Development of a GPC-based sliding mode controller

Boaventura‐Cunha

Lecture Notes in Electrical Engineering

et al. 2015

Abstract. This work presents a new approach to tune the parameters of the discontinuous component of the Sliding Mode Generalized Predictive Controller (SMGPC) subject to constraints. The strategy employs Particle Swarm Optimization (PSO) to minimize a second aggregated cost function. The continuous component is obtained by the standard procedure, by Sequential Quadratic Programming (SQP), thus yielding a dual optimization scheme. Simulations and performance indexes for a non minimum linear model result in a better performance, improving robustness and tracking accuracy.

Section: Introductionmentioning

confidence: 83%

Sliding Mode Generalized Predictive Control Based on Dual Optimization

Boaventura‐Cunha

Lecture Notes in Electrical Engineering

et al. 2015

“…Remark 2. Compared with (14), the auxiliary contractive constraint (19) is adopted in the optimization problem instead of the terminal constraint in (17). Unlike the dualmode strategy, this algorithm avoids the switching of the controller.…”

Section: Algorithm 1 Contractive Sm-mpc Algorithmmentioning

confidence: 99%

“…We call the control algorithm which combines MPC with SMC the sliding-mode MPC (SM-MPC). Parte et al [14] designed a generalized predictive control (GPC) method based on sliding mode controller. Xiao et al [15] addresed a similar approach, where the model algorithm control (MAC) is used.…”

Section: Introductionmentioning

confidence: 99%

A Contractive Sliding- mode MPC Algorithm for Nonlinear Discrete- time Systems

Zhao

Ding

2013

Int. J. Autom. Comput.

This paper investigates a sliding-mode model predictive control (MPC) algorithm with auxiliary contractive sliding vector constraint for constrained nonlinear discrete-time systems. By adding contractive constraint into the optimization problem in regular sliding-mode MPC algorithm, the value of the sliding vector is decreased to zero asymptotically, which means that the system state is driven into a vicinity of sliding surface with a certain width. Then, the system state moves along the sliding surface to the equilibrium point within the vicinity. By applying the proposed algorithm, the stability of the closed-loop system is guaranteed. A numerical example of a continuous stirred tank reactor (CSTR) system is given to verify the feasibility and effectiveness of the proposed method.

EPiC Series in Engineering

“…The well succeeded melting of SMPC motivated other works and applications [7,12,14,19,13]. Following some design steps of the SMPC presented in [13], this article keeps Quadratic…”

Section: Introductionmentioning

confidence: 86%

“…SMPC control law, ∆u SM P C (k ), is the combination of two additive parts: a continuous part ∆u c (k ) = ∆u(k ) developed like a GPC by the minimization of (9) using quadratic programming (QP), which is responsible for keeping the process variable on the reference value, and a discontinuous part ∆u d (k ) to be detailed further, responsible for guiding the system to the sliding surface. To calculate ∆u c (k ), (10) is substituted into (7) and, after putting it into matrix form, one has:Ŝ…”

Section: Prediction Of the Sliding Surfacementioning

confidence: 99%

A Sliding Mode-Based Predictive Strategy for Irrigation Canal Pools

Oliveira¹,

Pinho²,

Coelho³

et al.

This paper evaluates a robust Model Predictive Controller (MPC) based on Sliding Modes (SMPC) for the downstream level control in irrigation canal pools. Its features are compared with the conventional Generalized Predictive Controller (GPC), regarding set point tracking (water level) and output disturbances (offtake discharges). Simulation results suggest feasibility of applying SMPC for gate manipulation, with suitable command signals and robustness.