This paper presents a method for optimal control of hybrid systems. An inequality of Bellman type is considered and every solution to this inequality gives a lower bound on the optimal value function. A discretization of this "hybrid Bellman inequality" leads to a convex optimization problem in terms of finitedimensional linear programming. From the solution of the discretized problem, a value function that preserves the lower bound property can be constructed. An approximation of the optimal feedback control law is given and tried on some examples.
A classical linear programming approach to optimization of flow or transportation in a discrete graph is extended to hybrid systems. The problem is finite dimensional if the state space is discrete and finite, but becomes infinite dimensional for a continuous or hybrid state space. It is shown how strict lower bounds on the optimal loss function can be computed by gridding the continuous state space and restricting the linear program to a finite-dimensional subspace. Upper bounds can be obtained by evaluation of the corresponding control laws.
This paper reports the development of a Matlab toolbox for computational analysis of piecewise linear systems. The analysis is based on piecewise quadratic Lyapunov functions, which are computed via convex optimization. In this way, exponential stability and system performance can be assessed. The toolbox also supports efficient simulation of systems with discontinuous dynamics and sliding modes. A set of intuitive commands for describing piecewise linear systems is included, making the analysis routines easily accessible also for the inexperienced user.
Abstroct A theorem on duality between cost functions and density functions in optimal control is derived using the Hahn-Banach theorem. The result puts focus on convexity aspects in control synthesis and the recent theory of almost global stability. In particular, it gives a new proof that existence of a density function is both necessary and sufficient for almost global stability in a nonlinear system.
In a previous paper, we showed how classical ideas for dynamic programming in discrete networks can be adapted to hybrid systems. The approach is based on discretization of the continuous Bellman inequality which gives a lower bound on the optimal cost. The lower bound is maximized by linear programming to get an approximation of the optimal solution.In this paper, we apply ideas from infinitedimensional convex analysis to get an inequality which is dual to the well known Bellman inequality. The result is a linear programming problem that gives an estimate of the approximation error in the previous numerical approaches.
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