Data-driven H∞ control of constrained systems: An application to bilateral teleoperation system.

A novel, identification-free, data-driven (DD) H∞ control method is presented for discrete-time (DT) linear time-invariant (LTI) systems under physical limitations and norm-bounded disturbances. The presented approach does not demand information on system matrices or any measurements of disturbance affecting the system. The only information needed to develop a static state-feedback (SF) controller is the bounds on disturbances, states and control signals. It is assumed that only the disturbance input matrix and the performance matrices the user generally defines are known, and all others are entirely unknown. The proposed method relies on the closed-loop (CL) parametrization of the LTI system with control input and state measurements. The disturbances affecting the system states are handled as affine uncertainties, later represented as Linear Fractional Transformation (LFT). For obtaining a less conservative controller, a full block S-procedure method (FBSPM) is used, which takes advantage of relaxations such as convex hull relaxation or Pólya relaxation for the inner approximation of the disturbance set with arbitrary precision. Numerical illustrations and extensive case studies on a bilateral teleoperation system indicate that the proposed design method allows us to obtain very effective controllers which never exceed the bounds of the state and input variables and are capable of reference and force tracking.

Robust induced ℓ2-ℓ∞ optimal control of discrete-time systems having magnitude and rate-bounded actuators.

The design of robust state- and output-feedback control for uncertain discrete-time systems with physical magnitude and rate constraints on their actuator dynamics was addressed. Unlike the traditional methods such as anti-windup (AW) methods, nested ellipsoids, model predictive controllers (MPCs) and integral quadratic constraints(IQCs) formulated by sector bounded inequalities, this paper uses a transformation of the system dynamics to a form which considers control signal and its rate as controlled outputs and using discrete-time ℓ∞ induced (peak-to-peak) norm from disturbance inputs to these outputs. To cope with the magnitude and rate bound non-linearities together, the induced ℓ∞ norm from disturbance input to the outputs involving control signal and its rate is utilised. On the other hand, discrete-time(DT) induced ℓ2 norm from disturbance input to the main controlled output is used to mitigate the effects of disturbances. We can tackle this ambitious non-linear control problem in the domain of linear convex multi-objective optimal control problem, which can be solved by effective semi-definite optimisation methods by using the proposed transformation and handling the control constraints in terms of worst case peak-to-peak gain of the system. Extended Linear Matrix Inequalities (LMIs) and full block S-procedure based design conditions developed over Linear Fractional Representation(LFR) framework allow the user to obtain robust state- and output-feedback control solutions with reduced conservatism. For the first time, this paper introduces an extended LMI based robust output-feedback control design for magnitude and rate bounded (MRB) systems, using full block S-procedure. We demonstrate the performance of the proposed controller through several simulations over benchmark examples covering systems having multi-variable structures and uncertainties. Our study also involves comparison results with a recently introduced technique based on multi-stage AW technique. The simulation results show that the proposed method of this paper is much effective and less conservative compared to the recent AW method provided in the literature.

Fault-tolerant optimal pitch control of wind turbines using dynamic weighted parallel firefly algorithm.

With steadily increasing interest in utilizing wind turbine (WT) systems as primary electrical energy generators, fault-tolerance has been considered decisive to enhance their efficiency and reliability. In this work, an optimal fault-tolerant pitch control (FTPC) strategy is addressed to adjust the pitch angle of WT blades in the presence of sensor, actuator, and system faults. The proposed scheme incorporates a fractional-calculus based extended memory (EM) of pitch angles along with a fractional-order proportional-integral-derivative (FOPID) controller to enhance the performance of the WT. A dynamic weighted parallel firefly algorithm (DWPFA) is also proposed to tune the controller parameters. The efficiency of the proposed algorithm is evaluated on the test functions adopted from 2017 IEEE congress on evolutionary computation (CEC2017). The merits of the proposed fault-tolerant approach are tested on a 4.8-MW WT benchmark model and compared to conventional PI and optimal FOPID approaches. Corresponding comparative simulation results validate the effectiveness and fault-tolerant capability of the proposed control paradigm, where it is observed that the proposed control scheme tends to be more consistent in the power generated at a given wind speed.