Genetic-Algorithm-Based Fixed-Structure Robust H_∞Loop-Shaping Control of a Pneumatic Servosystem

Abstract

The robust controller designed by conventional H∞optimal control is complicated, high-order, and difficult to implement practically. In industrial applications, structures such as lead-lag compensators and PID are widely used because their structure is simple, tuning parameters are fewer, and they are lower-order. Their disadvantages are that control parameters are difficult to tune for good performance and they lack robustness. To solve these problems, we propose an algorithma genetic-algorithm-based fixed-structure robust H∞loop-shaping controlfor designing the robust controller. Conventional H∞loop shaping is a sensible procedure for designing the robust controller. To obtain parameters in the proposed controller, we proposed a genetic algorithm to optimize specified-structure H∞loop shaping problem. The infinity norm of transfer function from disturbances to states is minimized via searching and evolutionary computation. The resulting optimal parameters stabilize the system and guarantee robust performance. We applied the evolutionary robust controller to a pneumatic servosystem. To compare performance, we studied three types of controller PID with a derivative first-order filter controller, a PI controller, and an H∞loop-shaping controller. Results of experiments demonstrate the advantages of a simple structure and robustness against parameters changing. Simulations verify the effectiveness of the proposed technique.

Structure-Specified H∞ Loop Shaping Control for Balancing of Bicycle Robots: A Particle Swarm Optimization Approach

“…The structure-specified H ∞ loop shaping controller design problem can be defined as the problem of finding the parameters of all admissible controllers represented by equation (12) such that the H ∞ norm presented by equation (6) …”

“…It is a robust control technique that is suitable for the systems with unstructured uncertainties. This approach was firstly developed by McFarlane and Glover in 1992 [8], and has been used successfully in many practical applications [9][10][11][12]. However, in the conventional H ∞ loop shaping control design, the obtained controllers are normally high order ones, and it is difficult to implement in the reality.…”

Covariance matrix adaptation evolution strategy based design of fixed structure robust H∞ loop shaping controller

“…Additionally, model reduction methods are used to reduce the controller order which also increases the mathematical complexity of the controller design problem. Thus, a fixed structure controller with good robust stability and performance is preferred for the practical implementation point of view [12][13][14][18][19][20][21][22].…”

“…To overcome the limitations of LMI approaches, evolutionary computing (EC) techniques are very popular in solving the non-convex, non-smooth, multi-modal and badly scaled problems [17]. Many, EC techniques such as Genetic Algorithm (GA) [18,19], Particle Swarm Optimization (PSO) [20,21] and Heuristic Kalman Algorithm (HKA) [16], Hybrid PSO [22] are used for designing the fixed structure H ∞ loop shaping controller.…”

“…From the proposed formulation, the weights can be tuned only based on stability conditions and the desired performance can be obtained using the constraint. Also, in this disconnected surface problem, the performance requirement constraint helps to find the better stability margin than the existing work of [16,18].…”