Predictive controllers play an important role in today's industry because of their capability
of verifying optimum control signals for nonlinear systems in a real-time fashion.
Due to their mathematical properties, such controllers are best suited for control problems
with constraints. Also, these interesting controllers can be equipped with different types
of optimization and learning modules. The main goal of this thesis is to explore the potential of predictive controllers for a challenging automotive problem, known as active vehicle suspension control.
In this context, it is intended to explore both modeling and optimization modules
using different statistical methodologies ranging from statistical learning to random process
control. Among the variants of predictive controllers, learning-based model predictive
controller (LBMPC) is becoming more and more interesting to the researchers of control
society due to its structural flexibility and optimal performance. The current investigation
will contribute to the improvement of LBMPC by adopting different statistical learning
strategies and forecasting methods to improve the efficiency and robustness of learning
performed in LBMPC. Also, advanced probabilistic tools such as reinforcement learning,
absorbing state stochastic process, graphical modelling, and bootstrapping are used to
quantify different sources of uncertainty which can affect the performance of the LBMPC
when it is used for vehicle suspension control. Moreover, a comparative study is conducted
using gradient-based as well as deterministic and stochastic direct search optimization
algorithms for calculating the optimal control commands.
By combining the well-established control and statistical theories, a novel variant of
LBMPC is developed which not only affords stability and robustness, but also surpasses
a wide range of conventional controllers for the vehicle suspension control problem. The
findings of the current investigation can be interesting to the researchers of automotive
industry (in particular those interested in automotive control), as several open issues regarding the potential of statistical tools for improving the performance of controllers for
vehicle suspension problem are addressed
