control has been investigated. Closed-loop stability in presence of input constraint is discussed and related theorems have been stated. Two cases are considered. First it is assumed that all states are available and thereafter stability based on application of observer has been investigated. Since closed-loop performance is affected by the observer rate of convergence, in the second case, a high gain observer has been used and effect of observer gain on the system performance has been demonstrated.

Typical production objectives in distillation process require the delivery of products whose compositions meet certain specifications. The distillation Control system, therefore, must hold product compositions as near the set points as possible in faces of upset. In this project, inferential Model predictive Control, that utilizes an artificial neural network estimator and Model predictive Controller, is developed for an industrial multicomponent distillation column. First, composition Control by direct measuring composition is used. This method because of large sampling delay has a poor performance. The selection of the temperature measurement points is done for indirect Control of the column. The use of temperature loop leads to an offset in the composition, due to the fact that the temperature set-point must be changed when feed disturbances occurred. An artificial neural network estimator is designed to estimate the product compositions from tray temperature measurements. A Model predictive Controller is used to Control column composition based on composition estimates. The performance of the developed inferential Model predictive Control system is tested for set-point tracking and load rejection.

Drum boilers consume is one of the most important equipment of the thermal power plants in many countries and are widely used in industrial process applications. A boiler-turbine unit is a typical multivariable industrial Control system in power plant. The boiler is so manufactured that the drum level must be in the specified range. Output constraints are an important challenge in boiler drum systems to have safety and efficient operation. In this paper, the problem of state space Model predictive Control for a constrained nonlinear boiler drum systems is investigated. predictive Control is a highly efficient and strong method since its Control performance has been reported to be best among other conventional techniques to Control the multivariable dynamical plants with various inputs and outputs constraints. Simulation results demonstrate the satisfactory performance and effectiveness of this method and compatibility with practical implementation in boiler.

Fast and accurate transient response is the main requirement in electric machine position Control. Conventional cascade Control structure has sluggish response due to the limitation of inner Control loop bandwidth. In this paper, in order to decrease the Permanent Magnet Synchronous Motor (PMSM) transient response time it can be used reference Model using feed-forward signals. In this structure, feed-forward signals generated by simplified Model of permanent magnet synchronous motor. In this paper, feed-forward signals generated are emplyed in Model predictive Control; which are combined with conventional cascade Control structure. Using this approach, a fast transient response and satisfactory tracking ability will be guaranteed. The proposed method is compared with the Model reference method and conventional cascade structure. Simulation results showed a good performance of proposed method related to both methods. Verification of simulation results were carried out by experimental results.

Nowadays Autonomous Underwater Vehicles (AUVs) are an unavoidable part of marine industries. One of the most important parts of any autonomous vehicle is the Control issue to achieve the desired performance. This paper is concerned with speed Control of an AUV Model respecting the state and Control constraints. According to the Newton-Euler method, the 6 DOF kinematic and dynamic Models of the AUV are established. A well-defined performance index and constrained finite horizon optimization program in the form of Model predictive Control (MPC) strategy is proposed to regulate the horizontal speed of AUV to its desired value while the constraints on the states like depth and Control signals are considered in finite time horizon optimization program to be satisfied. The main problem for such a situation is the interaction between speed Control and depth deviation then quadratic programing technique managed responses to avoid state and Control signal constraints. Simulation results show a reliable performance of proposed MPC strategy to Control the horizontal speed of AUV while all the constraints on state, Control signal and also the variation of the Control signal are satisfied.

Surge is one of the two destructive factors in compressors. surge is the stream instability phenomenon in compressor that imposes severe damages to the compressors. Nowadays, suppressing surge phenomenon is one of the most important issues in oil and gas industries, especially when flow reduction or gas reflux is considered. According to Moore-Greitzer compressor Model, this paper designs an active Controller for surge Control in constant speed centrifugal compressors. As such, the applied operator considered for surge Control is Close Couple Valve (CCV) and it is designed to stabilize a centrifugal compressor system with disturbaces using nonlinear predictive Controller. The proposed Controller, without any information about the amount of Throttle Valve variations, could Control the surge instability and reduce the distance between compressor operation point and the surge line. Finally, the compressor system with Controller is simulated and the obtained results will show the efficiency of the designed nonlinear predictive Controller.