A Dual-Mode Model Predictive Control Algorithm Trajectory Tracking in Discrete-Time Nonlinear Dynamic Systems

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Asad A. Ul Haq ◽  
Michael E. Cholette ◽  
Dragan Djurdjanovic
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Discrete Time ◽  
Dynamic Systems ◽  
Trajectory Tracking ◽  
Closed Loop ◽  
Linear Control ◽  
Nonlinear Dynamic Systems ◽  
Closed Loop System ◽  
Dual Mode

In this paper, a dual-mode model predictive/linear control method is presented, which extends the concept of dual-mode model predictive control (MPC) to trajectory tracking control of nonlinear dynamic systems described by discrete-time state-space models. The dual-mode controller comprises of a time-varying linear control law, implemented when the states lie within a sufficiently small neighborhood of the reference trajectory, and a model predictive control strategy driving the system toward that neighborhood. The boundary of this neighborhood is characterized so as to ensure stability of the closed-loop system and terminate the optimization procedure in a finite number of iterations, without jeopardizing the stability of the closed-loop system. The developed controller is applied to the central air handling unit (AHU) of a two-zone variable air volume (VAV) heating, ventilation, and air conditioning (HVAC) system.

Speed Model Predictive Control Based on the Track Error Rate

2013 ◽  
Vol 336-338 ◽  
pp. 839-842
Author(s):  
Jin Huang ◽  
Cheng Zhi Yang ◽  
Ji Feng Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Tracking Error ◽  
Matlab Simulation ◽  
Closed Loop System ◽  
Index Function ◽  
Dynamical Characteristics ◽  
Simulation Results ◽  
Controlled Object

In order to make the controlled object have better dynamical characteristics, through introducing the differential item of error into optimal performance index function of tracking error, an improved algorithm of model predictive control is discussed in this paper. The theoretical analysis and Matlab simulation results show that it has better controlled quality and stronger robustness for closed-loop system.

scholarly journals Application of Model Predictive Control to Renewable Energy Development via Reactive Distillation Process

2016 ◽  
Vol 27 ◽  
pp. 95-110 ◽  
Author(s):  
Saidat Olanipekun Giwa ◽  
Abel Adekanmi Adeyi ◽  
Abdulwahab Giwa
Keyword(s):  
Renewable Energy ◽  
Control System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Reactive Distillation ◽  
Regulatory Control ◽  
Distillation Process ◽  
Closed Loop System ◽  
Tuning Parameters

Reactive distillation is a process that combines chemical reaction and separation in a single piece of equipment (distillation column). The process has a lot of benefits especially for those reactions occurring at conditions suitable for the distillation of the process components, and these result in significant economic advantages. However, owing to the complexities resulting from the integration of reaction and separation, its control is still a challenge to process engineers because it requires a control method that is robust enough to handle its complexities. Therefore, in this work, model predictive control (MPC) has been applied to a reactive distillation process used for developing a renewable energy known as biodiesel. The control algorithm of the MPC was formulated with the aid of MPC toolbox of MATLAB/Simulink in which the closed-loop models of the process were developed and simulated. The analysis of the results obtained from the simulations carried out for the optimization of the tuning parameters revealed that, among the tuning parameters considered, integral absolute error of the control system was less affected by the control horizon because its p-value was greater than 0.05 based on 95% confidence level. Furthermore, the simulation of the closed-loop system of the process using model predictive control tuned with control horizon of 11, prediction horizon of 18, weight on manipulated variable rate of 0.05 and weight on output variable of 2.17, which were the optimum parameters obtained using Excel Solver, showed that the system was well handled by the controller under servo control because it was able to get settled at desired mole fractions within 60 min. However, the settling time recorded in the case of regulatory control system of the process with the same controller was found not to be encouraging. Therefore, it is recommended that further work should be carried out on this subject matter in an attempt to obtain tuning parameters that will make the settling time of the closed-loop system of the process under regulatory control simulation very reasonable.

Ellipsoidal Lyapunov-based hybrid model predictive control for mixed logical dynamical systems with a recursive feasibility guarantee

2018 ◽  
Vol 41 (9) ◽  
pp. 2475-2487
Author(s):  
Alireza Olama ◽  
Mokhtar Shasadeghi ◽  
Amin Ramezani ◽  
Mostafa Khorramizadeh ◽  
Paulo R C Mendes
Keyword(s):  
Dynamical Systems ◽  
Model Predictive Control ◽  
Hybrid Model ◽  
Predictive Control ◽  
Robust Stability ◽  
Optimization Problem ◽  
Closed Loop ◽  
Loop System ◽  
Closed Loop System ◽  
Mixed Logical Dynamical

This paper proposes an ellipsoidal hybrid model predictive control approach to solve the robust stability problem of uncertain hybrid dynamical systems modelled by the mixed logical dynamical framework. In this approach, the traditional terminal equality constraint is replaced by an ellipsoid that results in a maximal positive invariant set for the closed-loop system. Then, a Lyapunov decreasing condition along with the robustness criterion is introduced to the optimization problem to achieve the robust stability of the closed-loop system. As the main advantages, the ellipsoidal terminal set proposed in this paper attains a larger domain of attraction along with the recursive feasibility guarantee. Moreover, the stability and robustness constraints are achieved by a lower prediction horizon, which leads to a smaller dimension optimization problem. In addition, to reduce the computational complexity of the corresponding optimization problem, a suboptimal version of the proposed algorithm is introduced. Finally, numerical and car suspension system examples show the capabilities of the proposed method.

scholarly journals A Robust Fault-Tolerant Predictive Control for Discrete-Time Linear Systems Subject to Sensor and Actuator Faults

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2307
Author(s):  
Sofiane Bououden ◽  
Ilyes Boulkaibet ◽  
Mohammed Chadli ◽  
Abdelaziz Abboudi
Keyword(s):  
Model Predictive Control ◽  
Linear Systems ◽  
Predictive Control ◽  
Robust Stability ◽  
Discrete Time ◽  
Fault Tolerant ◽  
Linear Matrix ◽  
Input Constraints ◽  
Actuator Faults ◽  
Time Linear

In this paper, a robust fault-tolerant model predictive control (RFTPC) approach is proposed for discrete-time linear systems subject to sensor and actuator faults, disturbances, and input constraints. In this approach, a virtual observer is first considered to improve the observation accuracy as well as reduce fault effects on the system. Then, a real observer is established based on the proposed virtual observer, since the performance of virtual observers is limited due to the presence of unmeasurable information in the system. Based on the estimated information obtained by the observers, a robust fault-tolerant model predictive control is synthesized and used to control discrete-time systems subject to sensor and actuator faults, disturbances, and input constraints. Additionally, an optimized cost function is employed in the RFTPC design to guarantee robust stability as well as the rejection of bounded disturbances for the discrete-time system with sensor and actuator faults. Furthermore, a linear matrix inequality (LMI) approach is used to propose sufficient stability conditions that ensure and guarantee the robust stability of the whole closed-loop system composed of the states and the estimation error of the system dynamics. As a result, the entire control problem is formulated as an LMI problem, and the gains of both observer and robust fault-tolerant model predictive controller are obtained by solving the linear matrix inequalities (LMIs). Finally, the efficiency of the proposed RFTPC controller is tested by simulating a numerical example where the simulation results demonstrate the applicability of the proposed method in dealing with linear systems subject to faults in both actuators and sensors.

Compensatory model predictive control for post-impact trajectory tracking via active front steering and differential torque vectoring

2020 ◽  
pp. 095440702097908
Author(s):  
Mingcong Cao ◽  
Chuan Hu ◽  
Rongrong Wang ◽  
Jinxiang Wang ◽  
Nan Chen
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Tracking Error ◽  
Collision Probability ◽  
Critical Conditions ◽  
Physical Analysis ◽  
Compensatory Model ◽  
Torque Vectoring ◽  
Post Impact

This paper investigates the trajectory tracking control of independently actuated autonomous vehicles after the first impact, aiming to mitigate the secondary collision probability. An integrated predictive control strategy is proposed to mitigate the deteriorated state propagation and facilitate safety objective achievement in critical conditions after a collision. Three highlights can be concluded in this work: (1) A compensatory model predictive control (MPC) strategy is proposed to incorporate a feedforward-feedback compensation control (FCC) method. Based on the definite physical analysis, it is verified that adequate reverse steering and differential torque vectoring render more potentials and flexibility for vehicle post-impact control; (2) With compensatory portions, the deteriorated states after a collision are far beyond the traditional stability envelope. Hence it can be further manipulated in MPC by constraint transformation, rather than introducing soft constraints and decreasing the control efforts on tracking error; (3) Considering time-varying saturation on input, input rate, and slip ratio, the proposed FCC-MPC controller is developed to improve faster deviation attenuation both in lateral and yaw motions. Finally two high-fidelity simulation cases implemented on CarSim-Simulink conjoint platform have demonstrated that the proposed controller has the advanced capabilities of vehicle safety improvement and better control performance achievement after severe impacts.

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