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Research Article |

Model Predictive Control for Trajectory Tracking in Multi Input Multi Output Systems

Author(s): Dr. Anil M. Kasture1*, Shrishail Swami2, Dr. Amar Jain3, Dr. Anil Deshmane4, Dr. Sanjeev Bhosale5, and Anand Gyanbote6

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 14, Issue 1

Publisher : FOREX Publication

Published : 30 March 2026

e-ISSN : 2347-470X

Page(s) : 160-166

DOI: https://doi.org/10.37391/IJEER.140117

Abstract

Trajectory tracking in Multi Input Multi-Output (MIMO) systems remains a fundamental challenge in advanced control engineering due to strong coupling effects, nonlinear dynamics, and stringent operational constraints. Traditional controllers like PID and LQR frequently fail to ensure resilience, optimality, and constraint compliance. In this paper, we look at the use of Model Predictive Control (MPC) as a trajectory tracking framework for MIMO systems. The suggested methodology, which incorporates system modelling, horizon-based optimization, and constraint handling, is assessed using simulated benchmarks versus PID and LQR controllers The findings show that despite closely adhering to input and state constraints, MPC achieves higher tracking accuracy, up to 52% lower RMSE, and shorter settling times. Strong analyses show that MPC outperforms traditional methods in the face of model uncertainty and external perturbations. Additionally, solver benchmarking demonstrates that modern optimization engines provide real-time viability on embedded platforms, making MPC feasible for implementations with limited resources. This paper offers a comprehensive framework for MPC design, implementation, and evaluation in MIMO trajectory tracking. The results confirm MPC as the state-of-the-art control method, offering a scalable, robust, and constraint-aware solution for a variety of applications, such as smart energy systems, robots, aircraft, and process industries.

Keywords: Adaptive and Learning Based Control, Constraint Handling, Nonlinear Model Predictive Control (NMPC), Multi Input Multi Output (MIMO) Systems, Optimization Solvers, Robust Control, Real Time Control, State Estimation, Trajectory Tracking.




Dr. Anil M. Kasture, Automation Engineer, Iwork Technologies Pvt. Ltd., Pune, India; Email: anilkasture01@gmail.com

Shrishail Swami, Deparment of E&TC Engineering, Walchand Institute of Technology, Solapur, India; Email: spswami@witsolapur.org

Dr. Amar Jain, Department of Civil Engineering, Symbiosis Institute of Technology Pune, India; Email: amar.jain@sitpune.edu.in

Dr. Anil Deshmane, Department of E&TC Engineering, Bharat-Ratna Indira Gandhi College of Engineering, Solapur, India; Email: anildeshmane68@gmail.com

Dr. Sanjeev Bhosale, Department of E&TC Engineering, Bharat-Ratna Indira Gandhi College of Engineering, Solapur, India; Email: sdbhosale1968@gmail.com

Anand Gyanbote, Department of Computer science and Engineering, Bharat-Ratna Indira Gandhi College of Engineering, Solapur, India; Email: anandgyanbote@gmail.com

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Dr. Anil M. Kasture, Shrishail Swami, Dr. Amar Jain, Dr. Anil Deshmane, Dr. Sanjeev Bhosale, and Anand Gyanbote (2026), Model Predictive Control for Trajectory Tracking in Multi Input Multi Output Systems. IJEER 14(1), 160-166. DOI: 10.37391/IJEER.140117.