Control Rate Constraints Research Articles

Sliding mode predictive control of a magnetic levitation system employing multi-parametric programming

This paper is concerned with the application of sliding mode predictive control (SMPC) on a laboratory-scale magnetic levitation system. The main contribution consists of the use of multi-parametric programming to reduce the computational workload required for real-time implementation. For comparison, a conventional sliding mode controller was also employed. The task consisted of tracking a reference signal in the presence of control rate constraints. In this scenario, the sliding mode controller was not able to maintain closed-loop stability, whereas the SMPC controller successfully performed the tracking task, with proper satisfaction of the constraints.

Data‐based two‐degree‐of‐freedom iterative control approach to constrained non‐linear systems

This study proposes a data-based model-free approach to reference trajectory tracking in two-degree-of-freedom non-linear control system (CS) structures. This model-free control approach tunes both the feedback controller parameters and the reference input sequence accounting for control saturation and control rate constraints. The controller is iteratively tuned in a non-linear framework that employs a gradient descent search approach. The model-free gradient estimates are obtained by a perturbation-based approach. The reference input tuning is carried out in a linear framework using an iterative learning control-based approach, and it also includes a model-free gradient search algorithm where the gradient estimates are obtained by a similar perturbation-based approach. The number of real-world experiments is significantly reduced by the use of simulated models identified as neural networks. A digitally simulated case study concerning the angular position control of a non-linear aerodynamic twin-rotor system shows that the author's approach can effectively improve the CS performance.

Real-Time Optimal Coherent Phantom Track Generation via the Virtual Motion Camouflage Approach

Coherent phantom track generation through controlling a group of electronic combat air vehicles is currently an area of great interest to the defense agency for the purpose of deceiving a radar network. However, generating an optimal or even feasible coherent phantom trajectory in real-time is challenging due to the high dimensionality of the problem and severe geometric, as well as state, control, and control rate constraints. In this paper, the bio-inspired virtual motion camouflage based methodology, augmented with the derived early termination condition, is investigated to solve this constrained collaborative trajectory planning problem in two approaches: centralized (one optimization loop) and decentralized (two optimization loops). Specifically, in the decentralized approach, the first loop finds feasible phantom tracks based on the early termination condition and the equality and inequality constraints of the phantom track. The second loop uses the virtual motion camouflage method to solve for the optimal electronic combat air vehicle trajectories based on the feasible phantom tracks obtained in the first loop. Necessary conditions are proposed for both approaches so that the initial and final velocities of the phantom and electronic combat air vehicles are coherent. It is shown that the decentralized approach can solve the problem much faster than the centralized one, and when the decentralized approach is applied, the computational cost remains roughly the same for the cases when the number of nodes and/or the number of electronic combat air vehicles increases. It is concluded that the virtual motion camouflage based decentralized approach has promising potential for usage in real-time implementation.

BACKLASH COMPENSATION USING RECEDING HORIZON CONTROL

We present a receding horizon control algorithm for compensation of backlash at the input of a stable linear system under control rate constraints. The problem is first posed as a receding horizon optimal control problem by modelling backlash as a piecewise affine system with a state space partition consisting of three regions. This optimal control problem involves solving, at each step, 3N quadratic programmes, where N is the optimisation horizon. As an alternative to solving the quadratic programmes, we propose a strategy based on a recently devised suboptimal receding horizon control algorithm which utilises a singular value decomposition of the Hessian of the quadratic programme. This alternative strategy leads, at the cost of some performance degradation, to much smaller computational load since a feasible rather than optimal solution has to be obtained at each step.

Tracking control of nonlinear systems with bounded controls and control rates

The problem of tracking control of a class of nonlinear systems subject to control and control-rate constraints is considered. A continuous-time predictive control approach is used to derive the nonlinear constrained control law. Asymptotically stable tracking is ensured in the absence of saturation. The control and control-rate constraints are guaranteed to be strictly satisfied by the control law. An example is given to demonstrate the effectiveness of the method

Tracking Control of Nonlinear Systems with Bounded Controls and Control Rates

The problem of tracking control of a class of nonlinear systems that are input-output feedback linearizable and subject to hard control and control-rate constraints is considered. A continuous-time predictive control approach is used to derive the nonlinear constrained control law. The control and control-rate constraints are guaranteed to be strictly satisfied by the control law. An example is given to demonstrate the effectiveness of the method.