Diego A. Muñoz

Robust control design of a class of nonlinear input- and state-constrained systems

Abstract This work focuses on control design for input-output feedback linearizable nonlinear systems with bounded inputs and state constraints in the presence of uncertainty. Controllers based on Lyapunov’s direct method have been synthesized before for this class of nonlinear systems to enforce asymptotic stability in the presence of bounded inputs. However, none of these controllers accounts explicitly for state constraints. In order to address this task, we propose an optimization-based design method for which two properties will be guaranteed simultaneously despite parametric uncertainty, namely, closed-loop stability with bounded inputs and feasibility of the transient in the presence of state constraints.

Integrated process and control design by the normal vector approach: Application to the Tennessee-Eastman process

Abstract This paper presents a large-scale application of the normal vector approach to demonstrate that the complexity of robust dynamic optimization with application to the integration of process and control design can be treated successfully for complex nonlinear systems. The case study further demonstrates that our approach can deal with a multi-dimensional uncertainty space. The normal vector approach is able to automatically identify the worst-case scenarios and find a solution that is optimal with respect to the cost function and robust with respect to path constraints on inputs and states in the presence of parameterized disturbances. The tedious analysis of a large number of different disturbance realizations is not required.

Input-constrained closed-loop systems with grazing bifurcations in optimal robust design

In this work, the normal vector method for robust design is considered to account for actuator saturation effects when unknown time-varying disturbances are present, and desired dynamic properties have to be guaranteed. The normal vector method ensures that desired dynamic properties hold despite uncertain parameters by maintaining a minimal distance between the operating point and so-called critical manifolds where the process behavior changes qualitatively. In this paper input saturation is considered for the first time in the normal vector framework. In order to solve the resulting optimization problem, first and second order derivatives of the flow of a dynamical system has to be computed efficiently. For this purpose, a new platform for source-level manipulation of mathematical models, currently under development at RWTH Aachen University, is proposed to solve the technical difficulties arising when the event of actuator saturation takes place.

Snowball effect detection and control proposal for its correction

Snowball effect is a positive mass and energy feedback acting on a process. This effect complicates process control by activating variables interactions and nonlinear phenomena inherent to the process. Traditional process control structures are strongly affected by snowball, which means that to reject relatively small perturbations large changes in the manipulated variables are required. In several cases, operative limits are surpassed and one or more variables cause a process fault. Present work proposes a control structure that can operate more efficiently in process with a tendency to develop snowball effect. The control system has a collaborative control structure, which uses a hierarchical structure with two levels of action in accordance with a dynamic hierarchy. Original PID controllers of traditional control structure are left in plant-floor level but a collaborative level is put over it, acting as an optimizer of process actions. The control proposal is illustrated using a typical buffer tank-separation process, commonly found in process industries.

Hierarchical Economic NMPC for a Class of Hybrid Systems Using Neighboring-Extremal Updates

Abstract A two-layer control algorithm is developed for a class of hybrid (discrete-continuous dynamic) systems comprising important applications such as the economically optimal operation of recipe-driven batch or continuous processes. On the upper layer, the economic optimal control problem is solved rigorously by a slow controller at a low sampling rate, whereas a fast neighboring-extremal controller updates rather than tracks the optimal trajectories to account for disturbances. Consequently, the process is steered to its operational bounds, while the optimal switching times under disturbances can be determined such that the economic potential of the process is fully exploited anytime.