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Receding horizon control (RHC) or model predictive control (MPC) solves online a finite horizon open-loop optimal control problem repeatedly in an infinite horizon context and provides a suboptimal control solution. It has been widely used in industry. For continuous-time (CT) systems, two categories of RHC have been investigated in literature, namely instantaneous RHC and sampled-data RHC. This paper...
We present an approach for compensating input delay of arbitrary length in nonlinear control systems. This approach, which due to the infinite dimensionality of the actuator dynamics and due to the nonlinear character of the plant results in a nonlinear feedback operator, is essentially a nonlinear version of the Smith Predictor and its various predictor-based modifications for linear plants. Global...
In this paper we use bilevel programming to find the maximum difference between a reference controller and a low-complexity controller in terms of the infinity-norm difference of their control laws. A nominal MPC for linear systems with constraints, and a robust MPC for linear systems with bounded additive noise are considered as reference controllers. For possible low-complexity controllers we discuss...
In semiconductor manufacturing processes, mixed-products are usually fabricated on the same set of process tool with different recipes. Run-to-run controllers which based on the exponential weighted moving average (EWMA) statistic are probably the most frequently used in industry for the quality control of certain semiconductor manufacturing process steps. However, for mixed-product drifting process,...
A Smith Predictor-like design for compensation of arbitrarily long input delays is available for general, controllable, possibly unstable LTI finite-dimensional systems. Such a design has not been proposed previously for problems where the plant is a PDE. We present a design and stability analysis for a prototype problem, where the plant is a reaction-diffusion (parabolic) PDE, with boundary control...
This paper proposes a novel synthesis approach to dynamic output feedback robust model predictive control for systems with both polytopic description and bounded disturbance. The notion of quadratic boundedness is utilized to characterize the stability properties of the augmented closed-loop system. An error signal is defined, which is a linear combination of the true state, controller state and output...
This paper is concerned with the problem of receding horizon control of discrete-time systems subject to possibly unbounded random noise inputs, while satisfying hard bounds on the control inputs. We use a nonlinear feedback policy with respect to noise measurements and show that the resulting mathematical program has a tractable convex solution. Moreover, under the assumption that the zero-input...
A recent paper proposed an MPC methodology which achieved a considerable reduction in the online optimization by transferring some of the computational load to calculations that can be performed offline. The approach was based on an augmented autonomous state space formulations of the prediction dynamics and gained significantly in efficiency by imposing a terminal constraint at current time. The...
Control performance is an important issue for model predictive control (MPC). For the aggregation based MPC controller, where the aggregation strategy is adopted to the control input sequence of MPC, the online computational burden is generally reduced at the cost of control performance. How to guarantee the control performance with the reduced online computational burden is a critical issue for applying...
Model predictive control (MPC) is an on-line control technique originally developed for slow processes which makes an assessment between input effort and output error while respecting constraints on inputs and outputs. Due to improved computing power and algorithms, MPC is nowadays also applied to mechatronic systems. For these systems, achieving minimal settling time is the main concern, while the...
In this work, we focus on the problem of stabilization of two constrained linear systems coupled through the inputs by two different agents which communicate in order to take a decision assuming that each agent only has partial information of the model and the state of the system. We extend previous results on distributed model predictive control and provide sufficient conditions that guarantee practical...
Smith Predictor-like designs for compensation of arbitrarily long input delays are commonly available only for finite-dimensional systems. Only very few examples exist where such compensation has been achieved for PDE systems, including our recent result for a parabolic (reaction-diffusion) PDE. In this paper we address a more challenging wave PDE problem, where the difficulty is amplified by allowing...
This paper considers the stabilization problem of linear systems with n + 1 poles and a time delay tau. First, the conditions for the existence of a stabilizing control by static output feedback are done. Subsequently, the conditions for the existence of a predictor scheme are established. Finally, the application of the results are illustrated with three academic examples.
Nonlinear Model predictive control (NMPC) suffers from the problems of closed loop instability and computational complexity, which greatly limit the applications of NMPC in real plants involving fast time-varying dynamics. During previous work, the authors have supposed a new real-time NMPC algorithm based on the concept of generalized pointwise min-norm (GPMN) scheme. And in this paper, the new real-time...
In this paper, we present a delay-dependent robust model predictive control (MPC) algorithm for a class of discrete-time linear state-delayed systems subjected to polytopic-type uncertainties and input constraints. The state-feedback MPC law is calculated by minimizing an upper bound of the worst-case quadratic cost function over an infinite time horizon at each sampling instant. In contrast to existing...
Electromagnetically driven mechanical systems are characterized by fast nonlinear dynamics that are subject to physical and control constraints, which makes controller design a challenging problem. This paper presents a novel model predictive control (MPC) scheme that can handle both the performance/physical constraints and the strict limits on computational complexity required in control of general...
Stabilization of unstable systems with actuator delay of substantial length and of completely unknown value is an important problem that has never been attempted. We present a Lyapunov-based adaptive control design that achieves global stability, without a requirement that the delay estimate be near the true delay value. We solve the problem by employing a framework where the actuator delay is represented...
In this paper we introduce a parameterized nonlinear MPC algorithm. Comparing with the classical method, it provides more flexibility in controller design. We discuss its applications in enlarging the attractive region and in multi-objective optimization design. These ideas are demonstrated by a two-mass-spring system example.
Electromagnetically driven mechanical systems are characterized by fast non-linear dynamics that are subject to physical and control constraints. This paper describes a Model Predictive Controller (MPC) for a general ElectroMagnetic (EM) actuator that satisfies both the performance constraints and the strict requirements on the computation time. Novel aspects of the MPC design are a one-step-ahead...
As a new type of output feedback control for a stochastic discrete-time state-space system, a finite memory control (FMC) is proposed and its properties are investigated. Instead of using an internal state involved with old data, the FMC is required to have finite memory with respect to inputs and outputs together with the future reference signals and minimize a conditional receding horizon quadratic...
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